diff --git "a/raw/ai_papers_data.csv" "b/raw/ai_papers_data.csv" new file mode 100644--- /dev/null +++ "b/raw/ai_papers_data.csv" @@ -0,0 +1,106893 @@ +text,target +" In these notes we formally describe the functionality of Calculating Valid +Domains from the BDD representing the solution space of valid configurations. +The formalization is largely based on the CLab configuration framework. +",Calculating Valid Domains for BDD-Based Interactive Configuration +" Motivation: Profile hidden Markov Models (pHMMs) are a popular and very +useful tool in the detection of the remote homologue protein families. +Unfortunately, their performance is not always satisfactory when proteins are +in the 'twilight zone'. We present HMMER-STRUCT, a model construction algorithm +and tool that tries to improve pHMM performance by using structural information +while training pHMMs. As a first step, HMMER-STRUCT constructs a set of pHMMs. +Each pHMM is constructed by weighting each residue in an aligned protein +according to a specific structural property of the residue. Properties used +were primary, secondary and tertiary structures, accessibility and packing. +HMMER-STRUCT then prioritizes the results by voting. Results: We used the SCOP +database to perform our experiments. Throughout, we apply leave-one-family-out +cross-validation over protein superfamilies. First, we used the MAMMOTH-mult +structural aligner to align the training set proteins. Then, we performed two +sets of experiments. In a first experiment, we compared structure weighted +models against standard pHMMs and against each other. In a second experiment, +we compared the voting model against individual pHMMs. We compare method +performance through ROC curves and through Precision/Recall curves, and assess +significance through the paired two tailed t-test. Our results show significant +performance improvements of all structurally weighted models over default +HMMER, and a significant improvement in sensitivity of the combined models over +both the original model and the structurally weighted models. +",A study of structural properties on profiles HMMs +" This paper proposes an approach to training rough set models using Bayesian +framework trained using Markov Chain Monte Carlo (MCMC) method. The prior +probabilities are constructed from the prior knowledge that good rough set +models have fewer rules. Markov Chain Monte Carlo sampling is conducted through +sampling in the rough set granule space and Metropolis algorithm is used as an +acceptance criteria. The proposed method is tested to estimate the risk of HIV +given demographic data. The results obtained shows that the proposed approach +is able to achieve an average accuracy of 58% with the accuracy varying up to +66%. In addition the Bayesian rough set give the probabilities of the estimated +HIV status as well as the linguistic rules describing how the demographic +parameters drive the risk of HIV. +",Bayesian approach to rough set +" Noise, corruptions and variations in face images can seriously hurt the +performance of face recognition systems. To make such systems robust, +multiclass neuralnetwork classifiers capable of learning from noisy data have +been suggested. However on large face data sets such systems cannot provide the +robustness at a high level. In this paper we explore a pairwise neural-network +system as an alternative approach to improving the robustness of face +recognition. In our experiments this approach is shown to outperform the +multiclass neural-network system in terms of the predictive accuracy on the +face images corrupted by noise. +","Comparing Robustness of Pairwise and Multiclass Neural-Network Systems + for Face Recognition" +" Evolutionary Learning proceeds by evolving a population of classifiers, from +which it generally returns (with some notable exceptions) the single +best-of-run classifier as final result. In the meanwhile, Ensemble Learning, +one of the most efficient approaches in supervised Machine Learning for the +last decade, proceeds by building a population of diverse classifiers. Ensemble +Learning with Evolutionary Computation thus receives increasing attention. The +Evolutionary Ensemble Learning (EEL) approach presented in this paper features +two contributions. First, a new fitness function, inspired by co-evolution and +enforcing the classifier diversity, is presented. Further, a new selection +criterion based on the classification margin is proposed. This criterion is +used to extract the classifier ensemble from the final population only +(Off-line) or incrementally along evolution (On-line). Experiments on a set of +benchmark problems show that Off-line outperforms single-hypothesis +evolutionary learning and state-of-art Boosting and generates smaller +classifier ensembles. +",Ensemble Learning for Free with Evolutionary Algorithms ? +" Gaussian mixture models (GMM) and support vector machines (SVM) are +introduced to classify faults in a population of cylindrical shells. The +proposed procedures are tested on a population of 20 cylindrical shells and +their performance is compared to the procedure, which uses multi-layer +perceptrons (MLP). The modal properties extracted from vibration data are used +to train the GMM, SVM and MLP. It is observed that the GMM produces 98%, SVM +produces 94% classification accuracy while the MLP produces 88% classification +rates. +","Fault Classification in Cylinders Using Multilayer Perceptrons, Support + Vector Machines and Guassian Mixture Models" +" The act of bluffing confounds game designers to this day. The very nature of +bluffing is even open for debate, adding further complication to the process of +creating intelligent virtual players that can bluff, and hence play, +realistically. Through the use of intelligent, learning agents, and carefully +designed agent outlooks, an agent can in fact learn to predict its opponents +reactions based not only on its own cards, but on the actions of those around +it. With this wider scope of understanding, an agent can in learn to bluff its +opponents, with the action representing not an illogical action, as bluffing is +often viewed, but rather as an act of maximising returns through an effective +statistical optimisation. By using a tee dee lambda learning algorithm to +continuously adapt neural network agent intelligence, agents have been shown to +be able to learn to bluff without outside prompting, and even to learn to call +each others bluffs in free, competitive play. +",Learning to Bluff +" The semiring-based constraint satisfaction problems (semiring CSPs), proposed +by Bistarelli, Montanari and Rossi \cite{BMR97}, is a very general framework of +soft constraints. In this paper we propose an abstraction scheme for soft +constraints that uses semiring homomorphism. To find optimal solutions of the +concrete problem, the idea is, first working in the abstract problem and +finding its optimal solutions, then using them to solve the concrete problem. + In particular, we show that a mapping preserves optimal solutions if and only +if it is an order-reflecting semiring homomorphism. Moreover, for a semiring +homomorphism $\alpha$ and a problem $P$ over $S$, if $t$ is optimal in +$\alpha(P)$, then there is an optimal solution $\bar{t}$ of $P$ such that +$\bar{t}$ has the same value as $t$ in $\alpha(P)$. +",Soft constraint abstraction based on semiring homomorphism +" This paper proposes a neuro-rough model based on multi-layered perceptron and +rough set. The neuro-rough model is then tested on modelling the risk of HIV +from demographic data. The model is formulated using Bayesian framework and +trained using Monte Carlo method and Metropolis criterion. When the model was +tested to estimate the risk of HIV infection given the demographic data it was +found to give the accuracy of 62%. The proposed model is able to combine the +accuracy of the Bayesian MLP model and the transparency of Bayesian rough set +model. +",Bayesian Approach to Neuro-Rough Models +" Water plays a pivotal role in many physical processes, and most importantly +in sustaining human life, animal life and plant life. Water supply entities +therefore have the responsibility to supply clean and safe water at the rate +required by the consumer. It is therefore necessary to implement mechanisms and +systems that can be employed to predict both short-term and long-term water +demands. The increasingly growing field of computational intelligence +techniques has been proposed as an efficient tool in the modelling of dynamic +phenomena. The primary objective of this paper is to compare the efficiency of +two computational intelligence techniques in water demand forecasting. The +techniques under comparison are the Artificial Neural Networks (ANNs) and the +Support Vector Machines (SVMs). In this study it was observed that the ANNs +perform better than the SVMs. This performance is measured against the +generalisation ability of the two. +","Artificial Neural Networks and Support Vector Machines for Water Demand + Time Series Forecasting" +" An ensemble based approach for dealing with missing data, without predicting +or imputing the missing values is proposed. This technique is suitable for +online operations of neural networks and as a result, is used for online +condition monitoring. The proposed technique is tested in both classification +and regression problems. An ensemble of Fuzzy-ARTMAPs is used for +classification whereas an ensemble of multi-layer perceptrons is used for the +regression problem. Results obtained using this ensemble-based technique are +compared to those obtained using a combination of auto-associative neural +networks and genetic algorithms and findings show that this method can perform +up to 9% better in regression problems. Another advantage of the proposed +technique is that it eliminates the need for finding the best estimate of the +data, and hence, saves time. +","Fuzzy Artmap and Neural Network Approach to Online Processing of Inputs + with Missing Values" +" Militarised conflict is one of the risks that have a significant impact on +society. Militarised Interstate Dispute (MID) is defined as an outcome of +interstate interactions, which result on either peace or conflict. Effective +prediction of the possibility of conflict between states is an important +decision support tool for policy makers. In a previous research, neural +networks (NNs) have been implemented to predict the MID. Support Vector +Machines (SVMs) have proven to be very good prediction techniques and are +introduced for the prediction of MIDs in this study and compared to neural +networks. The results show that SVMs predict MID better than NNs while NNs give +more consistent and easy to interpret sensitivity analysis than SVMs. +",Artificial Intelligence for Conflict Management +" The idea of symbolic controllers tries to bridge the gap between the top-down +manual design of the controller architecture, as advocated in Brooks' +subsumption architecture, and the bottom-up designer-free approach that is now +standard within the Evolutionary Robotics community. The designer provides a +set of elementary behavior, and evolution is given the goal of assembling them +to solve complex tasks. Two experiments are presented, demonstrating the +efficiency and showing the recursiveness of this approach. In particular, the +sensitivity with respect to the proposed elementary behaviors, and the +robustness w.r.t. generalization of the resulting controllers are studied in +detail. +",Evolving Symbolic Controllers +" This paper introduces a continuous model for Multi-cellular Developmental +Design. The cells are fixed on a 2D grid and exchange ""chemicals"" with their +neighbors during the growth process. The quantity of chemicals that a cell +produces, as well as the differentiation value of the cell in the phenotype, +are controlled by a Neural Network (the genotype) that takes as inputs the +chemicals produced by the neighboring cells at the previous time step. In the +proposed model, the number of iterations of the growth process is not +pre-determined, but emerges during evolution: only organisms for which the +growth process stabilizes give a phenotype (the stable state), others are +declared nonviable. The optimization of the controller is done using the NEAT +algorithm, that optimizes both the topology and the weights of the Neural +Networks. Though each cell only receives local information from its neighbors, +the experimental results of the proposed approach on the 'flags' problems (the +phenotype must match a given 2D pattern) are almost as good as those of a +direct regression approach using the same model with global information. +Moreover, the resulting multi-cellular organisms exhibit almost perfect +self-healing characteristics. +",Robust Multi-Cellular Developmental Design +" This paper uses Artificial Neural Network (ANN) models to compute response of +structural system subject to Indian earthquakes at Chamoli and Uttarkashi +ground motion data. The system is first trained for a single real earthquake +data. The trained ANN architecture is then used to simulate earthquakes with +various intensities and it was found that the predicted responses given by ANN +model are accurate for practical purposes. When the ANN is trained by a part of +the ground motion data, it can also identify the responses of the structural +system well. In this way the safeness of the structural systems may be +predicted in case of future earthquakes without waiting for the earthquake to +occur for the lessons. Time period and the corresponding maximum response of +the building for an earthquake has been evaluated, which is again trained to +predict the maximum response of the building at different time periods. The +trained time period versus maximum response ANN model is also tested for real +earthquake data of other place, which was not used in the training and was +found to be in good agreement. +","Response Prediction of Structural System Subject to Earthquake Motions + using Artificial Neural Network" +" This paper presents a fault classification method which makes use of a +Takagi-Sugeno neuro-fuzzy model and Pseudomodal energies calculated from the +vibration signals of cylindrical shells. The calculation of Pseudomodal +Energies, for the purposes of condition monitoring, has previously been found +to be an accurate method of extracting features from vibration signals. This +calculation is therefore used to extract features from vibration signals +obtained from a diverse population of cylindrical shells. Some of the cylinders +in the population have faults in different substructures. The pseudomodal +energies calculated from the vibration signals are then used as inputs to a +neuro-fuzzy model. A leave-one-out cross-validation process is used to test the +performance of the model. It is found that the neuro-fuzzy model is able to +classify faults with an accuracy of 91.62%, which is higher than the previously +used multilayer perceptron. +","Fault Classification using Pseudomodal Energies and Neuro-fuzzy + modelling" +" This paper presents bushing condition monitoring frameworks that use +multi-layer perceptrons (MLP), radial basis functions (RBF) and support vector +machines (SVM) classifiers. The first level of the framework determines if the +bushing is faulty or not while the second level determines the type of fault. +The diagnostic gases in the bushings are analyzed using the dissolve gas +analysis. MLP gives superior performance in terms of accuracy and training time +than SVM and RBF. In addition, an on-line bushing condition monitoring +approach, which is able to adapt to newly acquired data are introduced. This +approach is able to accommodate new classes that are introduced by incoming +data and is implemented using an incremental learning algorithm that uses MLP. +The testing results improved from 67.5% to 95.8% as new data were introduced +and the testing results improved from 60% to 95.3% as new conditions were +introduced. On average the confidence value of the framework on its decision +was 0.92. +",On-Line Condition Monitoring using Computational Intelligence +" This paper overviews the basic principles and recent advances in the emerging +field of Quantum Computation (QC), highlighting its potential application to +Artificial Intelligence (AI). The paper provides a very brief introduction to +basic QC issues like quantum registers, quantum gates and quantum algorithms +and then it presents references, ideas and research guidelines on how QC can be +used to deal with some basic AI problems, such as search and pattern matching, +as soon as quantum computers become widely available. +",The Road to Quantum Artificial Intelligence +" Cluster matching by permuting cluster labels is important in many clustering +contexts such as cluster validation and cluster ensemble techniques. The +classic approach is to minimize the euclidean distance between two cluster +solutions which induces inappropriate stability in certain settings. Therefore, +we present the truematch algorithm that introduces two improvements best +explained in the crisp case. First, instead of maximizing the trace of the +cluster crosstable, we propose to maximize a chi-square transformation of this +crosstable. Thus, the trace will not be dominated by the cells with the largest +counts but by the cells with the most non-random observations, taking into +account the marginals. Second, we suggest a probabilistic component in order to +break ties and to make the matching algorithm truly random on random data. The +truematch algorithm is designed as a building block of the truecluster +framework and scales in polynomial time. First simulation results confirm that +the truematch algorithm gives more consistent truecluster results for unequal +cluster sizes. Free R software is available. +",Truecluster matching +" Semantic network research has seen a resurgence from its early history in the +cognitive sciences with the inception of the Semantic Web initiative. The +Semantic Web effort has brought forth an array of technologies that support the +encoding, storage, and querying of the semantic network data structure at the +world stage. Currently, the popular conception of the Semantic Web is that of a +data modeling medium where real and conceptual entities are related in +semantically meaningful ways. However, new models have emerged that explicitly +encode procedural information within the semantic network substrate. With these +new technologies, the Semantic Web has evolved from a data modeling medium to a +computational medium. This article provides a classification of existing +computational modeling efforts and the requirements of supporting technologies +that will aid in the further growth of this burgeoning domain. +",Modeling Computations in a Semantic Network +" This paper describes a system capable of semi-automatically filling an XML +template from free texts in the clinical domain (practice guidelines). The XML +template includes semantic information not explicitly encoded in the text +(pairs of conditions and actions/recommendations). Therefore, there is a need +to compute the exact scope of conditions over text sequences expressing the +required actions. We present a system developed for this task. We show that it +yields good performance when applied to the analysis of French practice +guidelines. +",Automatically Restructuring Practice Guidelines using the GEM DTD +" Representing and reasoning about qualitative temporal information is an +essential part of many artificial intelligence tasks. Lots of models have been +proposed in the litterature for representing such temporal information. All +derive from a point-based or an interval-based framework. One fundamental +reasoning task that arises in applications of these frameworks is given by the +following scheme: given possibly indefinite and incomplete knowledge of the +binary relationships between some temporal objects, find the consistent +scenarii between all these objects. All these models require transitive tables +-- or similarly inference rules-- for solving such tasks. We have defined an +alternative model, S-languages - to represent qualitative temporal information, +based on the only two relations of \emph{precedence} and \emph{simultaneity}. +In this paper, we show how this model enables to avoid transitive tables or +inference rules to handle this kind of problem. +",Temporal Reasoning without Transitive Tables +" This paper is a survey of a large number of informal definitions of +``intelligence'' that the authors have collected over the years. Naturally, +compiling a complete list would be impossible as many definitions of +intelligence are buried deep inside articles and books. Nevertheless, the +70-odd definitions presented here are, to the authors' knowledge, the largest +and most well referenced collection there is. +",A Collection of Definitions of Intelligence +" Web semantic access in specific domains calls for specialized search engines +with enhanced semantic querying and indexing capacities, which pertain both to +information retrieval (IR) and to information extraction (IE). A rich +linguistic analysis is required either to identify the relevant semantic units +to index and weight them according to linguistic specific statistical +distribution, or as the basis of an information extraction process. Recent +developments make Natural Language Processing (NLP) techniques reliable enough +to process large collections of documents and to enrich them with semantic +annotations. This paper focuses on the design and the development of a text +processing platform, Ogmios, which has been developed in the ALVIS project. The +Ogmios platform exploits existing NLP modules and resources, which may be tuned +to specific domains and produces linguistically annotated documents. We show +how the three constraints of genericity, domain semantic awareness and +performance can be handled all together. +","A Robust Linguistic Platform for Efficient and Domain specific Web + Content Analysis" +" We consider the problem of finding an n-agent joint-policy for the optimal +finite-horizon control of a decentralized Pomdp (Dec-Pomdp). This is a problem +of very high complexity (NEXP-hard in n >= 2). In this paper, we propose a new +mathematical programming approach for the problem. Our approach is based on two +ideas: First, we represent each agent's policy in the sequence-form and not in +the tree-form, thereby obtaining a very compact representation of the set of +joint-policies. Second, using this compact representation, we solve this +problem as an instance of combinatorial optimization for which we formulate a +mixed integer linear program (MILP). The optimal solution of the MILP directly +yields an optimal joint-policy for the Dec-Pomdp. Computational experience +shows that formulating and solving the MILP requires significantly less time to +solve benchmark Dec-Pomdp problems than existing algorithms. For example, the +multi-agent tiger problem for horizon 4 is solved in 72 secs with the MILP +whereas existing algorithms require several hours to solve it. +","Mixed Integer Linear Programming For Exact Finite-Horizon Planning In + Decentralized Pomdps" +" In this paper, we employ Probabilistic Neural Network (PNN) with image and +data processing techniques to implement a general purpose automated leaf +recognition algorithm. 12 leaf features are extracted and orthogonalized into 5 +principal variables which consist the input vector of the PNN. The PNN is +trained by 1800 leaves to classify 32 kinds of plants with an accuracy greater +than 90%. Compared with other approaches, our algorithm is an accurate +artificial intelligence approach which is fast in execution and easy in +implementation. +","A Leaf Recognition Algorithm for Plant Classification Using + Probabilistic Neural Network" +" When Kurt Goedel layed the foundations of theoretical computer science in +1931, he also introduced essential concepts of the theory of Artificial +Intelligence (AI). Although much of subsequent AI research has focused on +heuristics, which still play a major role in many practical AI applications, in +the new millennium AI theory has finally become a full-fledged formal science, +with important optimality results for embodied agents living in unknown +environments, obtained through a combination of theory a la Goedel and +probability theory. Here we look back at important milestones of AI history, +mention essential recent results, and speculate about what we may expect from +the next 25 years, emphasizing the significance of the ongoing dramatic +hardware speedups, and discussing Goedel-inspired, self-referential, +self-improving universal problem solvers. +","2006: Celebrating 75 years of AI - History and Outlook: the Next 25 + Years" +" In this paper we extend the new family of (quantitative) Belief Conditioning +Rules (BCR) recently developed in the Dezert-Smarandache Theory (DSmT) to their +qualitative counterpart for belief revision. Since the revision of quantitative +as well as qualitative belief assignment given the occurrence of a new event +(the conditioning constraint) can be done in many possible ways, we present +here only what we consider as the most appealing Qualitative Belief +Conditioning Rules (QBCR) which allow to revise the belief directly with words +and linguistic labels and thus avoids the introduction of ad-hoc translations +of quantitative beliefs into quantitative ones for solving the problem. +",Qualitative Belief Conditioning Rules (QBCR) +" Many systems can be described in terms of networks of discrete elements and +their various relationships to one another. A semantic network, or +multi-relational network, is a directed labeled graph consisting of a +heterogeneous set of entities connected by a heterogeneous set of +relationships. Semantic networks serve as a promising general-purpose modeling +substrate for complex systems. Various standardized formats and tools are now +available to support practical, large-scale semantic network models. First, the +Resource Description Framework (RDF) offers a standardized semantic network +data model that can be further formalized by ontology modeling languages such +as RDF Schema (RDFS) and the Web Ontology Language (OWL). Second, the recent +introduction of highly performant triple-stores (i.e. semantic network +databases) allows semantic network models on the order of $10^9$ edges to be +efficiently stored and manipulated. RDF and its related technologies are +currently used extensively in the domains of computer science, digital library +science, and the biological sciences. This article will provide an introduction +to RDF/RDFS/OWL and an examination of its suitability to model discrete element +complex systems. +",Using RDF to Model the Structure and Process of Systems +" This paper deals with enriched qualitative belief functions for reasoning +under uncertainty and for combining information expressed in natural language +through linguistic labels. In this work, two possible enrichments (quantitative +and/or qualitative) of linguistic labels are considered and operators +(addition, multiplication, division, etc) for dealing with them are proposed +and explained. We denote them $qe$-operators, $qe$ standing for +""qualitative-enriched"" operators. These operators can be seen as a direct +extension of the classical qualitative operators ($q$-operators) proposed +recently in the Dezert-Smarandache Theory of plausible and paradoxist reasoning +(DSmT). $q$-operators are also justified in details in this paper. The +quantitative enrichment of linguistic label is a numerical supporting degree in +$[0,\infty)$, while the qualitative enrichment takes its values in a finite +ordered set of linguistic values. Quantitative enrichment is less precise than +qualitative enrichment, but it is expected more close with what human experts +can easily provide when expressing linguistic labels with supporting degrees. +Two simple examples are given to show how the fusion of qualitative-enriched +belief assignments can be done. +",Enrichment of Qualitative Beliefs for Reasoning under Uncertainty +" We try to perform geometrization of psychology by representing mental states, +<>, by points of a metric space, <>. Evolution of ideas is +described by dynamical systems in metric mental space. We apply the mental +space approach for modeling of flows of unconscious and conscious information +in the human brain. In a series of models, Models 1-4, we consider cognitive +systems with increasing complexity of psychological behavior determined by +structure of flows of ideas. Since our models are in fact models of the +AI-type, one immediately recognizes that they can be used for creation of +AI-systems, which we call psycho-robots, exhibiting important elements of human +psyche. Creation of such psycho-robots may be useful improvement of domestic +robots. At the moment domestic robots are merely simple working devices (e.g. +vacuum cleaners or lawn mowers) . However, in future one can expect demand in +systems which be able not only perform simple work tasks, but would have +elements of human self-developing psyche. Such AI-psyche could play an +important role both in relations between psycho-robots and their owners as well +as between psycho-robots. Since the presence of a huge numbers of +psycho-complexes is an essential characteristic of human psychology, it would +be interesting to model them in the AI-framework. +",Toward Psycho-robots +" In this paper we study cellular automata (CAs) that perform the computational +Majority task. This task is a good example of what the phenomenon of emergence +in complex systems is. We take an interest in the reasons that make this +particular fitness landscape a difficult one. The first goal is to study the +landscape as such, and thus it is ideally independent from the actual +heuristics used to search the space. However, a second goal is to understand +the features a good search technique for this particular problem space should +possess. We statistically quantify in various ways the degree of difficulty of +searching this landscape. Due to neutrality, investigations based on sampling +techniques on the whole landscape are difficult to conduct. So, we go exploring +the landscape from the top. Although it has been proved that no CA can perform +the task perfectly, several efficient CAs for this task have been found. +Exploiting similarities between these CAs and symmetries in the landscape, we +define the Olympus landscape which is regarded as the ''heavenly home'' of the +best local optima known (blok). Then we measure several properties of this +subspace. Although it is easier to find relevant CAs in this subspace than in +the overall landscape, there are structural reasons that prevent a searcher +from finding overfitted CAs in the Olympus. Finally, we study dynamics and +performance of genetic algorithms on the Olympus in order to confirm our +analysis and to find efficient CAs for the Majority problem with low +computational cost. +","Fitness landscape of the cellular automata majority problem: View from + the Olympus" +" This paper introduces the concept of fitness cloud as an alternative way to +visualize and analyze search spaces than given by the geographic notion of +fitness landscape. It is argued that the fitness cloud concept overcomes +several deficiencies of the landscape representation. Our analysis is based on +the correlation between fitness of solutions and fitnesses of nearest solutions +according to some neighboring. We focus on the behavior of local search +heuristics, such as hill climber, on the well-known NK fitness landscape. In +both cases the fitness vs. fitness correlation is shown to be related to the +epistatic parameter K. +",Local search heuristics: Fitness Cloud versus Fitness Landscape +" This theoretical work defines the measure of autocorrelation of evolvability +in the context of neutral fitness landscape. This measure has been studied on +the classical MAX-SAT problem. This work highlight a new characteristic of +neutral fitness landscapes which allows to design new adapted metaheuristic. +",Measuring the Evolvability Landscape to study Neutrality +" This paper describes a system capable of semi-automatically filling an XML +template from free texts in the clinical domain (practice guidelines). The XML +template includes semantic information not explicitly encoded in the text +(pairs of conditions and actions/recommendations). Therefore, there is a need +to compute the exact scope of conditions over text sequences expressing the +required actions. We present in this paper the rules developed for this task. +We show that the system yields good performance when applied to the analysis of +French practice guidelines. +","From Texts to Structured Documents: The Case of Health Practice + Guidelines" +" We develop a general framework for MAP estimation in discrete and Gaussian +graphical models using Lagrangian relaxation techniques. The key idea is to +reformulate an intractable estimation problem as one defined on a more +tractable graph, but subject to additional constraints. Relaxing these +constraints gives a tractable dual problem, one defined by a thin graph, which +is then optimized by an iterative procedure. When this iterative optimization +leads to a consistent estimate, one which also satisfies the constraints, then +it corresponds to an optimal MAP estimate of the original model. Otherwise +there is a ``duality gap'', and we obtain a bound on the optimal solution. +Thus, our approach combines convex optimization with dynamic programming +techniques applicable for thin graphs. The popular tree-reweighted max-product +(TRMP) method may be seen as solving a particular class of such relaxations, +where the intractable graph is relaxed to a set of spanning trees. We also +consider relaxations to a set of small induced subgraphs, thin subgraphs (e.g. +loops), and a connected tree obtained by ``unwinding'' cycles. In addition, we +propose a new class of multiscale relaxations that introduce ``summary'' +variables. The potential benefits of such generalizations include: reducing or +eliminating the ``duality gap'' in hard problems, reducing the number or +Lagrange multipliers in the dual problem, and accelerating convergence of the +iterative optimization procedure. +",Lagrangian Relaxation for MAP Estimation in Graphical Models +" This paper addresses a method to analyze the covert social network foundation +hidden behind the terrorism disaster. It is to solve a node discovery problem, +which means to discover a node, which functions relevantly in a social network, +but escaped from monitoring on the presence and mutual relationship of nodes. +The method aims at integrating the expert investigator's prior understanding, +insight on the terrorists' social network nature derived from the complex graph +theory, and computational data processing. The social network responsible for +the 9/11 attack in 2001 is used to execute simulation experiment to evaluate +the performance of the method. +",Analyzing covert social network foundation behind terrorism disaster +" Methods to solve a node discovery problem for a social network are presented. +Covert nodes refer to the nodes which are not observable directly. They +transmit the influence and affect the resulting collaborative activities among +the persons in a social network, but do not appear in the surveillance logs +which record the participants of the collaborative activities. Discovering the +covert nodes is identifying the suspicious logs where the covert nodes would +appear if the covert nodes became overt. The performance of the methods is +demonstrated with a test dataset generated from computationally synthesized +networks and a real organization. +",Node discovery problem for a social network +" An empty spot refers to an empty hard-to-fill space which can be found in the +records of the social interaction, and is the clue to the persons in the +underlying social network who do not appear in the records. This contribution +addresses a problem to predict relevant empty spots in social interaction. +Homogeneous and inhomogeneous networks are studied as a model underlying the +social interaction. A heuristic predictor function approach is presented as a +new method to address the problem. Simulation experiment is demonstrated over a +homogeneous network. A test data in the form of baskets is generated from the +simulated communication. Precision to predict the empty spots is calculated to +demonstrate the performance of the presented approach. +",Predicting relevant empty spots in social interaction +" To appear in Theory and Practice of Logic Programming (TPLP), 2008. + We are researching the interaction between the rule and the ontology layers +of the Semantic Web, by comparing two options: 1) using OWL and its rule +extension SWRL to develop an integrated ontology/rule language, and 2) layering +rules on top of an ontology with RuleML and OWL. Toward this end, we are +developing the SWORIER system, which enables efficient automated reasoning on +ontologies and rules, by translating all of them into Prolog and adding a set +of general rules that properly capture the semantics of OWL. We have also +enabled the user to make dynamic changes on the fly, at run time. This work +addresses several of the concerns expressed in previous work, such as negation, +complementary classes, disjunctive heads, and cardinality, and it discusses +alternative approaches for dealing with inconsistencies in the knowledge base. +In addition, for efficiency, we implemented techniques called +extensionalization, avoiding reanalysis, and code minimization. +","Translating OWL and Semantic Web Rules into Prolog: Moving Toward + Description Logic Programs" +" We consider hexagonal cellular automata with immediate cell neighbourhood and +three cell-states. Every cell calculates its next state depending on the +integral representation of states in its neighbourhood, i.e. how many +neighbours are in each one state. We employ evolutionary algorithms to breed +local transition functions that support mobile localizations (gliders), and +characterize sets of the functions selected in terms of quasi-chemical systems. +Analysis of the set of functions evolved allows to speculate that mobile +localizations are likely to emerge in the quasi-chemical systems with limited +diffusion of one reagent, a small number of molecules is required for +amplification of travelling localizations, and reactions leading to stationary +localizations involve relatively equal amount of quasi-chemical species. +Techniques developed can be applied in cascading signals in nature-inspired +spatially extended computing devices, and phenomenological studies and +classification of non-linear discrete systems. +",Evolving localizations in reaction-diffusion cellular automata +" We describe decomposition during search (DDS), an integration of And/Or tree +search into propagation-based constraint solvers. The presented search +algorithm dynamically decomposes sub-problems of a constraint satisfaction +problem into independent partial problems, avoiding redundant work. + The paper discusses how DDS interacts with key features that make +propagation-based solvers successful: constraint propagation, especially for +global constraints, and dynamic search heuristics. + We have implemented DDS for the Gecode constraint programming library. Two +applications, solution counting in graph coloring and protein structure +prediction, exemplify the benefits of DDS in practice. +",Decomposition During Search for Propagation-Based Constraint Solvers +" A fundamental problem in artificial intelligence is that nobody really knows +what intelligence is. The problem is especially acute when we need to consider +artificial systems which are significantly different to humans. In this paper +we approach this problem in the following way: We take a number of well known +informal definitions of human intelligence that have been given by experts, and +extract their essential features. These are then mathematically formalised to +produce a general measure of intelligence for arbitrary machines. We believe +that this equation formally captures the concept of machine intelligence in the +broadest reasonable sense. We then show how this formal definition is related +to the theory of universal optimal learning agents. Finally, we survey the many +other tests and definitions of intelligence that have been proposed for +machines. +",Universal Intelligence: A Definition of Machine Intelligence +" Although the definition and measurement of intelligence is clearly of +fundamental importance to the field of artificial intelligence, no general +survey of definitions and tests of machine intelligence exists. Indeed few +researchers are even aware of alternatives to the Turing test and its many +derivatives. In this paper we fill this gap by providing a short survey of the +many tests of machine intelligence that have been proposed. +",Tests of Machine Intelligence +" We consider an agent interacting with an unknown environment. The environment +is a function which maps natural numbers to natural numbers; the agent's set of +hypotheses about the environment contains all such functions which are +computable and compatible with a finite set of known input-output pairs, and +the agent assigns a positive probability to each such hypothesis. We do not +require that this probability distribution be computable, but it must be +bounded below by a positive computable function. The agent has a utility +function on outputs from the environment. We show that if this utility function +is bounded below in absolute value by an unbounded computable function, then +the expected utility of any input is undefined. This implies that a computable +utility function will have convergent expected utilities iff that function is +bounded. +","Convergence of Expected Utilities with Algorithmic Probability + Distributions" +" Most definitions of ontology, viewed as a ""specification of a +conceptualization"", agree on the fact that if an ontology can take different +forms, it necessarily includes a vocabulary of terms and some specification of +their meaning in relation to the domain's conceptualization. And as domain +knowledge is mainly conveyed through scientific and technical texts, we can +hope to extract some useful information from them for building ontology. But is +it as simple as this? In this article we shall see that the lexical structure, +i.e. the network of words linked by linguistic relationships, does not +necessarily match the domain conceptualization. We have to bear in mind that +writing documents is the concern of textual linguistics, of which one of the +principles is the incompleteness of text, whereas building ontology - viewed as +task-independent knowledge - is concerned with conceptualization based on +formal and not natural languages. Nevertheless, the famous Sapir and Whorf +hypothesis, concerning the interdependence of thought and language, is also +applicable to formal languages. This means that the way an ontology is built +and a concept is defined depends directly on the formal language which is used; +and the results will not be the same. The introduction of the notion of +ontoterminology allows to take into account epistemological principles for +formal ontology building. +",Le terme et le concept : fondements d'une ontoterminologie +" Stream computing is the use of multiple autonomic and parallel modules +together with integrative processors at a higher level of abstraction to embody +""intelligent"" processing. The biological basis of this computing is sketched +and the matter of learning is examined. +",Stream Computing +" In this paper we introduce a new selection scheme in cellular genetic +algorithms (cGAs). Anisotropic Selection (AS) promotes diversity and allows +accurate control of the selective pressure. First we compare this new scheme +with the classical rectangular grid shapes solution according to the selective +pressure: we can obtain the same takeover time with the two techniques although +the spreading of the best individual is different. We then give experimental +results that show to what extent AS promotes the emergence of niches that +support low coupling and high cohesion. Finally, using a cGA with anisotropic +selection on a Quadratic Assignment Problem we show the existence of an +anisotropic optimal value for which the best average performance is observed. +Further work will focus on the selective pressure self-adjustment ability +provided by this new selection scheme. +",Anisotropic selection in cellular genetic algorithms +" This philosophical paper explores the relation between modern scientific +simulations and the future of the universe. We argue that a simulation of an +entire universe will result from future scientific activity. This requires us +to tackle the challenge of simulating open-ended evolution at all levels in a +single simulation. The simulation should encompass not only biological +evolution, but also physical evolution (a level below) and cultural evolution +(a level above). The simulation would allow us to probe what would happen if we +would ""replay the tape of the universe"" with the same or different laws and +initial conditions. We also distinguish between real-world and artificial-world +modelling. Assuming that intelligent life could indeed simulate an entire +universe, this leads to two tentative hypotheses. Some authors have argued that +we may already be in a simulation run by an intelligent entity. Or, if such a +simulation could be made real, this would lead to the production of a new +universe. This last direction is argued with a careful speculative +philosophical approach, emphasizing the imperative to find a solution to the +heat death problem in cosmology. The reader is invited to consult Annex 1 for +an overview of the logical structure of this paper. -- Keywords: far future, +future of science, ALife, simulation, realization, cosmology, heat death, +fine-tuning, physical eschatology, cosmological natural selection, cosmological +artificial selection, artificial cosmogenesis, selfish biocosm hypothesis, +meduso-anthropic principle, developmental singularity hypothesis, role of +intelligent life. +","The Future of Scientific Simulations: from Artificial Life to Artificial + Cosmogenesis" +" This paper has been withdrawn. +",Serious Flaws in Korf et al.'s Analysis on Time Complexity of A* +" In this paper, we describe a new algorithm that consists in combining an +eye-tracker for minimizing the fatigue of a user during the evaluation process +of Interactive Evolutionary Computation. The approach is then applied to the +Interactive One-Max optimization problem. +","Eye-Tracking Evolutionary Algorithm to minimize user's fatigue in IEC + applied to Interactive One-Max problem" +" In this paper, I present a method to solve a node discovery problem in a +networked organization. Covert nodes refer to the nodes which are not +observable directly. They affect social interactions, but do not appear in the +surveillance logs which record the participants of the social interactions. +Discovering the covert nodes is defined as identifying the suspicious logs +where the covert nodes would appear if the covert nodes became overt. A +mathematical model is developed for the maximal likelihood estimation of the +network behind the social interactions and for the identification of the +suspicious logs. Precision, recall, and F measure characteristics are +demonstrated with the dataset generated from a real organization and the +computationally synthesized datasets. The performance is close to the +theoretical limit for any covert nodes in the networks of any topologies and +sizes if the ratio of the number of observation to the number of possible +communication patterns is large. +",Node discovery in a networked organization +" In an emergency situation, the actors need an assistance allowing them to +react swiftly and efficiently. In this prospect, we present in this paper a +decision support system that aims to prepare actors in a crisis situation +thanks to a decision-making support. The global architecture of this system is +presented in the first part. Then we focus on a part of this system which is +designed to represent the information of the current situation. This part is +composed of a multiagent system that is made of factual agents. Each agent +carries a semantic feature and aims to represent a partial part of a situation. +The agents develop thanks to their interactions by comparing their semantic +features using proximity measures and according to specific ontologies. +","Multiagent Approach for the Representation of Information in a Decision + Support System" +" A new method is presented, that can help a person become aware of his or her +unconscious preferences, and convey them to others in the form of verbal +explanation. The method combines the concepts of reflection, visualization, and +verbalization. The method was tested in an experiment where the unconscious +preferences of the subjects for various artworks were investigated. In the +experiment, two lessons were learned. The first is that it helps the subjects +become aware of their unconscious preferences to verbalize weak preferences as +compared with strong preferences through discussion over preference diagrams. +The second is that it is effective to introduce an adjustable factor into +visualization to adapt to the differences in the subjects and to foster their +mutual understanding. +",Reflective visualization and verbalization of unconscious preference +" This paper describes application of rough set theory, on the analysis of +hydrocyclone operation. In this manner, using Self Organizing Map (SOM) as +preprocessing step, best crisp granules of data are obtained. Then, using a +combining of SOM and rough set theory (RST)-called SORST-, the dominant rules +on the information table, obtained from laboratory tests, are extracted. Based +on these rules, an approximate estimation on decision attribute is fulfilled. +Finally, a brief comparison of this method with the SOM-NFIS system (briefly +SONFIS) is highlighted. +",Application of Rough Set Theory to Analysis of Hydrocyclone Operation +" We are interested in the problem of multiagent systems development for risk +detecting and emergency response in an uncertain and partially perceived +environment. The evaluation of the current situation passes by three stages +inside the multiagent system. In a first time, the situation is represented in +a dynamic way. The second step, consists to characterise the situation and +finally, it is compared with other similar known situations. In this paper, we +present an information modelling of an observed environment, that we have +applied on the RoboCupRescue Simulation System. Information coming from the +environment are formatted according to a taxonomy and using semantic features. +The latter are defined thanks to a fine ontology of the domain and are managed +by factual agents that aim to represent dynamically the current situation. +",Agent-Based Perception of an Environment in an Emergency Situation +" In this paper, we study the influence of the selective pressure on the +performance of cellular genetic algorithms. Cellular genetic algorithms are +genetic algorithms where the population is embedded on a toroidal grid. This +structure makes the propagation of the best so far individual slow down, and +allows to keep in the population potentially good solutions. We present two +selective pressure reducing strategies in order to slow down even more the best +solution propagation. We experiment these strategies on a hard optimization +problem, the quadratic assignment problem, and we show that there is a value +for of the control parameter for both which gives the best performance. This +optimal value does not find explanation on only the selective pressure, +measured either by take over time and diversity evolution. This study makes us +conclude that we need other tools than the sole selective pressure measures to +explain the performances of cellular genetic algorithms. +","On the Influence of Selection Operators on Performances in Cellular + Genetic Algorithms" +" Review of: Brigitte Le Roux and Henry Rouanet, Geometric Data Analysis, From +Correspondence Analysis to Structured Data Analysis, Kluwer, Dordrecht, 2004, +xi+475 pp. +","Geometric Data Analysis, From Correspondence Analysis to Structured Data + Analysis (book review)" +" In this study, we introduce general frame of MAny Connected Intelligent +Particles Systems (MACIPS). Connections and interconnections between particles +get a complex behavior of such merely simple system (system in +system).Contribution of natural computing, under information granulation +theory, are the main topics of this spacious skeleton. Upon this clue, we +organize two algorithms involved a few prominent intelligent computing and +approximate reasoning methods: self organizing feature map (SOM), Neuro- Fuzzy +Inference System and Rough Set Theory (RST). Over this, we show how our +algorithms can be taken as a linkage of government-society interaction, where +government catches various fashions of behavior: solid (absolute) or flexible. +So, transition of such society, by changing of connectivity parameters (noise) +from order to disorder is inferred. Add to this, one may find an indirect +mapping among financial systems and eventual market fluctuations with MACIPS. +Keywords: phase transition, SONFIS, SORST, many connected intelligent particles +system, society-government interaction +",Phase transition in SONFIS&SORST +" Affinity propagation clustering (AP) has two limitations: it is hard to know +what value of parameter 'preference' can yield an optimal clustering solution, +and oscillations cannot be eliminated automatically if occur. The adaptive AP +method is proposed to overcome these limitations, including adaptive scanning +of preferences to search space of the number of clusters for finding the +optimal clustering solution, adaptive adjustment of damping factors to +eliminate oscillations, and adaptive escaping from oscillations when the +damping adjustment technique fails. Experimental results on simulated and real +data sets show that the adaptive AP is effective and can outperform AP in +quality of clustering results. +",Adaptive Affinity Propagation Clustering +" Approximately more than 90% of all coal production in Iranian underground +mines is derived directly longwall mining method. Out of seam dilution is one +of the essential problems in these mines. Therefore the dilution can impose the +additional cost of mining and milling. As a result, recognition of the +effective parameters on the dilution has a remarkable role in industry. In this +way, this paper has analyzed the influence of 13 parameters (attributed +variables) versus the decision attribute (dilution value), so that using two +approximate reasoning methods, namely Rough Set Theory (RST) and Self +Organizing Neuro- Fuzzy Inference System (SONFIS) the best rules on our +collected data sets has been extracted. The other benefit of later methods is +to predict new unknown cases. So, the reduced sets (reducts) by RST have been +obtained. Therefore the emerged results by utilizing mentioned methods shows +that the high sensitive variables are thickness of layer, length of stope, rate +of advance, number of miners, type of advancing. +","Assessment of effective parameters on dilution using approximate + reasoning methods in longwall mining method, Iran coal mines" +" This study, fundamentals of fuzzy block theory, and its application in +assessment of stability in underground openings, has surveyed. Using fuzzy +topics and inserting them in to key block theory, in two ways, fundamentals of +fuzzy block theory has been presented. In indirect combining, by coupling of +adaptive Neuro Fuzzy Inference System (NFIS) and classic block theory, we could +extract possible damage parts around a tunnel. In direct solution, some +principles of block theory, by means of different fuzzy facets theory, were +rewritten. +",Toward Fuzzy block theory +" This paper describes application of information granulation theory, on the +analysis of hydrocyclone perforamance. In this manner, using a combining of +Self Organizing Map (SOM) and Neuro-Fuzzy Inference System (NFIS), crisp and +fuzzy granules are obtained(briefly called SONFIS). Balancing of crisp granules +and sub fuzzy granules, within non fuzzy information (initial granulation), is +rendered in an open-close iteration. Using two criteria, ""simplicity of rules +""and ""adaptive threoshold error level"", stability of algorithm is guaranteed. +Validation of the proposed method, on the data set of the hydrocyclone is +rendered. +","Analysis of hydrocyclone performance based on information granulation + theory" +" In everyday life it happens that a person has to reason about what other +people think and how they behave, in order to achieve his goals. In other +words, an individual may be required to adapt his behaviour by reasoning about +the others' mental state. In this paper we focus on a knowledge representation +language derived from logic programming which both supports the representation +of mental states of individual communities and provides each with the +capability of reasoning about others' mental states and acting accordingly. The +proposed semantics is shown to be translatable into stable model semantics of +logic programs with aggregates. +",Logic programming with social features +" Many social Web sites allow users to publish content and annotate with +descriptive metadata. In addition to flat tags, some social Web sites have +recently began to allow users to organize their content and metadata +hierarchically. The social photosharing site Flickr, for example, allows users +to group related photos in sets, and related sets in collections. The social +bookmarking site Del.icio.us similarly lets users group related tags into +bundles. Although the sites themselves don't impose any constraints on how +these hierarchies are used, individuals generally use them to capture +relationships between concepts, most commonly the broader/narrower relations. +Collective annotation of content with hierarchical relations may lead to an +emergent classification system, called a folksonomy. While some researchers +have explored using tags as evidence for learning folksonomies, we believe that +hierarchical relations described above offer a high-quality source of evidence +for this task. + We propose a simple approach to aggregate shallow hierarchies created by many +distinct Flickr users into a common folksonomy. Our approach uses statistics to +determine if a particular relation should be retained or discarded. The +relations are then woven together into larger hierarchies. Although we have not +carried out a detailed quantitative evaluation of the approach, it looks very +promising since it generates very reasonable, non-trivial hierarchies. +",Constructing Folksonomies from User-specified Relations on Flickr +" We analyze the style and structure of story narrative using the case of film +scripts. The practical importance of this is noted, especially the need to have +support tools for television movie writing. We use the Casablanca film script, +and scripts from six episodes of CSI (Crime Scene Investigation). For analysis +of style and structure, we quantify various central perspectives discussed in +McKee's book, ""Story: Substance, Structure, Style, and the Principles of +Screenwriting"". Film scripts offer a useful point of departure for exploration +of the analysis of more general narratives. Our methodology, using +Correspondence Analysis, and hierarchical clustering, is innovative in a range +of areas that we discuss. In particular this work is groundbreaking in taking +the qualitative analysis of McKee and grounding this analysis in a quantitative +and algorithmic framework. +",The Structure of Narrative: the Case of Film Scripts +" In the last year more than 70,000 people have been brought to the UK +hospitals with serious injuries. Each time a clinician has to urgently take a +patient through a screening procedure to make a reliable decision on the trauma +treatment. Typically, such procedure comprises around 20 tests; however the +condition of a trauma patient remains very difficult to be tested properly. +What happens if these tests are ambiguously interpreted, and information about +the severity of the injury will come misleading? The mistake in a decision can +be fatal: using a mild treatment can put a patient at risk of dying from +posttraumatic shock, while using an overtreatment can also cause death. How can +we reduce the risk of the death caused by unreliable decisions? It has been +shown that probabilistic reasoning, based on the Bayesian methodology of +averaging over decision models, allows clinicians to evaluate the uncertainty +in decision making. Based on this methodology, in this paper we aim at +selecting the most important screening tests, keeping a high performance. We +assume that the probabilistic reasoning within the Bayesian methodology allows +us to discover new relationships between the screening tests and uncertainty in +decisions. In practice, selection of the most informative tests can also reduce +the cost of a screening procedure in trauma care centers. In our experiments we +use the UK Trauma data to compare the efficiency of the proposed technique in +terms of the performance. We also compare the uncertainty in decisions in terms +of entropy. +",Feature Selection for Bayesian Evaluation of Trauma Death Risk +" We present in this article a new evaluation method for classification and +segmentation of textured images in uncertain environments. In uncertain +environments, real classes and boundaries are known with only a partial +certainty given by the experts. Most of the time, in many presented papers, +only classification or only segmentation are considered and evaluated. Here, we +propose to take into account both the classification and segmentation results +according to the certainty given by the experts. We present the results of this +method on a fusion of classifiers of sonar images for a seabed +characterization. +",Fusion for Evaluation of Image Classification in Uncertain Environments +" The investigation of the terrorist attack is a time-critical task. The +investigators have a limited time window to diagnose the organizational +background of the terrorists, to run down and arrest the wire-pullers, and to +take an action to prevent or eradicate the terrorist attack. The intuitive +interface to visualize the intelligence data set stimulates the investigators' +experience and knowledge, and aids them in decision-making for an immediately +effective action. This paper presents a computational method to analyze the +intelligence data set on the collective actions of the perpetrators of the +attack, and to visualize it into the form of a social network diagram which +predicts the positions where the wire-pullers conceals themselves. +","Intuitive visualization of the intelligence for the run-down of + terrorist wire-pullers" +" This paper describes application of information granulation theory, on the +design of rock engineering flowcharts. Firstly, an overall flowchart, based on +information granulation theory has been highlighted. Information granulation +theory, in crisp (non-fuzzy) or fuzzy format, can take into account engineering +experiences (especially in fuzzy shape-incomplete information or superfluous), +or engineering judgments, in each step of designing procedure, while the +suitable instruments modeling are employed. In this manner and to extension of +soft modeling instruments, using three combinations of Self Organizing Map +(SOM), Neuro-Fuzzy Inference System (NFIS), and Rough Set Theory (RST) crisp +and fuzzy granules, from monitored data sets are obtained. The main underlined +core of our algorithms are balancing of crisp(rough or non-fuzzy) granules and +sub fuzzy granules, within non fuzzy information (initial granulation) upon the +open-close iterations. Using different criteria on balancing best granules +(information pockets), are obtained. Validations of our proposed methods, on +the data set of in-situ permeability in rock masses in Shivashan dam, Iran have +been highlighted. +",Rock mechanics modeling based on soft granulation theory +" Knowledge-based economy forces companies in the nation to group together as a +cluster in order to maintain their competitiveness in the world market. The +cluster development relies on two key success factors which are knowledge +sharing and collaboration between the actors in the cluster. Thus, our study +tries to propose knowledge management system to support knowledge management +activities within the cluster. To achieve the objectives of this study, +ontology takes a very important role in knowledge management process in various +ways; such as building reusable and faster knowledge-bases, better way for +representing the knowledge explicitly. However, creating and representing +ontology create difficulties to organization due to the ambiguity and +unstructured of source of knowledge. Therefore, the objectives of this paper +are to propose the methodology to create and represent ontology for the +organization development by using knowledge engineering approach. The +handicraft cluster in Thailand is used as a case study to illustrate our +proposed methodology. +",An Ontology-based Knowledge Management System for Industry Clusters +" Crisis response poses many of the most difficult information technology in +crisis management. It requires information and communication-intensive efforts, +utilized for reducing uncertainty, calculating and comparing costs and +benefits, and managing resources in a fashion beyond those regularly available +to handle routine problems. In this paper, we explore the benefits of +artificial intelligence technologies in crisis response. This paper discusses +the role of artificial intelligence technologies; namely, robotics, ontology +and semantic web, and multi-agent systems in crisis response. +",The Role of Artificial Intelligence Technologies in Crisis Response +" We present and discuss a mixed conjunctive and disjunctive rule, a +generalization of conflict repartition rules, and a combination of these two +rules. In the belief functions theory one of the major problem is the conflict +repartition enlightened by the famous Zadeh's example. To date, many +combination rules have been proposed in order to solve a solution to this +problem. Moreover, it can be important to consider the specificity of the +responses of the experts. Since few year some unification rules are proposed. +We have shown in our previous works the interest of the proportional conflict +redistribution rule. We propose here a mixed combination rule following the +proportional conflict redistribution rule modified by a discounting procedure. +This rule generalizes many combination rules. +","Toward a combination rule to deal with partial conflict and specificity + in belief functions theory" +" In this chapter, we present and discuss a new generalized proportional +conflict redistribution rule. The Dezert-Smarandache extension of the +Demster-Shafer theory has relaunched the studies on the combination rules +especially for the management of the conflict. Many combination rules have been +proposed in the last few years. We study here different combination rules and +compare them in terms of decision on didactic example and on generated data. +Indeed, in real applications, we need a reliable decision and it is the final +results that matter. This chapter shows that a fine proportional conflict +redistribution rule must be preferred for the combination in the belief +function theory. +","A new generalization of the proportional conflict redistribution rule + stable in terms of decision" +" These last years, there were many studies on the problem of the conflict +coming from information combination, especially in evidence theory. We can +summarise the solutions for manage the conflict into three different +approaches: first, we can try to suppress or reduce the conflict before the +combination step, secondly, we can manage the conflict in order to give no +influence of the conflict in the combination step, and then take into account +the conflict in the decision step, thirdly, we can take into account the +conflict in the combination step. The first approach is certainly the better, +but not always feasible. It is difficult to say which approach is the best +between the second and the third. However, the most important is the produced +results in applications. We propose here a new combination rule that +distributes the conflict proportionally on the element given this conflict. We +compare these different combination rules on real data in Sonar imagery and +Radar target classification. +","Une nouvelle r\`egle de combinaison r\'epartissant le conflit - + Applications en imagerie Sonar et classification de cibles Radar" +" When implementing a propagator for a constraint, one must decide about +variants: When implementing min, should one also implement max? Should one +implement linear equations both with and without coefficients? Constraint +variants are ubiquitous: implementing them requires considerable (if not +prohibitive) effort and decreases maintainability, but will deliver better +performance. + This paper shows how to use variable views, previously introduced for an +implementation architecture, to derive perfect propagator variants. A model for +views and derived propagators is introduced. Derived propagators are proved to +be indeed perfect in that they inherit essential properties such as correctness +and domain and bounds consistency. Techniques for systematically deriving +propagators such as transformation, generalization, specialization, and +channeling are developed for several variable domains. We evaluate the massive +impact of derived propagators. Without derived propagators, Gecode would +require 140000 rather than 40000 lines of code for propagators. +",Perfect Derived Propagators +" A serious defect with the Halpern-Pearl (HP) definition of causality is +repaired by combining a theory of causality with a theory of defaults. In +addition, it is shown that (despite a claim to the contrary) a cause according +to the HP condition need not be a single conjunct. A definition of causality +motivated by Wright's NESS test is shown to always hold for a single conjunct. +Moreover, conditions that hold for all the examples considered by HP are given +that guarantee that causality according to (this version) of the NESS test is +equivalent to the HP definition. +",Defaults and Normality in Causal Structures +" This paper has been withdrawn by the author due to extremely unscientific +errors. +",The model of quantum evolution +" The textured images' classification assumes to consider the images in terms +of area with the same texture. In uncertain environment, it could be better to +take an imprecise decision or to reject the area corresponding to an unlearning +class. Moreover, on the areas that are the classification units, we can have +more than one texture. These considerations allows us to develop a belief +decision model permitting to reject an area as unlearning and to decide on +unions and intersections of learning classes. The proposed approach finds all +its justification in an application of seabed characterization from sonar +images, which contributes to an illustration. +",Belief decision support and reject for textured images characterization +" We study two aspects of information semantics: (i) the collection of all +relationships, (ii) tracking and spotting anomaly and change. The first is +implemented by endowing all relevant information spaces with a Euclidean metric +in a common projected space. The second is modelled by an induced ultrametric. +A very general way to achieve a Euclidean embedding of different information +spaces based on cross-tabulation counts (and from other input data formats) is +provided by Correspondence Analysis. From there, the induced ultrametric that +we are particularly interested in takes a sequential - e.g. temporal - ordering +of the data into account. We employ such a perspective to look at narrative, +""the flow of thought and the flow of language"" (Chafe). In application to +policy decision making, we show how we can focus analysis in a small number of +dimensions. +","The Correspondence Analysis Platform for Uncovering Deep Structure in + Data and Information" +" In this paper we extend Inagaki Weighted Operators fusion rule (WO) in +information fusion by doing redistribution of not only the conflicting mass, +but also of masses of non-empty intersections, that we call Double Weighted +Operators (DWO). Then we propose a new fusion rule Class of Proportional +Redistribution of Intersection Masses (CPRIM), which generates many interesting +particular fusion rules in information fusion. Both formulas are presented for +any number of sources of information. An application and comparison with other +fusion rules are given in the last section. +","Extension of Inagaki General Weighted Operators and A New Fusion Rule + Class of Proportional Redistribution of Intersection Masses" +" In this chapter, we propose a new practical codification of the elements of +the Venn diagram in order to easily manipulate the focal elements. In order to +reduce the complexity, the eventual constraints must be integrated in the +codification at the beginning. Hence, we only consider a reduced hyper power +set $D_r^\Theta$ that can be $2^\Theta$ or $D^\Theta$. We describe all the +steps of a general belief function framework. The step of decision is +particularly studied, indeed, when we can decide on intersections of the +singletons of the discernment space no actual decision functions are easily to +use. Hence, two approaches are proposed, an extension of previous one and an +approach based on the specificity of the elements on which to decide. The +principal goal of this chapter is to provide practical codes of a general +belief function framework for the researchers and users needing the belief +function theory. +","Implementing general belief function framework with a practical + codification for low complexity" +" In this paper, we propose in Dezert-Smarandache Theory (DSmT) framework, a +new probabilistic transformation, called DSmP, in order to build a subjective +probability measure from any basic belief assignment defined on any model of +the frame of discernment. Several examples are given to show how the DSmP +transformation works and we compare it to main existing transformations +proposed in the literature so far. We show the advantages of DSmP over +classical transformations in term of Probabilistic Information Content (PIC). +The direct extension of this transformation for dealing with qualitative belief +assignments is also presented. +",A new probabilistic transformation of belief mass assignment +" We discuss metacognitive modelling as an enhancement to cognitive modelling +and computing. Metacognitive control mechanisms should enable AI systems to +self-reflect, reason about their actions, and to adapt to new situations. In +this respect, we propose implementation details of a knowledge taxonomy and an +augmented data mining life cycle which supports a live integration of obtained +models. +","On Introspection, Metacognitive Control and Augmented Data Mining Live + Cycles" +" Each cognitive science tries to understand a set of cognitive behaviors. The +structuring of knowledge of this nature's aspect is far from what it can be +expected about a science. Until now universal standard consistently describing +the set of cognitive behaviors has not been found, and there are many questions +about the cognitive behaviors for which only there are opinions of members of +the scientific community. This article has three proposals. The first proposal +is to raise to the scientific community the necessity of unified the cognitive +behaviors. The second proposal is claim the application of the Newton's +reasoning rules about nature of his book, Philosophiae Naturalis Principia +Mathematica, to the cognitive behaviors. The third is to propose a scientific +theory, currently developing, that follows the rules established by Newton to +make sense of nature, and could be the theory to explain all the cognitive +behaviors. +",Hacia una teoria de unificacion para los comportamientos cognitivos +" In this article we review standard null-move pruning and introduce our +extended version of it, which we call verified null-move pruning. In verified +null-move pruning, whenever the shallow null-move search indicates a fail-high, +instead of cutting off the search from the current node, the search is +continued with reduced depth. + Our experiments with verified null-move pruning show that on average, it +constructs a smaller search tree with greater tactical strength in comparison +to standard null-move pruning. Moreover, unlike standard null-move pruning, +which fails badly in zugzwang positions, verified null-move pruning manages to +detect most zugzwangs and in such cases conducts a re-search to obtain the +correct result. In addition, verified null-move pruning is very easy to +implement, and any standard null-move pruning program can use verified +null-move pruning by modifying only a few lines of code. +",Verified Null-Move Pruning +" We extend Knuth's 16 Boolean binary logic operators to fuzzy logic and +neutrosophic logic binary operators. Then we generalize them to n-ary fuzzy +logic and neutrosophic logic operators using the smarandache codification of +the Venn diagram and a defined vector neutrosophic law. In such way, new +operators in neutrosophic logic/set/probability are built. +",n-ary Fuzzy Logic and Neutrosophic Logic Operators +" Various local search approaches have recently been applied to machine +scheduling problems under multiple objectives. Their foremost consideration is +the identification of the set of Pareto optimal alternatives. An important +aspect of successfully solving these problems lies in the definition of an +appropriate neighbourhood structure. Unclear in this context remains, how +interdependencies within the fitness landscape affect the resolution of the +problem. + The paper presents a study of neighbourhood search operators for multiple +objective flow shop scheduling. Experiments have been carried out with twelve +different combinations of criteria. To derive exact conclusions, small problem +instances, for which the optimal solutions are known, have been chosen. +Statistical tests show that no single neighbourhood operator is able to equally +identify all Pareto optimal alternatives. Significant improvements however have +been obtained by hybridising the solution algorithm using a randomised variable +neighbourhood search technique. +","Randomised Variable Neighbourhood Search for Multi Objective + Optimisation" +" The paper describes the proposition and application of a local search +metaheuristic for multi-objective optimization problems. It is based on two +main principles of heuristic search, intensification through variable +neighborhoods, and diversification through perturbations and successive +iterations in favorable regions of the search space. The concept is +successfully tested on permutation flow shop scheduling problems under multiple +objectives. While the obtained results are encouraging in terms of their +quality, another positive attribute of the approach is its' simplicity as it +does require the setting of only very few parameters. The implementation of the +Pareto Iterated Local Search metaheuristic is based on the MOOPPS computer +system of local search heuristics for multi-objective scheduling which has been +awarded the European Academic Software Award 2002 in Ronneby, Sweden +(http://www.easa-award.net/, http://www.bth.se/llab/easa_2002.nsf) +",Foundations of the Pareto Iterated Local Search Metaheuristic +" The article describes an investigation of the effectiveness of genetic +algorithms for multi-objective combinatorial optimization (MOCO) by presenting +an application for the vehicle routing problem with soft time windows. The work +is motivated by the question, if and how the problem structure influences the +effectiveness of different configurations of the genetic algorithm. +Computational results are presented for different classes of vehicle routing +problems, varying in their coverage with time windows, time window size, +distribution and number of customers. The results are compared with a simple, +but effective local search approach for multi-objective combinatorial +optimization problems. +","A Computational Study of Genetic Crossover Operators for Multi-Objective + Vehicle Routing Problem with Soft Time Windows" +" The talk describes a general approach of a genetic algorithm for multiple +objective optimization problems. A particular dominance relation between the +individuals of the population is used to define a fitness operator, enabling +the genetic algorithm to adress even problems with efficient, but +convex-dominated alternatives. The algorithm is implemented in a multilingual +computer program, solving vehicle routing problems with time windows under +multiple objectives. The graphical user interface of the program shows the +progress of the genetic algorithm and the main parameters of the approach can +be easily modified. In addition to that, the program provides powerful decision +support to the decision maker. The software has proved it's excellence at the +finals of the European Academic Software Award EASA, held at the Keble college/ +University of Oxford/ Great Britain. +",Genetic Algorithms for multiple objective vehicle routing +" The article presents a framework for the resolution of rich vehicle routing +problems which are difficult to address with standard optimization techniques. +We use local search on the basis on variable neighborhood search for the +construction of the solutions, but embed the techniques in a flexible framework +that allows the consideration of complex side constraints of the problem such +as time windows, multiple depots, heterogeneous fleets, and, in particular, +multiple optimization criteria. In order to identify a compromise alternative +that meets the requirements of the decision maker, an interactive procedure is +integrated in the resolution of the problem, allowing the modification of the +preference information articulated by the decision maker. The framework is +prototypically implemented in a computer system. First results of test runs on +multiple depot vehicle routing problems with time windows are reported. +","A framework for the interactive resolution of multi-objective vehicle + routing problems" +" The integration of fuzzy set theory and fuzzy logic into scheduling is a +rather new aspect with growing importance for manufacturing applications, +resulting in various unsolved aspects. In the current paper, we investigate an +improved local search technique for fuzzy scheduling problems with fitness +plateaus, using a multi criteria formulation of the problem. We especially +address the problem of changing job priorities over time as studied at the +Sherwood Press Ltd, a Nottingham based printing company, who is a collaborator +on the project. +",Improving Local Search for Fuzzy Scheduling Problems +" The article proposes a heuristic approximation approach to the bin packing +problem under multiple objectives. In addition to the traditional objective of +minimizing the number of bins, the heterogeneousness of the elements in each +bin is minimized, leading to a biobjective formulation of the problem with a +tradeoff between the number of bins and their heterogeneousness. An extension +of the Best-Fit approximation algorithm is presented to solve the problem. +Experimental investigations have been carried out on benchmark instances of +different size, ranging from 100 to 1000 items. Encouraging results have been +obtained, showing the applicability of the heuristic approach to the described +problem. +","Bin Packing Under Multiple Objectives - a Heuristic Approximation + Approach" +" The article presents a local search approach for the solution of timetabling +problems in general, with a particular implementation for competition track 3 +of the International Timetabling Competition 2007 (ITC 2007). The heuristic +search procedure is based on Threshold Accepting to overcome local optima. A +stochastic neighborhood is proposed and implemented, randomly removing and +reassigning events from the current solution. + The overall concept has been incrementally obtained from a series of +experiments, which we describe in each (sub)section of the paper. In result, we +successfully derived a potential candidate solution approach for the finals of +track 3 of the ITC 2007. +","An application of the Threshold Accepting metaheuristic for curriculum + based course timetabling" +" The paper presents a study of local search heuristics in general and variable +neighborhood search in particular for the resolution of an assignment problem +studied in the practical work of universities. Here, students have to be +assigned to scientific topics which are proposed and supported by members of +staff. The problem involves the optimization under given preferences of +students which may be expressed when applying for certain topics. + It is possible to observe that variable neighborhood search leads to superior +results for the tested problem instances. One instance is taken from an actual +case, while others have been generated based on the real world data to support +the analysis with a deeper analysis. + An extension of the problem has been formulated by integrating a second +objective function that simultaneously balances the workload of the members of +staff while maximizing utility of the students. The algorithmic approach has +been prototypically implemented in a computer system. One important aspect in +this context is the application of the research work to problems of other +scientific institutions, and therefore the provision of decision support +functionalities. +","Variable Neighborhood Search for the University Lecturer-Student + Assignment Problem" +" We introduce an extended tableau calculus for answer set programming (ASP). +The proof system is based on the ASP tableaux defined in [Gebser&Schaub, ICLP +2006], with an added extension rule. We investigate the power of Extended ASP +Tableaux both theoretically and empirically. We study the relationship of +Extended ASP Tableaux with the Extended Resolution proof system defined by +Tseitin for sets of clauses, and separate Extended ASP Tableaux from ASP +Tableaux by giving a polynomial-length proof for a family of normal logic +programs P_n for which ASP Tableaux has exponential-length minimal proofs with +respect to n. Additionally, Extended ASP Tableaux imply interesting insight +into the effect of program simplification on the lengths of proofs in ASP. +Closely related to Extended ASP Tableaux, we empirically investigate the effect +of redundant rules on the efficiency of ASP solving. + To appear in Theory and Practice of Logic Programming (TPLP). +",Extended ASP tableaux and rule redundancy in normal logic programs +" In this paper, a Gaifman-Shapiro-style module architecture is tailored to the +case of Smodels programs under the stable model semantics. The composition of +Smodels program modules is suitably limited by module conditions which ensure +the compatibility of the module system with stable models. Hence the semantics +of an entire Smodels program depends directly on stable models assigned to its +modules. This result is formalized as a module theorem which truly strengthens +Lifschitz and Turner's splitting-set theorem for the class of Smodels programs. +To streamline generalizations in the future, the module theorem is first proved +for normal programs and then extended to cover Smodels programs using a +translation from the latter class of programs to the former class. Moreover, +the respective notion of module-level equivalence, namely modular equivalence, +is shown to be a proper congruence relation: it is preserved under +substitutions of modules that are modularly equivalent. Principles for program +decomposition are also addressed. The strongly connected components of the +respective dependency graph can be exploited in order to extract a module +structure when there is no explicit a priori knowledge about the modules of a +program. The paper includes a practical demonstration of tools that have been +developed for automated (de)composition of Smodels programs. + To appear in Theory and Practice of Logic Programming. +","Achieving compositionality of the stable model semantics for Smodels + programs" +" Most research related to unithood were conducted as part of a larger effort +for the determination of termhood. Consequently, novelties are rare in this +small sub-field of term extraction. In addition, existing work were mostly +empirically motivated and derived. We propose a new probabilistically-derived +measure, independent of any influences of termhood, that provides dedicated +measures to gather linguistic evidence from parsed text and statistical +evidence from Google search engine for the measurement of unithood. Our +comparative study using 1,825 test cases against an existing +empirically-derived function revealed an improvement in terms of precision, +recall and accuracy. +","Determining the Unithood of Word Sequences using a Probabilistic + Approach" +" Most works related to unithood were conducted as part of a larger effort for +the determination of termhood. Consequently, the number of independent research +that study the notion of unithood and produce dedicated techniques for +measuring unithood is extremely small. We propose a new approach, independent +of any influences of termhood, that provides dedicated measures to gather +linguistic evidence from parsed text and statistical evidence from Google +search engine for the measurement of unithood. Our evaluations revealed a +precision and recall of 98.68% and 91.82% respectively with an accuracy at +95.42% in measuring the unithood of 1005 test cases. +","Determining the Unithood of Word Sequences using Mutual Information and + Independence Measure" +" An increasing number of approaches for ontology engineering from text are +gearing towards the use of online sources such as company intranet and the +World Wide Web. Despite such rise, not much work can be found in aspects of +preprocessing and cleaning dirty texts from online sources. This paper presents +an enhancement of an Integrated Scoring for Spelling error correction, +Abbreviation expansion and Case restoration (ISSAC). ISSAC is implemented as +part of a text preprocessing phase in an ontology engineering system. New +evaluations performed on the enhanced ISSAC using 700 chat records reveal an +improved accuracy of 98% as compared to 96.5% and 71% based on the use of only +basic ISSAC and of Aspell, respectively. +",Enhanced Integrated Scoring for Cleaning Dirty Texts +" We present a domain-independent algorithm that computes macros in a novel +way. Our algorithm computes macros ""on-the-fly"" for a given set of states and +does not require previously learned or inferred information, nor prior domain +knowledge. The algorithm is used to define new domain-independent tractable +classes of classical planning that are proved to include \emph{Blocksworld-arm} +and \emph{Towers of Hanoi}. +",On-the-fly Macros +" In this study, we reproduce two new hybrid intelligent systems, involve three +prominent intelligent computing and approximate reasoning methods: Self +Organizing feature Map (SOM), Neruo-Fuzzy Inference System and Rough Set Theory +(RST),called SONFIS and SORST. We show how our algorithms can be construed as a +linkage of government-society interactions, where government catches various +states of behaviors: solid (absolute) or flexible. So, transition of society, +by changing of connectivity parameters (noise) from order to disorder is +inferred. +",Modeling of Social Transitions Using Intelligent Systems +" The paper presents the investigation and implementation of the relationship +between diversity and the performance of multiple classifiers on classification +accuracy. The study is critical as to build classifiers that are strong and can +generalize better. The parameters of the neural network within the committee +were varied to induce diversity; hence structural diversity is the focus for +this study. The hidden nodes and the activation function are the parameters +that were varied. The diversity measures that were adopted from ecology such as +Shannon and Simpson were used to quantify diversity. Genetic algorithm is used +to find the optimal ensemble by using the accuracy as the cost function. The +results observed shows that there is a relationship between structural +diversity and accuracy. It is observed that the classification accuracy of an +ensemble increases as the diversity increases. There was an increase of 3%-6% +in the classification accuracy. +","Relationship between Diversity and Perfomance of Multiple Classifiers + for Decision Support" +" The paper presents an exponential pheromone deposition rule to modify the +basic ant system algorithm which employs constant deposition rule. A stability +analysis using differential equation is carried out to find out the values of +parameters that make the ant system dynamics stable for both kinds of +deposition rule. A roadmap of connected cities is chosen as the problem +environment where the shortest route between two given cities is required to be +discovered. Simulations performed with both forms of deposition approach using +Elitist Ant System model reveal that the exponential deposition approach +outperforms the classical one by a large extent. Exhaustive experiments are +also carried out to find out the optimum setting of different controlling +parameters for exponential deposition approach and an empirical relationship +between the major controlling parameters of the algorithm and some features of +problem environment. +","Balancing Exploration and Exploitation by an Elitist Ant System with + Exponential Pheromone Deposition Rule" +" This article presents a unique design for a parser using the Ant Colony +Optimization algorithm. The paper implements the intuitive thought process of +human mind through the activities of artificial ants. The scheme presented here +uses a bottom-up approach and the parsing program can directly use ambiguous or +redundant grammars. We allocate a node corresponding to each production rule +present in the given grammar. Each node is connected to all other nodes +(representing other production rules), thereby establishing a completely +connected graph susceptible to the movement of artificial ants. Each ant tries +to modify this sentential form by the production rule present in the node and +upgrades its position until the sentential form reduces to the start symbol S. +Successful ants deposit pheromone on the links that they have traversed +through. Eventually, the optimum path is discovered by the links carrying +maximum amount of pheromone concentration. The design is simple, versatile, +robust and effective and obviates the calculation of the above mentioned sets +and precedence relation tables. Further advantages of our scheme lie in i) +ascertaining whether a given string belongs to the language represented by the +grammar, and ii) finding out the shortest possible path from the given string +to the start symbol S in case multiple routes exist. +",A Novel Parser Design Algorithm Based on Artificial Ants +" The paper presents an exponential pheromone deposition approach to improve +the performance of classical Ant System algorithm which employs uniform +deposition rule. A simplified analysis using differential equations is carried +out to study the stability of basic ant system dynamics with both exponential +and constant deposition rules. A roadmap of connected cities, where the +shortest path between two specified cities are to be found out, is taken as a +platform to compare Max-Min Ant System model (an improved and popular model of +Ant System algorithm) with exponential and constant deposition rules. Extensive +simulations are performed to find the best parameter settings for non-uniform +deposition approach and experiments with these parameter settings revealed that +the above approach outstripped the traditional one by a large extent in terms +of both solution quality and convergence time. +","Extension of Max-Min Ant System with Exponential Pheromone Deposition + Rule" +" We address here two major challenges presented by dynamic data mining: 1) the +stability challenge: we have implemented a rigorous incremental density-based +clustering algorithm, independent from any initial conditions and ordering of +the data-vectors stream, 2) the cognitive challenge: we have implemented a +stringent selection process of association rules between clusters at time t-1 +and time t for directly generating the main conclusions about the dynamics of a +data-stream. We illustrate these points with an application to a two years and +2600 documents scientific information database. +","Document stream clustering: experimenting an incremental algorithm and + AR-based tools for highlighting dynamic trends" +" Data-stream clustering is an ever-expanding subdomain of knowledge +extraction. Most of the past and present research effort aims at efficient +scaling up for the huge data repositories. Our approach focuses on qualitative +improvement, mainly for ""weak signals"" detection and precise tracking of +topical evolutions in the framework of information watch - though scalability +is intrinsically guaranteed in a possibly distributed implementation. Our +GERMEN algorithm exhaustively picks up the whole set of density peaks of the +data at time t, by identifying the local perturbations induced by the current +document vector, such as changing cluster borders, or new/vanishing clusters. +Optimality yields from the uniqueness 1) of the density landscape for any value +of our zoom parameter, 2) of the cluster allocation operated by our border +propagation rule. This results in a rigorous independence from the data +presentation ranking or any initialization parameter. We present here as a +first step the only assessment of a static view resulting from one year of the +CNRS/INIST Pascal database in the field of geotechnics. +","Classification dynamique d'un flux documentaire : une \'evaluation + statique pr\'ealable de l'algorithme GERMEN" +" This paper gives an introduction to this issue, and presents the framework +and the main steps of the Rosa project. Four teams of researchers, agronomists, +computer scientists, psychologists and linguists were involved during five +years within this project that aimed at the development of a knowledge based +system. The purpose of the Rosa system is the modelling and the comparison of +farm spatial organizations. It relies on a formalization of agronomical +knowledge and thus induces a joint knowledge building process involving both +the agronomists and the computer scientists. The paper describes the steps of +the modelling process as well as the filming procedures set up by the +psychologists and linguists in order to make explicit and to analyze the +underlying knowledge building process. +","\'Etude longitudinale d'une proc\'edure de mod\'elisation de + connaissances en mati\`ere de gestion du territoire agricole" +" Collaborative tagging systems, such as Delicious, CiteULike, and others, +allow users to annotate resources, e.g., Web pages or scientific papers, with +descriptive labels called tags. The social annotations contributed by thousands +of users, can potentially be used to infer categorical knowledge, classify +documents or recommend new relevant information. Traditional text inference +methods do not make best use of social annotation, since they do not take into +account variations in individual users' perspectives and vocabulary. In a +previous work, we introduced a simple probabilistic model that takes interests +of individual annotators into account in order to find hidden topics of +annotated resources. Unfortunately, that approach had one major shortcoming: +the number of topics and interests must be specified a priori. To address this +drawback, we extend the model to a fully Bayesian framework, which offers a way +to automatically estimate these numbers. In particular, the model allows the +number of interests and topics to change as suggested by the structure of the +data. We evaluate the proposed model in detail on the synthetic and real-world +data by comparing its performance to Latent Dirichlet Allocation on the topic +extraction task. For the latter evaluation, we apply the model to infer topics +of Web resources from social annotations obtained from Delicious in order to +discover new resources similar to a specified one. Our empirical results +demonstrate that the proposed model is a promising method for exploiting social +knowledge contained in user-generated annotations. +",Modeling Social Annotation: a Bayesian Approach +" Airport gate assignment is of great importance in airport operations. In this +paper, we study the Airport Gate Assignment Problem (AGAP), propose a new model +and implement the model with Optimization Programming language (OPL). With the +objective to minimize the number of conflicts of any two adjacent aircrafts +assigned to the same gate, we build a mathematical model with logical +constraints and the binary constraints, which can provide an efficient +evaluation criterion for the Airlines to estimate the current gate assignment. +To illustrate the feasibility of the model we construct experiments with the +data obtained from Continental Airlines, Houston Gorge Bush Intercontinental +Airport IAH, which indicate that our model is both energetic and effective. +Moreover, we interpret experimental results, which further demonstrate that our +proposed model can provide a powerful tool for airline companies to estimate +the efficiency of their current work of gate assignment. +",Airport Gate Assignment: New Model and Implementation +" This paper investigates the use of different Artificial Intelligence methods +to predict the values of several continuous variables from a Steam Generator. +The objective was to determine how the different artificial intelligence +methods performed in making predictions on the given dataset. The artificial +intelligence methods evaluated were Neural Networks, Support Vector Machines, +and Adaptive Neuro-Fuzzy Inference Systems. The types of neural networks +investigated were Multi-Layer Perceptions, and Radial Basis Function. Bayesian +and committee techniques were applied to these neural networks. Each of the AI +methods considered was simulated in Matlab. The results of the simulations +showed that all the AI methods were capable of predicting the Steam Generator +data reasonably accurately. However, the Adaptive Neuro-Fuzzy Inference system +out performed the other methods in terms of accuracy and ease of +implementation, while still achieving a fast execution time as well as a +reasonable training time. +",Artificial Intelligence Techniques for Steam Generator Modelling +" Theoretical analysis of machine intelligence (MI) is useful for defining a +common platform in both theoretical and applied artificial intelligence (AI). +The goal of this paper is to set canonical definitions that can assist +pragmatic research in both strong and weak AI. Described epistemological +features of machine intelligence include relationship between intelligent +behavior, intelligent and unintelligent machine characteristics, observable and +unobservable entities and classification of intelligence. The paper also +establishes algebraic definitions of efficiency and accuracy of MI tests as +their quality measure. The last part of the paper addresses the learning +process with respect to the traditional epistemology and the epistemology of MI +described here. The proposed views on MI positively correlate to the Hegelian +monistic epistemology and contribute towards amalgamating idealistic +deliberations with the AI theory, particularly in a local frame of reference. +",Elementary epistemological features of machine intelligence +" Answer set programming (ASP) is a logic programming paradigm that can be used +to solve complex combinatorial search problems. Aggregates are an ASP construct +that plays an important role in many applications. Defining a satisfactory +semantics of aggregates turned out to be a difficult problem, and in this paper +we propose a new approach, based on an analogy between aggregates and +propositional connectives. First, we extend the definition of an answer +set/stable model to cover arbitrary propositional theories; then we define +aggregates on top of them both as primitive constructs and as abbreviations for +formulas. Our definition of an aggregate combines expressiveness and +simplicity, and it inherits many theorems about programs with nested +expressions, such as theorems about strong equivalence and splitting. +",Logic programs with propositional connectives and aggregates +" The standard classification of emotions involves categorizing the expression +of emotions. In this paper, parameters underlying some emotions are identified +and a new classification based on these parameters is suggested. +","Identification of parameters underlying emotions and a classification of + emotions" +" Neural networks are powerful tools for classification and regression in +static environments. This paper describes a technique for creating an ensemble +of neural networks that adapts dynamically to changing conditions. The model +separates the input space into four regions and each network is given a weight +in each region based on its performance on samples from that region. The +ensemble adapts dynamically by constantly adjusting these weights based on the +current performance of the networks. The data set used is a collection of +financial indicators with the goal of predicting the future platinum price. An +ensemble with no weightings does not improve on the naive estimate of no weekly +change; our weighting algorithm gives an average percentage error of 63% for +twenty weeks of prediction. +","Prediction of Platinum Prices Using Dynamically Weighted Mixture of + Experts" +" Health Practice Guideliens are supposed to unify practices and propose +recommendations to physicians. This paper describes GemFrame, a system capable +of semi-automatically filling an XML template from free texts in the clinical +domain. The XML template includes semantic information not explicitly encoded +in the text (pairs of conditions and ac-tions/recommendations). Therefore, +there is a need to compute the exact scope of condi-tions over text sequences +expressing the re-quired actions. We present a system developped for this task. +We show that it yields good performance when applied to the analysis of French +practice guidelines. We conclude with a precise evaluation of the tool. +","Analyse et structuration automatique des guides de bonnes pratiques + cliniques : essai d'\'evaluation" +" The need for domain ontologies in mission critical applications such as risk +management and hazard identification is becoming more and more pressing. Most +research on ontology learning conducted in the academia remains unrealistic for +real-world applications. One of the main problems is the dependence on +non-incremental, rare knowledge and textual resources, and manually-crafted +patterns and rules. This paper reports work in progress aiming to address such +undesirable dependencies during ontology construction. Initial experiments +using a working prototype of the system revealed promising potentials in +automatically constructing high-quality domain ontologies using real-world +texts. +","Automatic Construction of Lightweight Domain Ontologies for Chemical + Engineering Risk Management" +" We introduce novel results for approximate inference on planar graphical +models using the loop calculus framework. The loop calculus (Chertkov and +Chernyak, 2006) allows to express the exact partition function of a graphical +model as a finite sum of terms that can be evaluated once the belief +propagation (BP) solution is known. In general, full summation over all +correction terms is intractable. We develop an algorithm for the approach +presented in (Certkov et al., 2008) which represents an efficient truncation +scheme on planar graphs and a new representation of the series in terms of +Pfaffians of matrices. We analyze the performance of the algorithm for the +partition function approximation for models with binary variables and pairwise +interactions on grids and other planar graphs. We study in detail both the loop +series and the equivalent Pfaffian series and show that the first term of the +Pfaffian series for the general, intractable planar model, can provide very +accurate approximations. The algorithm outperforms previous truncation schemes +of the loop series and is competitive with other state-of-the-art methods for +approximate inference. +","Approximate inference on planar graphs using Loop Calculus and Belief + Propagation" +" In this paper we present the N-norms/N-conorms in neutrosophic logic and set +as extensions of T-norms/T-conorms in fuzzy logic and set. Also, as an +extension of the Intuitionistic Fuzzy Topology we present the Neutrosophic +Topologies. +","N-norm and N-conorm in Neutrosophic Logic and Set, and the Neutrosophic + Topologies" +" When a considerable number of mutations have no effects on fitness values, +the fitness landscape is said neutral. In order to study the interplay between +neutrality, which exists in many real-world applications, and performances of +metaheuristics, it is useful to design landscapes which make it possible to +tune precisely neutral degree distribution. Even though many neutral landscape +models have already been designed, none of them are general enough to create +landscapes with specific neutral degree distributions. We propose three steps +to design such landscapes: first using an algorithm we construct a landscape +whose distribution roughly fits the target one, then we use a simulated +annealing heuristic to bring closer the two distributions and finally we affect +fitness values to each neutral network. Then using this new family of fitness +landscapes we are able to highlight the interplay between deceptiveness and +neutrality. +",Deceptiveness and Neutrality - the ND family of fitness landscapes +" The pharmacovigilance databases consist of several case reports involving +drugs and adverse events (AEs). Some methods are applied consistently to +highlight all signals, i.e. all statistically significant associations between +a drug and an AE. These methods are appropriate for verification of more +complex relationships involving one or several drug(s) and AE(s) (e.g; +syndromes or interactions) but do not address the identification of them. We +propose a method for the extraction of these relationships based on Formal +Concept Analysis (FCA) associated with disproportionality measures. This method +identifies all sets of drugs and AEs which are potential signals, syndromes or +interactions. Compared to a previous experience of disproportionality analysis +without FCA, the addition of FCA was more efficient for identifying false +positives related to concomitant drugs. +",Mining for adverse drug events with formal concept analysis +" Domain experts should provide relevant domain knowledge to an Intelligent +Tutoring System (ITS) so that it can guide a learner during problemsolving +learning activities. However, for many ill-defined domains, the domain +knowledge is hard to define explicitly. In previous works, we showed how +sequential pattern mining can be used to extract a partial problem space from +logged user interactions, and how it can support tutoring services during +problem-solving exercises. This article describes an extension of this approach +to extract a problem space that is richer and more adapted for supporting +tutoring services. We combined sequential pattern mining with (1) dimensional +pattern mining (2) time intervals, (3) the automatic clustering of valued +actions and (4) closed sequences mining. Some tutoring services have been +implemented and an experiment has been conducted in a tutoring system. +","A Knowledge Discovery Framework for Learning Task Models from User + Interactions in Intelligent Tutoring Systems" +" In this paper we propose the CTS (Concious Tutoring System) technology, a +biologically plausible cognitive agent based on human brain functions.This +agent is capable of learning and remembering events and any related information +such as corresponding procedures, stimuli and their emotional valences. Our +proposed episodic memory and episodic learning mechanism are closer to the +current multiple-trace theory in neuroscience, because they are inspired by it +[5] contrary to other mechanisms that are incorporated in cognitive agents. +This is because in our model emotions play a role in the encoding and +remembering of events. This allows the agent to improve its behavior by +remembering previously selected behaviors which are influenced by its emotional +mechanism. Moreover, the architecture incorporates a realistic memory +consolidation process based on a data mining algorithm. +",How Emotional Mechanism Helps Episodic Learning in a Cognitive Agent +" Consumers of mass media must have a comprehensive, balanced and plural +selection of news to get an unbiased perspective; but achieving this goal can +be very challenging, laborious and time consuming. News stories development +over time, its (in)consistency, and different level of coverage across the +media outlets are challenges that a conscientious reader has to overcome in +order to alleviate bias. + In this paper we present an intelligent agent framework currently +facilitating analysis of the main sources of on-line news in El Salvador. We +show how prior tools of text analysis and Web 2.0 technologies can be combined +with minimal manual intervention to help individuals on their rational decision +process, while holding media outlets accountable for their work. +",Alleviating Media Bias Through Intelligent Agent Blogging +" We study the logic of comparative concept similarity $\CSL$ introduced by +Sheremet, Tishkovsky, Wolter and Zakharyaschev to capture a form of qualitative +similarity comparison. In this logic we can formulate assertions of the form "" +objects A are more similar to B than to C"". The semantics of this logic is +defined by structures equipped by distance functions evaluating the similarity +degree of objects. We consider here the particular case of the semantics +induced by \emph{minspaces}, the latter being distance spaces where the minimum +of a set of distances always exists. It turns out that the semantics over +arbitrary minspaces can be equivalently specified in terms of preferential +structures, typical of conditional logics. We first give a direct +axiomatisation of this logic over Minspaces. We next define a decision +procedure in the form of a tableaux calculus. Both the calculus and the +axiomatisation take advantage of the reformulation of the semantics in terms of +preferential structures. +","Comparative concept similarity over Minspaces: Axiomatisation and + Tableaux Calculus" +" Data mining algorithms are now able to efficiently deal with huge amount of +data. Various kinds of patterns may be discovered and may have some great +impact on the general development of knowledge. In many domains, end users may +want to have their data mined by data mining tools in order to extract patterns +that could impact their business. Nevertheless, those users are often +overwhelmed by the large quantity of patterns extracted in such a situation. +Moreover, some privacy issues, or some commercial one may lead the users not to +be able to mine the data by themselves. Thus, the users may not have the +possibility to perform many experiments integrating various constraints in +order to focus on specific patterns they would like to extract. Post processing +of patterns may be an answer to that drawback. Thus, in this paper we present a +framework that could allow end users to manage collections of patterns. We +propose to use an efficient data structure on which some algebraic operators +may be used in order to retrieve or access patterns in pattern bases. +",A Model for Managing Collections of Patterns +" Empirical evidence suggests that hashing is an effective strategy for +dimensionality reduction and practical nonparametric estimation. In this paper +we provide exponential tail bounds for feature hashing and show that the +interaction between random subspaces is negligible with high probability. We +demonstrate the feasibility of this approach with experimental results for a +new use case -- multitask learning with hundreds of thousands of tasks. +",Feature Hashing for Large Scale Multitask Learning +" We propose a new extended format to represent constraint networks using XML. +This format allows us to represent constraints defined either in extension or +in intension. It also allows us to reference global constraints. Any instance +of the problems CSP (Constraint Satisfaction Problem), QCSP (Quantified CSP) +and WCSP (Weighted CSP) can be represented using this format. +",XML Representation of Constraint Networks: Format XCSP 2.1 +" The present work consisted in developing a plateau game. There are the +traditional ones (monopoly, cluedo, ect.) but those which interest us leave +less place at the chance (luck) than to the strategy such that the chess game. +Kallah is an old African game, its rules are simple but the strategies to be +used are very complex to implement. Of course, they are based on a strongly +mathematical basis as in the film ""Rain-Man"" where one can see that gambling +can be payed with strategies based on mathematical theories. The Artificial +Intelligence gives the possibility ""of thinking"" to a machine and, therefore, +allows it to make decisions. In our work, we use it to give the means to the +computer choosing its best movement. +",The Semantics of Kalah Game +" 1-Nearest Neighbor with the Dynamic Time Warping (DTW) distance is one of the +most effective classifiers on time series domain. Since the global constraint +has been introduced in speech community, many global constraint models have +been proposed including Sakoe-Chiba (S-C) band, Itakura Parallelogram, and +Ratanamahatana-Keogh (R-K) band. The R-K band is a general global constraint +model that can represent any global constraints with arbitrary shape and size +effectively. However, we need a good learning algorithm to discover the most +suitable set of R-K bands, and the current R-K band learning algorithm still +suffers from an 'overfitting' phenomenon. In this paper, we propose two new +learning algorithms, i.e., band boundary extraction algorithm and iterative +learning algorithm. The band boundary extraction is calculated from the bound +of all possible warping paths in each class, and the iterative learning is +adjusted from the original R-K band learning. We also use a Silhouette index, a +well-known clustering validation technique, as a heuristic function, and the +lower bound function, LB_Keogh, to enhance the prediction speed. Twenty +datasets, from the Workshop and Challenge on Time Series Classification, held +in conjunction of the SIGKDD 2007, are used to evaluate our approach. +",Learning DTW Global Constraint for Time Series Classification +" We propose Range and Roots which are two common patterns useful for +specifying a wide range of counting and occurrence constraints. We design +specialised propagation algorithms for these two patterns. Counting and +occurrence constraints specified using these patterns thus directly inherit a +propagation algorithm. To illustrate the capabilities of the Range and Roots +constraints, we specify a number of global constraints taken from the +literature. Preliminary experiments demonstrate that propagating counting and +occurrence constraints using these two patterns leads to a small loss in +performance when compared to specialised global constraints and is competitive +with alternative decompositions using elementary constraints. +","Range and Roots: Two Common Patterns for Specifying and Propagating + Counting and Occurrence Constraints" +" The management and combination of uncertain, imprecise, fuzzy and even +paradoxical or high conflicting sources of information has always been, and +still remains today, of primal importance for the development of reliable +modern information systems involving artificial reasoning. In this +introduction, we present a survey of our recent theory of plausible and +paradoxical reasoning, known as Dezert-Smarandache Theory (DSmT), developed for +dealing with imprecise, uncertain and conflicting sources of information. We +focus our presentation on the foundations of DSmT and on its most important +rules of combination, rather than on browsing specific applications of DSmT +available in literature. Several simple examples are given throughout this +presentation to show the efficiency and the generality of this new approach. +",An introduction to DSmT +" When mathematicians present proofs they usually adapt their explanations to +their didactic goals and to the (assumed) knowledge of their addressees. Modern +automated theorem provers, in contrast, present proofs usually at a fixed level +of detail (also called granularity). Often these presentations are neither +intended nor suitable for human use. A challenge therefore is to develop user- +and goal-adaptive proof presentation techniques that obey common mathematical +practice. We present a flexible and adaptive approach to proof presentation +that exploits machine learning techniques to extract a model of the specific +granularity of proof examples and employs this model for the automated +generation of further proofs at an adapted level of granularity. +",Granularity-Adaptive Proof Presentation +" Symmetry is an important factor in solving many constraint satisfaction +problems. One common type of symmetry is when we have symmetric values. In a +recent series of papers, we have studied methods to break value symmetries. Our +results identify computational limits on eliminating value symmetry. For +instance, we prove that pruning all symmetric values is NP-hard in general. +Nevertheless, experiments show that much value symmetry can be broken in +practice. These results may be useful to researchers in planning, scheduling +and other areas as value symmetry occurs in many different domains. +",Breaking Value Symmetry +" An attractive mechanism to specify global constraints in rostering and other +domains is via formal languages. For instance, the Regular and Grammar +constraints specify constraints in terms of the languages accepted by an +automaton and a context-free grammar respectively. Taking advantage of the +fixed length of the constraint, we give an algorithm to transform a +context-free grammar into an automaton. We then study the use of minimization +techniques to reduce the size of such automata and speed up propagation. We +show that minimizing such automata after they have been unfolded and domains +initially reduced can give automata that are more compact than minimizing +before unfolding and reducing. Experimental results show that such +transformations can improve the size of rostering problems that we can 'model +and run'. +",Reformulating Global Grammar Constraints +" We propose a new family of constraints which combine together lexicographical +ordering constraints for symmetry breaking with other common global +constraints. We give a general purpose propagator for this family of +constraints, and show how to improve its complexity by exploiting properties of +the included global constraints. +",Combining Symmetry Breaking and Global Constraints +" We present an online method for estimating the cost of solving SAT problems. +Modern SAT solvers present several challenges to estimate search cost including +non-chronological backtracking, learning and restarts. Our method uses a linear +model trained on data gathered at the start of search. We show the +effectiveness of this method using random and structured problems. We +demonstrate that predictions made in early restarts can be used to improve +later predictions. We also show that we can use such cost estimations to select +a solver from a portfolio. +",Online Estimation of SAT Solving Runtime +" In this work, we deal with the question of modeling programming exercises for +novices pointing to an e-learning scenario. Our purpose is to identify basic +requirements, raise some key questions and propose potential answers from a +conceptual perspective. Presented as a general picture, we hypothetically +situate our work in a general context where e-learning instructional material +needs to be adapted to form part of an introductory Computer Science (CS) +e-learning course at the CS1-level. Meant is a potential course which aims at +improving novices skills and knowledge on the essentials of programming by +using e-learning based approaches in connection (at least conceptually) with a +general host framework like Activemath (www.activemath.org). Our elaboration +covers contextual and, particularly, cognitive elements preparing the terrain +for eventual research stages in a derived project, as indicated. We concentrate +our main efforts on reasoning mechanisms about exercise complexity that can +eventually offer tool support for the task of exercise authoring. We base our +requirements analysis on our own perception of the exercise subsystem provided +by Activemath especially within the domain reasoner area. We enrich the +analysis by bringing to the discussion several relevant contextual elements +from the CS1 courses, its definition and implementation. Concerning cognitive +models and exercises, we build upon the principles of Bloom's Taxonomy as a +relatively standardized basis and use them as a framework for study and +analysis of complexity in basic programming exercises. Our analysis includes +requirements for the domain reasoner which are necessary for the exercise +analysis. We propose for such a purpose a three-layered conceptual model +considering exercise evaluation, programming and metaprogramming. +",On Requirements for Programming Exercises from an E-learning Perspective +" Successful management of emotional stimuli is a pivotal issue concerning +Affective Computing (AC) and the related research. As a subfield of Artificial +Intelligence, AC is concerned not only with the design of computer systems and +the accompanying hardware that can recognize, interpret, and process human +emotions, but also with the development of systems that can trigger human +emotional response in an ordered and controlled manner. This requires the +maximum attainable precision and efficiency in the extraction of data from +emotionally annotated databases While these databases do use keywords or tags +for description of the semantic content, they do not provide either the +necessary flexibility or leverage needed to efficiently extract the pertinent +emotional content. Therefore, to this extent we propose an introduction of +ontologies as a new paradigm for description of emotionally annotated data. The +ability to select and sequence data based on their semantic attributes is vital +for any study involving metadata, semantics and ontological sorting like the +Semantic Web or the Social Semantic Desktop, and the approach described in the +paper facilitates reuse in these areas as well. +",Tagging multimedia stimuli with ontologies +" To model combinatorial decision problems involving uncertainty and +probability, we introduce scenario based stochastic constraint programming. +Stochastic constraint programs contain both decision variables, which we can +set, and stochastic variables, which follow a discrete probability +distribution. We provide a semantics for stochastic constraint programs based +on scenario trees. Using this semantics, we can compile stochastic constraint +programs down into conventional (non-stochastic) constraint programs. This +allows us to exploit the full power of existing constraint solvers. We have +implemented this framework for decision making under uncertainty in stochastic +OPL, a language which is based on the OPL constraint modelling language +[Hentenryck et al., 1999]. To illustrate the potential of this framework, we +model a wide range of problems in areas as diverse as portfolio +diversification, agricultural planning and production/inventory management. +",Stochastic Constraint Programming: A Scenario-Based Approach +" To model combinatorial decision problems involving uncertainty and +probability, we introduce stochastic constraint programming. Stochastic +constraint programs contain both decision variables (which we can set) and +stochastic variables (which follow a probability distribution). They combine +together the best features of traditional constraint satisfaction, stochastic +integer programming, and stochastic satisfiability. We give a semantics for +stochastic constraint programs, and propose a number of complete algorithms and +approximation procedures. Finally, we discuss a number of extensions of +stochastic constraint programming to relax various assumptions like the +independence between stochastic variables, and compare with other approaches +for decision making under uncertainty. +",Stochastic Constraint Programming +" Often user interfaces of theorem proving systems focus on assisting +particularly trained and skilled users, i.e., proof experts. As a result, the +systems are difficult to use for non-expert users. This paper describes a paper +and pencil HCI experiment, in which (non-expert) students were asked to make +suggestions for a GUI for an interactive system for mathematical proofs. They +had to explain the usage of the GUI by applying it to construct a proof sketch +for a given theorem. The evaluation of the experiment provides insights for the +interaction design for non-expert users and the needs and wants of this user +group. +",Designing a GUI for Proofs - Evaluation of an HCI Experiment +" The Ouroboros Model is a new conceptual proposal for an algorithmic structure +for efficient data processing in living beings as well as for artificial +agents. Its central feature is a general repetitive loop where one iteration +cycle sets the stage for the next. Sensory input activates data structures +(schemata) with similar constituents encountered before, thus expectations are +kindled. This corresponds to the highlighting of empty slots in the selected +schema, and these expectations are compared with the actually encountered +input. Depending on the outcome of this consumption analysis different next +steps like search for further data or a reset, i.e. a new attempt employing +another schema, are triggered. Monitoring of the whole process, and in +particular of the flow of activation directed by the consumption analysis, +yields valuable feedback for the optimum allocation of attention and resources +including the selective establishment of useful new memory entries. +",Flow of Activity in the Ouroboros Model +" In a certain number of situations, human cognitive functioning is difficult +to represent with classical artificial intelligence structures. Such a +difficulty arises in the polyneuropathy diagnosis which is based on the spatial +distribution, along the nerve fibres, of lesions, together with the synthesis +of several partial diagnoses. Faced with this problem while building up an +expert system (NEUROP), we developed a heterogeneous knowledge representation +associating a finite automaton with first order logic. A number of knowledge +representation problems raised by the electromyography test features are +examined in this study and the expert system architecture allowing such a +knowledge modeling are laid out. +","Heterogeneous knowledge representation using a finite automaton and + first order logic: a case study in electromyography" +" In this paper a new dynamic subsumption technique for Boolean CNF formulae is +proposed. It exploits simple and sufficient conditions to detect during +conflict analysis, clauses from the original formula that can be reduced by +subsumption. During the learnt clause derivation, and at each step of the +resolution process, we simply check for backward subsumption between the +current resolvent and clauses from the original formula and encoded in the +implication graph. Our approach give rise to a strong and dynamic +simplification technique that exploits learning to eliminate literals from the +original clauses. Experimental results show that the integration of our dynamic +subsumption approach within the state-of-the-art SAT solvers Minisat and Rsat +achieves interesting improvements particularly on crafted instances. +",Learning for Dynamic subsumption +" Today, science have a powerful tool for the description of reality - the +numbers. However, the concept of number was not immediately, lets try to trace +the evolution of the concept. The numbers emerged as the need for accurate +estimates of the amount in order to permit a comparison of some objects. So if +you see to it how many times a day a person uses the numbers and compare, it +becomes evident that the comparison is used much more frequently. However, the +comparison is not possible without two opposite basic standards. Thus, to +introduce the concept of comparison, must have two opposing standards, in turn, +the operation of comparison is necessary to introduce the concept of number. +Arguably, the scientific description of reality is impossible without the +concept of opposites. + In this paper analyzes the concept of opposites, as the basis for the +introduction of the principle of development. +",Principle of development +" Social Network Analysis (SNA) tries to understand and exploit the key +features of social networks in order to manage their life cycle and predict +their evolution. Increasingly popular web 2.0 sites are forming huge social +network. Classical methods from social network analysis (SNA) have been applied +to such online networks. In this paper, we propose leveraging semantic web +technologies to merge and exploit the best features of each domain. We present +how to facilitate and enhance the analysis of online social networks, +exploiting the power of semantic social network analysis. +",Semantic Social Network Analysis +" We describe a variant of resolution rule of proof and show that it is +complete for stable semantics of logic programs. We show applications of this +result. +",Guarded resolution for answer set programming +" A fuzzy mnesor space is a semimodule over the positive real numbers. It can +be used as theoretical framework for fuzzy sets. Hence we can prove a great +number of properties for fuzzy sets without refering to the membership +functions. +",Fuzzy Mnesors +" We apply proof-theoretic techniques in answer Set Programming. The main +results include: 1. A characterization of continuity properties of +Gelfond-Lifschitz operator for logic program. 2. A propositional +characterization of stable models of logic programs (without referring to loop +formulas. +",An Application of Proof-Theory in Answer Set Programming +" We show that some common and important global constraints like ALL-DIFFERENT +and GCC can be decomposed into simple arithmetic constraints on which we +achieve bound or range consistency, and in some cases even greater pruning. +These decompositions can be easily added to new solvers. They also provide +other constraints with access to the state of the propagator by sharing of +variables. Such sharing can be used to improve propagation between constraints. +We report experiments with our decomposition in a pseudo-Boolean solver. +","Decompositions of All Different, Global Cardinality and Related + Constraints" +" To model combinatorial decision problems involving uncertainty and +probability, we extend the stochastic constraint programming framework proposed +in [Walsh, 2002] along a number of important dimensions (e.g. to multiple +chance constraints and to a range of new objectives). We also provide a new +(but equivalent) semantics based on scenarios. Using this semantics, we can +compile stochastic constraint programs down into conventional (nonstochastic) +constraint programs. This allows us to exploit the full power of existing +constraint solvers. We have implemented this framework for decision making +under uncertainty in stochastic OPL, a language which is based on the OPL +constraint modelling language [Hentenryck et al., 1999]. To illustrate the +potential of this framework, we model a wide range of problems in areas as +diverse as finance, agriculture and production. +",Scenario-based Stochastic Constraint Programming +" Many real life optimization problems contain both hard and soft constraints, +as well as qualitative conditional preferences. However, there is no single +formalism to specify all three kinds of information. We therefore propose a +framework, based on both CP-nets and soft constraints, that handles both hard +and soft constraints as well as conditional preferences efficiently and +uniformly. We study the complexity of testing the consistency of preference +statements, and show how soft constraints can faithfully approximate the +semantics of conditional preference statements whilst improving the +computational complexity +","Reasoning about soft constraints and conditional preferences: complexity + results and approximation techniques" +" We identify a new and important global (or non-binary) constraint. This +constraint ensures that the values taken by two vectors of variables, when +viewed as multisets, are ordered. This constraint is useful for a number of +different applications including breaking symmetry and fuzzy constraint +satisfaction. We propose and implement an efficient linear time algorithm for +enforcing generalised arc consistency on such a multiset ordering constraint. +Experimental results on several problem domains show considerable promise. +",Multiset Ordering Constraints +" We relate tag clouds to other forms of visualization, including planar or +reduced dimensionality mapping, and Kohonen self-organizing maps. Using a +modified tag cloud visualization, we incorporate other information into it, +including text sequence and most pertinent words. Our notion of word pertinence +goes beyond just word frequency and instead takes a word in a mathematical +sense as located at the average of all of its pairwise relationships. We +capture semantics through context, taken as all pairwise relationships. Our +domain of application is that of filmscript analysis. The analysis of +filmscripts, always important for cinema, is experiencing a major gain in +importance in the context of television. Our objective in this work is to +visualize the semantics of filmscript, and beyond filmscript any other +partially structured, time-ordered, sequence of text segments. In particular we +develop an innovative approach to plot characterization. +",Tag Clouds for Displaying Semantics: The Case of Filmscripts +" The paper proposes an analysis on some existent ontologies, in order to point +out ways to resolve semantic heterogeneity in information systems. Authors are +highlighting the tasks in a Knowledge Acquisiton System and identifying aspects +related to the addition of new information to an intelligent system. A solution +is proposed, as a combination of ontology reasoning services and natural +languages generation. A multi-agent system will be conceived with an extractor +agent, a reasoner agent and a competence management agent. +",Considerations on Construction Ontologies +" We have been developing a system for recognising human activity given a +symbolic representation of video content. The input of our system is a set of +time-stamped short-term activities detected on video frames. The output of our +system is a set of recognised long-term activities, which are pre-defined +temporal combinations of short-term activities. The constraints on the +short-term activities that, if satisfied, lead to the recognition of a +long-term activity, are expressed using a dialect of the Event Calculus. We +illustrate the expressiveness of the dialect by showing the representation of +several typical complex activities. Furthermore, we present a detailed +evaluation of the system through experimentation on a benchmark dataset of +surveillance videos. +",A Logic Programming Approach to Activity Recognition +" People have to make important decisions within a time frame. Hence, it is +imperative to employ means or strategy to aid effective decision making. +Consequently, Economic Intelligence (EI) has emerged as a field to aid +strategic and timely decision making in an organization. In the course of +attaining this goal: it is indispensable to be more optimistic towards +provision for conservation of intellectual resource invested into the process +of decision making. This intellectual resource is nothing else but the +knowledge of the actors as well as that of the various processes for effecting +decision making. Knowledge has been recognized as a strategic economic resource +for enhancing productivity and a key for innovation in any organization or +community. Thus, its adequate management with cognizance of its temporal +properties is highly indispensable. Temporal properties of knowledge refer to +the date and time (known as timestamp) such knowledge is created as well as the +duration or interval between related knowledge. This paper focuses on the needs +for a user-centered knowledge management approach as well as exploitation of +associated temporal properties. Our perspective of knowledge is with respect to +decision-problems projects in EI. Our hypothesis is that the possibility of +reasoning about temporal properties in exploitation of knowledge in EI projects +should foster timely decision making through generation of useful inferences +from available and reusable knowledge for a new project. +","Knowledge Management in Economic Intelligence with Reasoning on Temporal + Attributes" +" I discuss (ontologies_and_ontological_knowledge_bases / +formal_methods_and_theories) duality and its category theory extensions as a +step toward a solution to Knowledge-Based Systems Theory. In particular I focus +on the example of the design of elements of ontologies and ontological +knowledge bases of next three electronic courses: Foundations of Research +Activities, Virtual Modeling of Complex Systems and Introduction to String +Theory. +",Toward a Category Theory Design of Ontological Knowledge Bases +" Mnesors are defined as elements of a semimodule over the min-plus integers. +This two-sorted structure is able to merge graduation properties of vectors and +idempotent properties of boolean numbers, which makes it appropriate for hybrid +systems. We apply it to the control of an inverted pendulum and design a full +logical controller, that is, without the usual algebra of real numbers. +",Mnesors for automatic control +" We consider the following sequential decision problem. Given a set of items +of unknown utility, we need to select one of as high a utility as possible +(``the selection problem''). Measurements (possibly noisy) of item values prior +to selection are allowed, at a known cost. The goal is to optimize the overall +sequential decision process of measurements and selection. + Value of information (VOI) is a well-known scheme for selecting measurements, +but the intractability of the problem typically leads to using myopic VOI +estimates. In the selection problem, myopic VOI frequently badly underestimates +the value of information, leading to inferior sensing plans. We relax the +strict myopic assumption into a scheme we term semi-myopic, providing a +spectrum of methods that can improve the performance of sensing plans. In +particular, we propose the efficiently computable method of ``blinkered'' VOI, +and examine theoretical bounds for special cases. Empirical evaluation of +``blinkered'' VOI in the selection problem with normally distributed item +values shows that is performs much better than pure myopic VOI. +",Semi-Myopic Sensing Plans for Value Optimization +" There are several well-known justifications for conditioning as the +appropriate method for updating a single probability measure, given an +observation. However, there is a significant body of work arguing for sets of +probability measures, rather than single measures, as a more realistic model of +uncertainty. Conditioning still makes sense in this context--we can simply +condition each measure in the set individually, then combine the results--and, +indeed, it seems to be the preferred updating procedure in the literature. But +how justified is conditioning in this richer setting? Here we show, by +considering an axiomatic account of conditioning given by van Fraassen, that +the single-measure and sets-of-measures cases are very different. We show that +van Fraassen's axiomatization for the former case is nowhere near sufficient +for updating sets of measures. We give a considerably longer (and not as +compelling) list of axioms that together force conditioning in this setting, +and describe other update methods that are allowed once any of these axioms is +dropped. +",Updating Sets of Probabilities +" This paper presents a novel two-stage flexible dynamic decision support based +optimal threat evaluation and defensive resource scheduling algorithm for +multi-target air-borne threats. The algorithm provides flexibility and +optimality by swapping between two objective functions, i.e. the preferential +and subtractive defense strategies as and when required. To further enhance the +solution quality, it outlines and divides the critical parameters used in +Threat Evaluation and Weapon Assignment (TEWA) into three broad categories +(Triggering, Scheduling and Ranking parameters). Proposed algorithm uses a +variant of many-to-many Stable Marriage Algorithm (SMA) to solve Threat +Evaluation (TE) and Weapon Assignment (WA) problem. In TE stage, Threat Ranking +and Threat-Asset pairing is done. Stage two is based on a new flexible dynamic +weapon scheduling algorithm, allowing multiple engagements using +shoot-look-shoot strategy, to compute near-optimal solution for a range of +scenarios. Analysis part of this paper presents the strengths and weaknesses of +the proposed algorithm over an alternative greedy algorithm as applied to +different offline scenarios. +","A Novel Two-Stage Dynamic Decision Support based Optimal Threat + Evaluation and Defensive Resource Scheduling Algorithm for Multi Air-borne + threats" +" Martin and Osswald \cite{Martin07} have recently proposed many +generalizations of combination rules on quantitative beliefs in order to manage +the conflict and to consider the specificity of the responses of the experts. +Since the experts express themselves usually in natural language with +linguistic labels, Smarandache and Dezert \cite{Li07} have introduced a +mathematical framework for dealing directly also with qualitative beliefs. In +this paper we recall some element of our previous works and propose the new +combination rules, developed for the fusion of both qualitative or quantitative +beliefs. +",General combination rules for qualitative and quantitative beliefs +" Surveillance control and reporting (SCR) system for air threats play an +important role in the defense of a country. SCR system corresponds to air and +ground situation management/processing along with information fusion, +communication, coordination, simulation and other critical defense oriented +tasks. Threat Evaluation and Weapon Assignment (TEWA) sits at the core of SCR +system. In such a system, maximal or near maximal utilization of constrained +resources is of extreme importance. Manual TEWA systems cannot provide +optimality because of different limitations e.g.surface to air missile (SAM) +can fire from a distance of 5Km, but manual TEWA systems are constrained by +human vision range and other constraints. Current TEWA systems usually work on +target-by-target basis using some type of greedy algorithm thus affecting the +optimality of the solution and failing in multi-target scenario. his paper +relates to a novel two-staged flexible dynamic decision support based optimal +threat evaluation and weapon assignment algorithm for multi-target air-borne +threats. +","A Novel Two-Staged Decision Support based Threat Evaluation and Weapon + Assignment Algorithm, Asset-based Dynamic Weapon Scheduling using Artificial + Intelligence Techinques" +" Collective graphical models exploit inter-instance associative dependence to +output more accurate labelings. However existing models support very limited +kind of associativity which restricts accuracy gains. This paper makes two +major contributions. First, we propose a general collective inference framework +that biases data instances to agree on a set of {\em properties} of their +labelings. Agreement is encouraged through symmetric clique potentials. We show +that rich properties leads to bigger gains, and present a systematic inference +procedure for a large class of such properties. The procedure performs message +passing on the cluster graph, where property-aware messages are computed with +cluster specific algorithms. This provides an inference-only solution for +domain adaptation. Our experiments on bibliographic information extraction +illustrate significant test error reduction over unseen domains. Our second +major contribution consists of algorithms for computing outgoing messages from +clique clusters with symmetric clique potentials. Our algorithms are exact for +arbitrary symmetric potentials on binary labels and for max-like and +majority-like potentials on multiple labels. For majority potentials, we also +provide an efficient Lagrangian Relaxation based algorithm that compares +favorably with the exact algorithm. We present a 13/15-approximation algorithm +for the NP-hard Potts potential, with runtime sub-quadratic in the clique size. +In contrast, the best known previous guarantee for graphs with Potts potentials +is only 1/2. We empirically show that our method for Potts potentials is an +order of magnitude faster than the best alternatives, and our Lagrangian +Relaxation based algorithm for majority potentials beats the best applicable +heuristic -- ICM. +",Generalized Collective Inference with Symmetric Clique Potentials +" This research report presents an extension of Cumulative of Choco constraint +solver, which is useful to encode over-constrained cumulative problems. This +new global constraint uses sweep and task interval violation-based algorithms. +",The Soft Cumulative Constraint +" In this paper, we propose a first-order ontology for generalized stratified +order structure. We then classify the models of the theory using +model-theoretic techniques. An ontology mapping from this ontology to the core +theory of Process Specification Language is also discussed. +",Modelling Concurrent Behaviors in the Process Specification Language +" The article presents a study of rather simple local search heuristics for the +single machine total weighted tardiness problem (SMTWTP), namely hillclimbing +and Variable Neighborhood Search. In particular, we revisit these approaches +for the SMTWTP as there appears to be a lack of appropriate/challenging +benchmark instances in this case. The obtained results are impressive indeed. +Only few instances remain unsolved, and even those are approximated within 1% +of the optimal/best known solutions. Our experiments support the claim that +metaheuristics for the SMTWTP are very likely to lead to good results, and +that, before refining search strategies, more work must be done with regard to +the proposition of benchmark data. Some recommendations for the construction of +such data sets are derived from our investigations. +","The Single Machine Total Weighted Tardiness Problem - Is it (for + Metaheuristics) a Solved Problem ?" +" The article describes the proposition and application of a local search +metaheuristic for multi-objective optimization problems. It is based on two +main principles of heuristic search, intensification through variable +neighborhoods, and diversification through perturbations and successive +iterations in favorable regions of the search space. The concept is +successfully tested on permutation flow shop scheduling problems under multiple +objectives and compared to other local search approaches. While the obtained +results are encouraging in terms of their quality, another positive attribute +of the approach is its simplicity as it does require the setting of only very +few parameters. +","Improvements for multi-objective flow shop scheduling by Pareto Iterated + Local Search" +" This paper discusses ""computational"" systems capable of ""computing"" functions +not computable by predefined Turing machines if the systems are not isolated +from their environment. Roughly speaking, these systems can change their finite +descriptions by interacting with their environment. +",Beyond Turing Machines +" I propose that pattern recognition, memorization and processing are key +concepts that can be a principle set for the theoretical modeling of the mind +function. Most of the questions about the mind functioning can be answered by a +descriptive modeling and definitions from these principles. An understandable +consciousness definition can be drawn based on the assumption that a pattern +recognition system can recognize its own patterns of activity. The principles, +descriptive modeling and definitions can be a basis for theoretical and applied +research on cognitive sciences, particularly at artificial intelligence +studies. +",Pattern Recognition Theory of Mind +" The report gives an overview about activities on the topic Semantic Web. It +has been released as technical report for the project ""KTweb -- Connecting +Knowledge Technologies Communities"" in 2003. +",Fact Sheet on Semantic Web +" Restart strategies are an important factor in the performance of +conflict-driven Davis Putnam style SAT solvers. Selecting a good restart +strategy for a problem instance can enhance the performance of a solver. +Inspired by recent success applying machine learning techniques to predict the +runtime of SAT solvers, we present a method which uses machine learning to +boost solver performance through a smart selection of the restart strategy. +Based on easy to compute features, we train both a satisfiability classifier +and runtime models. We use these models to choose between restart strategies. +We present experimental results comparing this technique with the most commonly +used restart strategies. Our results demonstrate that machine learning is +effective in improving solver performance. +",Restart Strategy Selection using Machine Learning Techniques +" We present two different methods for estimating the cost of solving SAT +problems. The methods focus on the online behaviour of the backtracking solver, +as well as the structure of the problem. Modern SAT solvers present several +challenges to estimate search cost including coping with nonchronological +backtracking, learning and restarts. Our first method adapt an existing +algorithm for estimating the size of a search tree to deal with these +challenges. We then suggest a second method that uses a linear model trained on +data gathered online at the start of search. We compare the effectiveness of +these two methods using random and structured problems. We also demonstrate +that predictions made in early restarts can be used to improve later +predictions. We conclude by showing that the cost of solving a set of problems +can be reduced by selecting a solver from a portfolio based on such cost +estimations. +",Online Search Cost Estimation for SAT Solvers +" Classification is the basis of cognition. Unlike other solutions, this study +approaches it from the view of outliers. We present an expanding algorithm to +detect outliers in univariate datasets, together with the underlying +foundation. The expanding algorithm runs in a holistic way, making it a rather +robust solution. Synthetic and real data experiments show its power. +Furthermore, an application for multi-class problems leads to the introduction +of the oscillator algorithm. The corresponding result implies the potential +wide use of the expanding algorithm. +",On Classification from Outlier View +" We consider a sequence of repeated interactions between an agent and an +environment. Uncertainty about the environment is captured by a probability +distribution over a space of hypotheses, which includes all computable +functions. Given a utility function, we can evaluate the expected utility of +any computational policy for interaction with the environment. After making +some plausible assumptions (and maybe one not-so-plausible assumption), we show +that if the utility function is unbounded, then the expected utility of any +policy is undefined. +",Convergence of Expected Utility for Universal AI +" This study describes application of some approximate reasoning methods to +analysis of hydrocyclone performance. In this manner, using a combining of Self +Organizing Map (SOM), Neuro-Fuzzy Inference System (NFIS)-SONFIS- and Rough Set +Theory (RST)-SORST-crisp and fuzzy granules are obtained. Balancing of crisp +granules and non-crisp granules can be implemented in close-open iteration. +Using different criteria and based on granulation level balance point +(interval) or a pseudo-balance point is estimated. Validation of the proposed +methods, on the data set of the hydrocyclone is rendered. +",Knowledge Discovery of Hydrocyclone s Circuit Based on SONFIS and SORST +" In this paper we introduce two new DSm fusion conditioning rules with +example, and as a generalization of them a class of DSm fusion conditioning +rules, and then extend them to a class of DSm conditioning rules. +",A Class of DSm Conditional Rules +" When implementing a propagator for a constraint, one must decide about +variants: When implementing min, should one also implement max? Should one +implement linear constraints both with unit and non-unit coefficients? +Constraint variants are ubiquitous: implementing them requires considerable (if +not prohibitive) effort and decreases maintainability, but will deliver better +performance than resorting to constraint decomposition. + This paper shows how to use views to derive perfect propagator variants. A +model for views and derived propagators is introduced. Derived propagators are +proved to be indeed perfect in that they inherit essential properties such as +correctness and domain and bounds consistency. Techniques for systematically +deriving propagators such as transformation, generalization, specialization, +and type conversion are developed. The paper introduces an implementation +architecture for views that is independent of the underlying constraint +programming system. A detailed evaluation of views implemented in Gecode shows +that derived propagators are efficient and that views often incur no overhead. +Without views, Gecode would either require 180 000 rather than 40 000 lines of +propagator code, or would lack many efficient propagator variants. Compared to +8 000 lines of code for views, the reduction in code for propagators yields a +1750% return on investment. +",View-based Propagator Derivation +" The explorative mind-map is a dynamic framework, that emerges automatically +from the input, it gets. It is unlike a verificative modeling system where +existing (human) thoughts are placed and connected together. In this regard, +explorative mind-maps change their size continuously, being adaptive with +connectionist cells inside; mind-maps process data input incrementally and +offer lots of possibilities to interact with the user through an appropriate +communication interface. With respect to a cognitive motivated situation like a +conversation between partners, mind-maps become interesting as they are able to +process stimulating signals whenever they occur. If these signals are close to +an own understanding of the world, then the conversational partner becomes +automatically more trustful than if the signals do not or less match the own +knowledge scheme. In this (position) paper, we therefore motivate explorative +mind-maps as a cognitive engine and propose these as a decision support engine +to foster trust. +",A Cognitive Mind-map Framework to Foster Trust +" To capture the uncertainty of information or knowledge in information +systems, various information granulations, also known as knowledge +granulations, have been proposed. Recently, several axiomatic definitions of +information granulation have been introduced. In this paper, we try to improve +these axiomatic definitions and give a universal construction of information +granulation by relating information granulations with a class of functions of +multiple variables. We show that the improved axiomatic definition has some +concrete information granulations in the literature as instances. +",An improved axiomatic definition of information granulation +" Current research on qualitative spatial representation and reasoning mainly +focuses on one single aspect of space. In real world applications, however, +multiple spatial aspects are often involved simultaneously. + This paper investigates problems arising in reasoning with combined +topological and directional information. We use the RCC8 algebra and the +Rectangle Algebra (RA) for expressing topological and directional information +respectively. We give examples to show that the bipath-consistency algorithm +BIPATH is incomplete for solving even basic RCC8 and RA constraints. If +topological constraints are taken from some maximal tractable subclasses of +RCC8, and directional constraints are taken from a subalgebra, termed DIR49, of +RA, then we show that BIPATH is able to separate topological constraints from +directional ones. This means, given a set of hybrid topological and directional +constraints from the above subclasses of RCC8 and RA, we can transfer the joint +satisfaction problem in polynomial time to two independent satisfaction +problems in RCC8 and RA. For general RA constraints, we give a method to +compute solutions that satisfy all topological constraints and approximately +satisfy each RA constraint to any prescribed precision. +",Reasoning with Topological and Directional Spatial Information +" Direction relations between extended spatial objects are important +commonsense knowledge. Recently, Goyal and Egenhofer proposed a formal model, +known as Cardinal Direction Calculus (CDC), for representing direction +relations between connected plane regions. CDC is perhaps the most expressive +qualitative calculus for directional information, and has attracted increasing +interest from areas such as artificial intelligence, geographical information +science, and image retrieval. Given a network of CDC constraints, the +consistency problem is deciding if the network is realizable by connected +regions in the real plane. This paper provides a cubic algorithm for checking +consistency of basic CDC constraint networks, and proves that reasoning with +CDC is in general an NP-Complete problem. For a consistent network of basic CDC +constraints, our algorithm also returns a 'canonical' solution in cubic time. +This cubic algorithm is also adapted to cope with cardinal directions between +possibly disconnected regions, in which case currently the best algorithm is of +time complexity O(n^5). +",Reasoning about Cardinal Directions between Extended Objects +" In this paper, we address the problem of generating preferred plans by +combining the procedural control knowledge specified by Hierarchical Task +Networks (HTNs) with rich qualitative user preferences. The outcome of our work +is a language for specifyin user preferences, tailored to HTN planning, +together with a provably optimal preference-based planner, HTNPLAN, that is +implemented as an extension of SHOP2. To compute preferred plans, we propose an +approach based on forward-chaining heuristic search. Our heuristic uses an +admissible evaluation function measuring the satisfaction of preferences over +partial plans. Our empirical evaluation demonstrates the effectiveness of our +HTNPLAN heuristics. We prove our approach sound and optimal with respect to the +plans it generates by appealing to a situation calculus semantics of our +preference language and of HTN planning. While our implementation builds on +SHOP2, the language and techniques proposed here are relevant to a broad range +of HTN planners. +",On Planning with Preferences in HTN +" We study the notion of informedness in a client-consultant setting. Using a +software simulator, we examine the extent to which it pays off for consultants +to provide their clients with advice that is well-informed, or with advice that +is merely meant to appear to be well-informed. The latter strategy is +beneficial in that it costs less resources to keep up-to-date, but carries the +risk of a decreased reputation if the clients discover the low level of +informedness of the consultant. Our experimental results indicate that under +different circumstances, different strategies yield the optimal results (net +profit) for the consultants. +",Assessing the Impact of Informedness on a Consultant's Profit +" We describe in this article a multiagent urban traffic simulation, as we +believe individual-based modeling is necessary to encompass the complex +influence the actions of an individual vehicle can have on the overall flow of +vehicles. We first describe how we build a graph description of the network +from purely geometric data, ESRI shapefiles. We then explain how we include +traffic related data to this graph. We go on after that with the model of the +vehicle agents: origin and destination, driving behavior, multiple lanes, +crossroads, and interactions with the other vehicles in day-to-day, ?ordinary? +traffic. We conclude with the presentation of the resulting simulation of this +model on the Rouen agglomeration. +",A multiagent urban traffic simulation Part I: dealing with the ordinary +" 2007 was the first international congress on the ?square of oppositions?. A +first attempt to structure debate using n-opposition theory was presented along +with the results of a first experiment on the web. Our proposal for this paper +is to define relations between arguments through a structure of opposition +(square of oppositions is one structure of opposition). We will be trying to +answer the following questions: How to organize debates on the web 2.0? How to +structure them in a logical way? What is the role of n-opposition theory, in +this context? We present in this paper results of three experiments +(Betapolitique 2007, ECAP 2008, Intermed 2008). +",n-Opposition theory to structure debates +" We propose Interactive Differential Evolution (IDE) based on paired +comparisons for reducing user fatigue and evaluate its convergence speed in +comparison with Interactive Genetic Algorithms (IGA) and tournament IGA. User +interface and convergence performance are two big keys for reducing Interactive +Evolutionary Computation (IEC) user fatigue. Unlike IGA and conventional IDE, +users of the proposed IDE and tournament IGA do not need to compare whole +individuals each other but compare pairs of individuals, which largely +decreases user fatigue. In this paper, we design a pseudo-IEC user and evaluate +another factor, IEC convergence performance, using IEC simulators and show that +our proposed IDE converges significantly faster than IGA and tournament IGA, +i.e. our proposed one is superior to others from both user interface and +convergence performance points of view. +",Paired Comparisons-based Interactive Differential Evolution +" This paper describes application of information granulation theory, on the +back analysis of Jeffrey mine southeast wall Quebec. In this manner, using a +combining of Self Organizing Map (SOM) and rough set theory (RST), crisp and +rough granules are obtained. Balancing of crisp granules and sub rough granules +is rendered in close-open iteration. Combining of hard and soft computing, +namely finite difference method (FDM) and computational intelligence and taking +in to account missing information are two main benefits of the proposed method. +As a practical example, reverse analysis on the failure of the southeast wall +Jeffrey mine is accomplished. +",Back analysis based on SOM-RST system +" Fault diagnosis has become a very important area of research during the last +decade due to the advancement of mechanical and electrical systems in +industries. The automobile is a crucial field where fault diagnosis is given a +special attention. Due to the increasing complexity and newly added features in +vehicles, a comprehensive study has to be performed in order to achieve an +appropriate diagnosis model. A diagnosis system is capable of identifying the +faults of a system by investigating the observable effects (or symptoms). The +system categorizes the fault into a diagnosis class and identifies a probable +cause based on the supplied fault symptoms. Fault categorization and +identification are done using similarity matching techniques. The development +of diagnosis classes is done by making use of previous experience, knowledge or +information within an application area. The necessary information used may come +from several sources of knowledge, such as from system analysis. In this paper +similarity matching techniques for fault diagnosis in automotive infotainment +applications are discussed. +","Similarity Matching Techniques for Fault Diagnosis in Automotive + Infotainment Electronics" +" Diverse recommendation techniques have been already proposed and encapsulated +into several e-business applications, aiming to perform a more accurate +evaluation of the existing information and accordingly augment the assistance +provided to the users involved. This paper reports on the development and +integration of a recommendation module in an agent-based transportation +transactions management system. The module is built according to a novel hybrid +recommendation technique, which combines the advantages of collaborative +filtering and knowledge-based approaches. The proposed technique and supporting +module assist customers in considering in detail alternative transportation +transactions that satisfy their requests, as well as in evaluating completed +transactions. The related services are invoked through a software agent that +constructs the appropriate knowledge rules and performs a synthesis of the +recommendation policy. +","Performing Hybrid Recommendation in Intermodal Transportation-the + FTMarket System's Recommendation Module" +" We study decompositions of NVALUE, a global constraint that can be used to +model a wide range of problems where values need to be counted. Whilst +decomposition typically hinders propagation, we identify one decomposition that +maintains a global view as enforcing bound consistency on the decomposition +achieves bound consistency on the original global NVALUE constraint. Such +decompositions offer the prospect for advanced solving techniques like nogood +learning and impact based branching heuristics. They may also help SAT and IP +solvers take advantage of the propagation of global constraints. +",Decomposition of the NVALUE constraint +" Symmetry is an important feature of many constraint programs. We show that +any symmetry acting on a set of symmetry breaking constraints can be used to +break symmetry. Different symmetries pick out different solutions in each +symmetry class. We use these observations in two methods for eliminating +symmetry from a problem. These methods are designed to have many of the +advantages of symmetry breaking methods that post static symmetry breaking +constraint without some of the disadvantages. In particular, the two methods +prune the search space using fast and efficient propagation of posted +constraints, whilst reducing the conflict between symmetry breaking and +branching heuristics. Experimental results show that the two methods perform +well on some standard benchmarks. +",Symmetries of Symmetry Breaking Constraints +" Fuzzy constraints are a popular approach to handle preferences and +over-constrained problems in scenarios where one needs to be cautious, such as +in medical or space applications. We consider here fuzzy constraint problems +where some of the preferences may be missing. This models, for example, +settings where agents are distributed and have privacy issues, or where there +is an ongoing preference elicitation process. In this setting, we study how to +find a solution which is optimal irrespective of the missing preferences. In +the process of finding such a solution, we may elicit preferences from the user +if necessary. However, our goal is to ask the user as little as possible. We +define a combined solving and preference elicitation scheme with a large number +of different instantiations, each corresponding to a concrete algorithm which +we compare experimentally. We compute both the number of elicited preferences +and the ""user effort"", which may be larger, as it contains all the preference +values the user has to compute to be able to respond to the elicitation +requests. While the number of elicited preferences is important when the +concern is to communicate as little information as possible, the user effort +measures also the hidden work the user has to do to be able to communicate the +elicited preferences. Our experimental results show that some of our algorithms +are very good at finding a necessarily optimal solution while asking the user +for only a very small fraction of the missing preferences. The user effort is +also very small for the best algorithms. Finally, we test these algorithms on +hard constraint problems with possibly missing constraints, where the aim is to +find feasible solutions irrespective of the missing constraints. +","Elicitation strategies for fuzzy constraint problems with missing + preferences: algorithms and experimental studies" +" We propose new filtering algorithms for the SEQUENCE constraint and some +extensions of the SEQUENCE constraint based on network flows. We enforce domain +consistency on the SEQUENCE constraint in $O(n^2)$ time down a branch of the +search tree. This improves upon the best existing domain consistency algorithm +by a factor of $O(\log n)$. The flows used in these algorithms are derived from +a linear program. Some of them differ from the flows used to propagate global +constraints like GCC since the domains of the variables are encoded as costs on +the edges rather than capacities. Such flows are efficient for maintaining +bounds consistency over large domains and may be useful for other global +constraints. +",Flow-Based Propagators for the SEQUENCE and Related Global Constraints +" We introduce the weighted CFG constraint and propose a propagation algorithm +that enforces domain consistency in $O(n^3|G|)$ time. We show that this +algorithm can be decomposed into a set of primitive arithmetic constraints +without hindering propagation. +",The Weighted CFG Constraint +" Many models in natural and social sciences are comprised of sets of +inter-acting entities whose intensity of interaction decreases with distance. +This often leads to structures of interest in these models composed of dense +packs of entities. Fast Multipole Methods are a family of methods developed to +help with the calculation of a number of computable models such as described +above. We propose a method that builds upon FMM to detect and model the dense +structures of these systems. +",Building upon Fast Multipole Methods to Detect and Model Organizations +" In Probabilistic Risk Management, risk is characterized by two quantities: +the magnitude (or severity) of the adverse consequences that can potentially +result from the given activity or action, and by the likelihood of occurrence +of the given adverse consequences. But a risk seldom exists in isolation: chain +of consequences must be examined, as the outcome of one risk can increase the +likelihood of other risks. Systemic theory must complement classic PRM. Indeed +these chains are composed of many different elements, all of which may have a +critical importance at many different levels. Furthermore, when urban +catastrophes are envisioned, space and time constraints are key determinants of +the workings and dynamics of these chains of catastrophes: models must include +a correct spatial topology of the studied risk. Finally, literature insists on +the importance small events can have on the risk on a greater scale: urban +risks management models belong to self-organized criticality theory. We chose +multiagent systems to incorporate this property in our model: the behavior of +an agent can transform the dynamics of important groups of them. +","A multiagent urban traffic simulation. Part II: dealing with the + extraordinary" +" Constrained Optimum Path (COP) problems appear in many real-life +applications, especially on communication networks. Some of these problems have +been considered and solved by specific techniques which are usually difficult +to extend. In this paper, we introduce a novel local search modeling for +solving some COPs by local search. The modeling features the compositionality, +modularity, reuse and strengthens the benefits of Constrained-Based Local +Search. We also apply the modeling to the edge-disjoint paths problem (EDP). We +show that side constraints can easily be added in the model. Computational +results show the significance of the approach. +","A Local Search Modeling for Constrained Optimum Paths Problems (Extended + Abstract)" +" Using constraint-based local search, we effectively model and efficiently +solve the problem of balancing the traffic demands on portions of the European +airspace while ensuring that their capacity constraints are satisfied. The +traffic demand of a portion of airspace is the hourly number of flights planned +to enter it, and its capacity is the upper bound on this number under which +air-traffic controllers can work. Currently, the only form of demand-capacity +balancing we allow is ground holding, that is the changing of the take-off +times of not yet airborne flights. Experiments with projected European flight +plans of the year 2030 show that already this first form of demand-capacity +balancing is feasible without incurring too much total delay and that it can +lead to a significantly better demand-capacity balance. +","Dynamic Demand-Capacity Balancing for Air Traffic Management Using + Constraint-Based Local Search: First Results" +" Stochastic local search (SLS) has been an active field of research in the +last few years, with new techniques and procedures being developed at an +astonishing rate. SLS has been traditionally associated with satisfiability +solving, that is, finding a solution for a given problem instance, as its +intrinsic nature does not address unsatisfiable problems. Unsatisfiable +instances were therefore commonly solved using backtrack search solvers. For +this reason, in the late 90s Selman, Kautz and McAllester proposed a challenge +to use local search instead to prove unsatisfiability. More recently, two SLS +solvers - Ranger and Gunsat - have been developed, which are able to prove +unsatisfiability albeit being SLS solvers. In this paper, we first compare +Ranger with Gunsat and then propose to improve Ranger performance using some of +Gunsat's techniques, namely unit propagation look-ahead and extended +resolution. +",On Improving Local Search for Unsatisfiability +" This article introduces SatHyS (SAT HYbrid Solver), a novel hybrid approach +for propositional satisfiability. It combines local search and conflict driven +clause learning (CDCL) scheme. Each time the local search part reaches a local +minimum, the CDCL is launched. For SAT problems it behaves like a tabu list, +whereas for UNSAT ones, the CDCL part tries to focus on minimum unsatisfiable +sub-formula (MUS). Experimental results show good performances on many classes +of SAT instances from the last SAT competitions. +",Integrating Conflict Driven Clause Learning to Local Search +" In this paper, we investigate the hybridization of constraint programming and +local search techniques within a large neighbourhood search scheme for solving +highly constrained nurse rostering problems. As identified by the research, a +crucial part of the large neighbourhood search is the selection of the fragment +(neighbourhood, i.e. the set of variables), to be relaxed and re-optimized +iteratively. The success of the large neighbourhood search depends on the +adequacy of this identified neighbourhood with regard to the problematic part +of the solution assignment and the choice of the neighbourhood size. We +investigate three strategies to choose the fragment of different sizes within +the large neighbourhood search scheme. The first two strategies are tailored +concerning the problem properties. The third strategy is more general, using +the information of the cost from the soft constraint violations and their +propagation as the indicator to choose the variables added into the fragment. +The three strategies are analyzed and compared upon a benchmark nurse rostering +problem. Promising results demonstrate the possibility of future work in the +hybrid approach. +",A Constraint-directed Local Search Approach to Nurse Rostering Problems +" This paper presents a new method and a constraint-based objective function to +solve two problems related to the design of optical telecommunication networks, +namely the Synchronous Optical Network Ring Assignment Problem (SRAP) and the +Intra-ring Synchronous Optical Network Design Problem (IDP). These network +topology problems can be represented as a graph partitioning with capacity +constraints as shown in previous works. We present here a new objective +function and a new local search algorithm to solve these problems. Experiments +conducted in Comet allow us to compare our method to previous ones and show +that we obtain better results. +",Sonet Network Design Problems +" We explore the use of the Cell Broadband Engine (Cell/BE for short) for +combinatorial optimization applications: we present a parallel version of a +constraint-based local search algorithm that has been implemented on a +multiprocessor BladeCenter machine with twin Cell/BE processors (total of 16 +SPUs per blade). This algorithm was chosen because it fits very well the +Cell/BE architecture and requires neither shared memory nor communication +between processors, while retaining a compact memory footprint. We study the +performance on several large optimization benchmarks and show that this +achieves mostly linear time speedups, even sometimes super-linear. This is +possible because the parallel implementation might explore simultaneously +different parts of the search space and therefore converge faster towards the +best sub-space and thus towards a solution. Besides getting speedups, the +resulting times exhibit a much smaller variance, which benefits applications +where a timely reply is critical. +","Parallel local search for solving Constraint Problems on the Cell + Broadband Engine (Preliminary Results)" +" We explore the idea of using finite automata to implement new constraints for +local search (this is already a successful technique in constraint-based global +search). We show how it is possible to maintain incrementally the violations of +a constraint and its decision variables from an automaton that describes a +ground checker for that constraint. We establish the practicality of our +approach idea on real-life personnel rostering problems, and show that it is +competitive with the approach of [Pralong, 2007]. +",Toward an automaton Constraint for Local Search +" LSCS is a satellite workshop of the international conference on principles +and practice of Constraint Programming (CP), since 2004. It is devoted to local +search techniques in constraint satisfaction, and focuses on all aspects of +local search techniques, including: design and implementation of new +algorithms, hybrid stochastic-systematic search, reactive search optimization, +adaptive search, modeling for local-search, global constraints, flexibility and +robustness, learning methods, and specific applications. +","Proceedings 6th International Workshop on Local Search Techniques in + Constraint Satisfaction" +" In this paper the behavior of three combinational rules for +temporal/sequential attribute data fusion for target type estimation are +analyzed. The comparative analysis is based on: Dempster's fusion rule proposed +in Dempster-Shafer Theory; Proportional Conflict Redistribution rule no. 5 +(PCR5), proposed in Dezert-Smarandache Theory and one alternative class fusion +rule, connecting the combination rules for information fusion with particular +fuzzy operators, focusing on the t-norm based Conjunctive rule as an analog of +the ordinary conjunctive rule and t-conorm based Disjunctive rule as an analog +of the ordinary disjunctive rule. The way how different t-conorms and t-norms +functions within TCN fusion rule influence over target type estimation +performance is studied and estimated. +",Tracking object's type changes with fuzzy based fusion rule +" This paper proposes the application of particle swarm optimization (PSO) to +the problem of finite element model (FEM) selection. This problem arises when a +choice of the best model for a system has to be made from set of competing +models, each developed a priori from engineering judgment. PSO is a +population-based stochastic search algorithm inspired by the behaviour of +biological entities in nature when they are foraging for resources. Each +potentially correct model is represented as a particle that exhibits both +individualistic and group behaviour. Each particle moves within the model +search space looking for the best solution by updating the parameters values +that define it. The most important step in the particle swarm algorithm is the +method of representing models which should take into account the number, +location and variables of parameters to be updated. One example structural +system is used to show the applicability of PSO in finding an optimal FEM. An +optimal model is defined as the model that has the least number of updated +parameters and has the smallest parameter variable variation from the mean +material properties. Two different objective functions are used to compare +performance of the PSO algorithm. +",Finite element model selection using Particle Swarm Optimization +" Motivated by Zadeh's paradigm of computing with words rather than numbers, +several formal models of computing with words have recently been proposed. +These models are based on automata and thus are not well-suited for concurrent +computing. In this paper, we incorporate the well-known model of concurrent +computing, Petri nets, together with fuzzy set theory and thereby establish a +concurrency model of computing with words--fuzzy Petri nets for computing with +words (FPNCWs). The new feature of such fuzzy Petri nets is that the labels of +transitions are some special words modeled by fuzzy sets. By employing the +methodology of fuzzy reasoning, we give a faithful extension of an FPNCW which +makes it possible for computing with more words. The language expressiveness of +the two formal models of computing with words, fuzzy automata for computing +with words and FPNCWs, is compared as well. A few small examples are provided +to illustrate the theoretical development. +",A Fuzzy Petri Nets Model for Computing With Words +" Modelling emotion has become a challenge nowadays. Therefore, several models +have been produced in order to express human emotional activity. However, only +a few of them are currently able to express the close relationship existing +between emotion and cognition. An appraisal-coping model is presented here, +with the aim to simulate the emotional impact caused by the evaluation of a +particular situation (appraisal), along with the consequent cognitive reaction +intended to face the situation (coping). This model is applied to the +""Cascades"" problem, a small arithmetical exercise designed for ten-year-old +pupils. The goal is to create a model corresponding to a child's behaviour when +solving the problem using his own strategies. +","Emotion: Appraisal-coping model for the ""Cascades"" problem" +" Modeling emotion has become a challenge nowadays. Therefore, several models +have been produced in order to express human emotional activity. However, only +a few of them are currently able to express the close relationship existing +between emotion and cognition. An appraisal-coping model is presented here, +with the aim to simulate the emotional impact caused by the evaluation of a +particular situation (appraisal), along with the consequent cognitive reaction +intended to face the situation (coping). This model is applied to the +?Cascades? problem, a small arithmetical exercise designed for ten-year-old +pupils. The goal is to create a model corresponding to a child's behavior when +solving the problem using his own strategies. +",Emotion : mod\`ele d'appraisal-coping pour le probl\`eme des Cascades +" Rough set theory, a mathematical tool to deal with inexact or uncertain +knowledge in information systems, has originally described the indiscernibility +of elements by equivalence relations. Covering rough sets are a natural +extension of classical rough sets by relaxing the partitions arising from +equivalence relations to coverings. Recently, some topological concepts such as +neighborhood have been applied to covering rough sets. In this paper, we +further investigate the covering rough sets based on neighborhoods by +approximation operations. We show that the upper approximation based on +neighborhoods can be defined equivalently without using neighborhoods. To +analyze the coverings themselves, we introduce unary and composition operations +on coverings. A notion of homomorphismis provided to relate two covering +approximation spaces. We also examine the properties of approximations +preserved by the operations and homomorphisms, respectively. +","Covering rough sets based on neighborhoods: An approach without using + neighborhoods" +" In Pawlak's rough set theory, a set is approximated by a pair of lower and +upper approximations. To measure numerically the roughness of an approximation, +Pawlak introduced a quantitative measure of roughness by using the ratio of the +cardinalities of the lower and upper approximations. Although the roughness +measure is effective, it has the drawback of not being strictly monotonic with +respect to the standard ordering on partitions. Recently, some improvements +have been made by taking into account the granularity of partitions. In this +paper, we approach the roughness measure in an axiomatic way. After +axiomatically defining roughness measure and partition measure, we provide a +unified construction of roughness measure, called strong Pawlak roughness +measure, and then explore the properties of this measure. We show that the +improved roughness measures in the literature are special instances of our +strong Pawlak roughness measure and introduce three more strong Pawlak +roughness measures as well. The advantage of our axiomatic approach is that +some properties of a roughness measure follow immediately as soon as the +measure satisfies the relevant axiomatic definition. +",An axiomatic approach to the roughness measure of rough sets +" Adaptation has long been considered as the Achilles' heel of case-based +reasoning since it requires some domain-specific knowledge that is difficult to +acquire. In this paper, two strategies are combined in order to reduce the +knowledge engineering cost induced by the adaptation knowledge (CA) acquisition +task: CA is learned from the case base by the means of knowledge discovery +techniques, and the CA acquisition sessions are opportunistically triggered, +i.e., at problem-solving time. +",Opportunistic Adaptation Knowledge Discovery +" Real-time heuristic search algorithms are suitable for situated agents that +need to make their decisions in constant time. Since the original work by Korf +nearly two decades ago, numerous extensions have been suggested. One of the +most intriguing extensions is the idea of backtracking wherein the agent +decides to return to a previously visited state as opposed to moving forward +greedily. This idea has been empirically shown to have a significant impact on +various performance measures. The studies have been carried out in particular +empirical testbeds with specific real-time search algorithms that use +backtracking. Consequently, the extent to which the trends observed are +characteristic of backtracking in general is unclear. In this paper, we present +the first entirely theoretical study of backtracking in real-time heuristic +search. In particular, we present upper bounds on the solution cost exponential +and linear in a parameter regulating the amount of backtracking. The results +hold for a wide class of real-time heuristic search algorithms that includes +many existing algorithms as a small subclass. +",On Backtracking in Real-time Heuristic Search +" This paper presents several novel generalization bounds for the problem of +learning kernels based on the analysis of the Rademacher complexity of the +corresponding hypothesis sets. Our bound for learning kernels with a convex +combination of p base kernels has only a log(p) dependency on the number of +kernels, p, which is considerably more favorable than the previous best bound +given for the same problem. We also give a novel bound for learning with a +linear combination of p base kernels with an L_2 regularization whose +dependency on p is only in p^{1/4}. +",New Generalization Bounds for Learning Kernels +" This paper formulates a necessary and sufficient condition for a generic +graph matching problem to be equivalent to the maximum vertex and edge weight +clique problem in a derived association graph. The consequences of this results +are threefold: first, the condition is general enough to cover a broad range of +practical graph matching problems; second, a proof to establish equivalence +between graph matching and clique search reduces to showing that a given graph +matching problem satisfies the proposed condition; and third, the result sets +the scene for generic continuous solutions for a broad range of graph matching +problems. To illustrate the mathematical framework, we apply it to a number of +graph matching problems, including the problem of determining the graph edit +distance. +","A Necessary and Sufficient Condition for Graph Matching Being Equivalent + to the Maximum Weight Clique Problem" +" This paper extends k-means algorithms from the Euclidean domain to the domain +of graphs. To recompute the centroids, we apply subgradient methods for solving +the optimization-based formulation of the sample mean of graphs. To accelerate +the k-means algorithm for graphs without trading computational time against +solution quality, we avoid unnecessary graph distance calculations by +exploiting the triangle inequality of the underlying distance metric following +Elkan's k-means algorithm proposed in \cite{Elkan03}. In experiments we show +that the accelerated k-means algorithm are faster than the standard k-means +algorithm for graphs provided there is a cluster structure in the data. +",Elkan's k-Means for Graphs +" Traditional staging is based on a formal approach of similarity leaning on +dramaturgical ontologies and instanciation variations. Inspired by interactive +data mining, that suggests different approaches, we give an overview of +computer science and theater researches using computers as partners of the +actor to escape the a priori specification of roles. +","Similarit\'e en intension vs en extension : \`a la crois\'ee de + l'informatique et du th\'e\^atre" +" We address the problem of belief change in (nonmonotonic) logic programming +under answer set semantics. Unlike previous approaches to belief change in +logic programming, our formal techniques are analogous to those of +distance-based belief revision in propositional logic. In developing our +results, we build upon the model theory of logic programs furnished by SE +models. Since SE models provide a formal, monotonic characterisation of logic +programs, we can adapt techniques from the area of belief revision to belief +change in logic programs. We introduce methods for revising and merging logic +programs, respectively. For the former, we study both subset-based revision as +well as cardinality-based revision, and we show that they satisfy the majority +of the AGM postulates for revision. For merging, we consider operators +following arbitration merging and IC merging, respectively. We also present +encodings for computing the revision as well as the merging of logic programs +within the same logic programming framework, giving rise to a direct +implementation of our approach in terms of off-the-shelf answer set solvers. +These encodings reflect in turn the fact that our change operators do not +increase the complexity of the base formalism. +",A general approach to belief change in answer set programming +" Nearly 15 years ago, a set of qualitative spatial relations between oriented +straight line segments (dipoles) was suggested by Schlieder. This work received +substantial interest amongst the qualitative spatial reasoning community. +However, it turned out to be difficult to establish a sound constraint calculus +based on these relations. In this paper, we present the results of a new +investigation into dipole constraint calculi which uses algebraic methods to +derive sound results on the composition of relations and other properties of +dipole calculi. Our results are based on a condensed semantics of the dipole +relations. + In contrast to the points that are normally used, dipoles are extended and +have an intrinsic direction. Both features are important properties of natural +objects. This allows for a straightforward representation of prototypical +reasoning tasks for spatial agents. As an example, we show how to generate +survey knowledge from local observations in a street network. The example +illustrates the fast constraint-based reasoning capabilities of the dipole +calculus. We integrate our results into two reasoning tools which are publicly +available. +","Oriented Straight Line Segment Algebra: Qualitative Spatial Reasoning + about Oriented Objects" +" In this paper we give a thorough presentation of a model proposed by Tononi +et al. for modeling \emph{integrated information}, i.e. how much information is +generated in a system transitioning from one state to the next one by the +causal interaction of its parts and \emph{above and beyond} the information +given by the sum of its parts. We also provides a more general formulation of +such a model, independent from the time chosen for the analysis and from the +uniformity of the probability distribution at the initial time instant. +Finally, we prove that integrated information is null for disconnected systems. +",On a Model for Integrated Information +" Vector quantization(VQ) is a lossy data compression technique from signal +processing, which is restricted to feature vectors and therefore inapplicable +for combinatorial structures. This contribution presents a theoretical +foundation of graph quantization (GQ) that extends VQ to the domain of +attributed graphs. We present the necessary Lloyd-Max conditions for optimality +of a graph quantizer and consistency results for optimal GQ design based on +empirical distortion measures and stochastic optimization. These results +statistically justify existing clustering algorithms in the domain of graphs. +The proposed approach provides a template of how to link structural pattern +recognition methods other than GQ to statistical pattern recognition. +",Graph Quantization +" Human decisional processes result from the employment of selected quantities +of relevant information, generally synthesized from environmental incoming data +and stored memories. Their main goal is the production of an appropriate and +adaptive response to a cognitive or behavioral task. Different strategies of +response production can be adopted, among which haphazard trials, formation of +mental schemes and heuristics. In this paper, we propose a model of Boolean +neural network that incorporates these strategies by recurring to global +optimization strategies during the learning session. The model characterizes as +well the passage from an unstructured/chaotic attractor neural network typical +of data-driven processes to a faster one, forward-only and representative of +schema-driven processes. Moreover, a simplified version of the Iowa Gambling +Task (IGT) is introduced in order to test the model. Our results match with +experimental data and point out some relevant knowledge coming from +psychological domain. +","Decisional Processes with Boolean Neural Network: the Emergence of + Mental Schemes" +" Internet and expert systems have offered new ways of sharing and distributing +knowledge, but there is a lack of researches in the area of web based expert +systems. This paper introduces a development of a web-based expert system for +the regulations of civil service in the Kingdom of Saudi Arabia named as RCSES. +It is the first time to develop such system (application of civil service +regulations) as well the development of it using web based approach. The +proposed system considers 17 regulations of the civil service system. The +different phases of developing the RCSES system are presented, as knowledge +acquiring and selection, ontology and knowledge representations using XML +format. XML Rule-based knowledge sources and the inference mechanisms were +implemented using ASP.net technique. An interactive tool for entering the +ontology and knowledge base, and the inferencing was built. It gives the +ability to use, modify, update, and extend the existing knowledge base in an +easy way. The knowledge was validated by experts in the domain of civil service +regulations, and the proposed RCSES was tested, verified, and validated by +different technical users and the developers staff. The RCSES system is +compared with other related web based expert systems, that comparison proved +the goodness, usability, and high performance of RCSES. +",Web-Based Expert System for Civil Service Regulations: RCSES +" Many engineering optimization problems can be considered as linear +programming problems where all or some of the parameters involved are +linguistic in nature. These can only be quantified using fuzzy sets. The aim of +this paper is to solve a fuzzy linear programming problem in which the +parameters involved are fuzzy quantities with logistic membership functions. To +explore the applicability of the method a numerical example is considered to +determine the monthly production planning quotas and profit of a home textile +group. +","Application of a Fuzzy Programming Technique to Production Planning in + the Textile Industry" +" Mamdani Fuzzy Model is an important technique in Computational Intelligence +(CI) study. This paper presents an implementation of a supervised learning +method based on membership function training in the context of Mamdani fuzzy +models. Specifically, auto zoom function of a digital camera is modelled using +Mamdani technique. The performance of control method is verified through a +series of simulation and numerical results are provided as illustrations. +","The Application of Mamdani Fuzzy Model for Auto Zoom Function of a + Digital Camera" +" In this book we introduce a new procedure called \alpha-Discounting Method +for Multi-Criteria Decision Making (\alpha-D MCDM), which is as an alternative +and extension of Saaty Analytical Hierarchy Process (AHP). It works for any +number of preferences that can be transformed into a system of homogeneous +linear equations. A degree of consistency (and implicitly a degree of +inconsistency) of a decision-making problem are defined. \alpha-D MCDM is +afterwards generalized to a set of preferences that can be transformed into a +system of linear and or non-linear homogeneous and or non-homogeneous equations +and or inequalities. The general idea of \alpha-D MCDM is to assign non-null +positive parameters \alpha_1, \alpha_2, and so on \alpha_p to the coefficients +in the right-hand side of each preference that diminish or increase them in +order to transform the above linear homogeneous system of equations which has +only the null-solution, into a system having a particular non-null solution. +After finding the general solution of this system, the principles used to +assign particular values to all parameters \alpha is the second important part +of \alpha-D, yet to be deeper investigated in the future. In the current book +we propose the Fairness Principle, i.e. each coefficient should be discounted +with the same percentage (we think this is fair: not making any favoritism or +unfairness to any coefficient), but the reader can propose other principles. +For consistent decision-making problems with pairwise comparisons, +\alpha-Discounting Method together with the Fairness Principle give the same +result as AHP. But for weak inconsistent decision-making problem, +\alpha-Discounting together with the Fairness Principle give a different result +from AHP. Many consistent, weak inconsistent, and strong inconsistent examples +are given in this book. +",$\alpha$-Discounting Multi-Criteria Decision Making ($\alpha$-D MCDM) +" This paper proposes a design for a system to generate constraint solvers that +are specialised for specific problem models. It describes the design in detail +and gives preliminary experimental results showing the feasibility and +effectiveness of the approach. +",Dominion -- A constraint solver generator +" Machine Consciousness is the study of consciousness in a biological, +philosophical, mathematical and physical perspective and designing a model that +can fit into a programmable system architecture. Prime objective of the study +is to make the system architecture behave consciously like a biological model +does. Present work has developed a feasible definition of consciousness, that +characterizes consciousness with four parameters i.e., parasitic, symbiotic, +self referral and reproduction. Present work has also developed a biologically +inspired consciousness architecture that has following layers: quantum layer, +cellular layer, organ layer and behavioral layer and traced the characteristics +of consciousness at each layer. Finally, the work has estimated physical and +algorithmic architecture to devise a system that can behave consciously. +","Logical Evaluation of Consciousness: For Incorporating Consciousness + into Machine Architecture" +" To study the communication between information systems, Wang et al. [C. Wang, +C. Wu, D. Chen, Q. Hu, and C. Wu, Communicating between information systems, +Information Sciences 178 (2008) 3228-3239] proposed two concepts of type-1 and +type-2 consistent functions. Some properties of such functions and induced +relation mappings have been investigated there. In this paper, we provide an +improvement of the aforementioned work by disclosing the symmetric relationship +between type-1 and type-2 consistent functions. We present more properties of +consistent functions and induced relation mappings and improve upon several +deficient assertions in the original work. In particular, we unify and extend +type-1 and type-2 consistent functions into the so-called +neighborhood-consistent functions. This provides a convenient means for +studying the communication between information systems based on various +neighborhoods. +",Some improved results on communication between information systems +" Recently, Wang et al. discussed the properties of fuzzy information systems +under homomorphisms in the paper [C. Wang, D. Chen, L. Zhu, Homomorphisms +between fuzzy information systems, Applied Mathematics Letters 22 (2009) +1045-1050], where homomorphisms are based upon the concepts of consistent +functions and fuzzy relation mappings. In this paper, we classify consistent +functions as predecessor-consistent and successor-consistent, and then proceed +to present more properties of consistent functions. In addition, we improve +some characterizations of fuzzy relation mappings provided by Wang et al. +",Homomorphisms between fuzzy information systems revisited +" In this paper, research on AI based modeling technique to optimize +development of new alloys with necessitated improvements in properties and +chemical mixture over existing alloys as per functional requirements of product +is done. The current research work novels AI in lieu of predictions to +establish association between material and product customary. Advanced +computational simulation techniques like CFD, FEA interrogations are made +viable to authenticate product dynamics in context to experimental +investigations. Accordingly, the current research is focused towards binding +relationships between material design and product design domains. The input to +feed forward back propagation prediction network model constitutes of material +design features. Parameters relevant to product design strategies are furnished +as target outputs. The outcomes of ANN shows good sign of correlation between +material and product design domains. The study enriches a new path to +illustrate material factors at the time of new product development. +","Establishment of Relationships between Material Design and Product + Design Domains by Hybrid FEM-ANN Technique" +" Currently, criminals profile (CP) is obtained from investigators or forensic +psychologists interpretation, linking crime scene characteristics and an +offenders behavior to his or her characteristics and psychological profile. +This paper seeks an efficient and systematic discovery of nonobvious and +valuable patterns between variables from a large database of solved cases via a +probabilistic network (PN) modeling approach. The PN structure can be used to +extract behavioral patterns and to gain insight into what factors influence +these behaviors. Thus, when a new case is being investigated and the profile +variables are unknown because the offender has yet to be identified, the +observed crime scene variables are used to infer the unknown variables based on +their connections in the structure and the corresponding numerical +(probabilistic) weights. The objective is to produce a more systematic and +empirical approach to profiling, and to use the resulting PN model as a +decision tool. +",Modeling of Human Criminal Behavior using Probabilistic Networks +" Constraint programming can definitely be seen as a model-driven paradigm. The +users write programs for modeling problems. These programs are mapped to +executable models to calculate the solutions. This paper focuses on efficient +model management (definition and transformation). From this point of view, we +propose to revisit the design of constraint-programming systems. A model-driven +architecture is introduced to map solving-independent constraint models to +solving-dependent decision models. Several important questions are examined, +such as the need for a visual highlevel modeling language, and the quality of +metamodeling techniques to implement the transformations. A main result is the +s-COMMA platform that efficiently implements the chain from modeling to solving +constraint problems +",Model-Driven Constraint Programming +" An important challenge in constraint programming is to rewrite constraint +models into executable programs calculat- ing the solutions. This phase of +constraint processing may require translations between constraint programming +lan- guages, transformations of constraint representations, model +optimizations, and tuning of solving strategies. In this paper, we introduce a +pivot metamodel describing the common fea- tures of constraint models including +different kinds of con- straints, statements like conditionals and loops, and +other first-class elements like object classes and predicates. This metamodel +is general enough to cope with the constructions of many languages, from +object-oriented modeling languages to logic languages, but it is independent +from them. The rewriting operations manipulate metamodel instances apart from +languages. As a consequence, the rewriting operations apply whatever languages +are selected and they are able to manage model semantic information. A bridge +is created between the metamodel space and languages using parsing techniques. +Tools from the software engineering world can be useful to implement this +framework. +",Rewriting Constraint Models with Metamodels +" Transforming constraint models is an important task in re- cent constraint +programming systems. User-understandable models are defined during the modeling +phase but rewriting or tuning them is manda- tory to get solving-efficient +models. We propose a new architecture al- lowing to define bridges between any +(modeling or solver) languages and to implement model optimizations. This +architecture follows a model- driven approach where the constraint modeling +process is seen as a set of model transformations. Among others, an interesting +feature is the def- inition of transformations as concept-oriented rules, i.e. +based on types of model elements where the types are organized into a hierarchy +called a metamodel. +","Using ATL to define advanced and flexible constraint model + transformations" +" The methodology of Bayesian Model Averaging (BMA) is applied for assessment +of newborn brain maturity from sleep EEG. In theory this methodology provides +the most accurate assessments of uncertainty in decisions. However, the +existing BMA techniques have been shown providing biased assessments in the +absence of some prior information enabling to explore model parameter space in +details within a reasonable time. The lack in details leads to disproportional +sampling from the posterior distribution. In case of the EEG assessment of +brain maturity, BMA results can be biased because of the absence of information +about EEG feature importance. In this paper we explore how the posterior +information about EEG features can be used in order to reduce a negative impact +of disproportional sampling on BMA performance. We use EEG data recorded from +sleeping newborns to test the efficiency of the proposed BMA technique. +","Feature Importance in Bayesian Assessment of Newborn Brain Maturity from + EEG" +" In this paper we describe an original computational model for solving +different types of Distributed Constraint Satisfaction Problems (DCSP). The +proposed model is called Controller-Agents for Constraints Solving (CACS). This +model is intended to be used which is an emerged field from the integration +between two paradigms of different nature: Multi-Agent Systems (MAS) and the +Constraint Satisfaction Problem paradigm (CSP) where all constraints are +treated in central manner as a black-box. This model allows grouping +constraints to form a subset that will be treated together as a local problem +inside the controller. Using this model allows also handling non-binary +constraints easily and directly so that no translating of constraints into +binary ones is needed. This paper presents the implementation outlines of a +prototype of DCSP solver, its usage methodology and overview of the CACS +application for timetabling problems. +",A new model for solution of complex distributed constrained problems +" Model transformations operate on models conforming to precisely defined +metamodels. Consequently, it often seems relatively easy to chain them: the +output of a transformation may be given as input to a second one if metamodels +match. However, this simple rule has some obvious limitations. For instance, a +transformation may only use a subset of a metamodel. Therefore, chaining +transformations appropriately requires more information. We present here an +approach that automatically discovers more detailed information about actual +chaining constraints by statically analyzing transformations. The objective is +to provide developers who decide to chain transformations with more data on +which to base their choices. This approach has been successfully applied to the +case of a library of endogenous transformations. They all have the same source +and target metamodel but have some hidden chaining constraints. In such a case, +the simple metamodel matching rule given above does not provide any useful +information. +",Automatically Discovering Hidden Transformation Chaining Constraints +" This article presents the results of the research carried out on the +development of a medical diagnostic system applied to the Acute Bacterial +Meningitis, using the Case Based Reasoning methodology. The research was +focused on the implementation of the adaptation stage, from the integration of +Case Based Reasoning and Rule Based Expert Systems. In this adaptation stage we +use a higher level RBC that stores and allows reutilizing change experiences, +combined with a classic rule-based inference engine. In order to take into +account the most evident clinical situation, a pre-diagnosis stage is +implemented using a rule engine that, given an evident situation, emits the +corresponding diagnosis and avoids the complete process. +","Integration of Rule Based Expert Systems and Case Based Reasoning in an + Acute Bacterial Meningitis Clinical Decision Support System" +" Recent advancement in web services plays an important role in business to +business and business to consumer interaction. Discovery mechanism is not only +used to find a suitable service but also provides collaboration between service +providers and consumers by using standard protocols. A static web service +discovery mechanism is not only time consuming but requires continuous human +interaction. This paper proposed an efficient dynamic web services discovery +mechanism that can locate relevant and updated web services from service +registries and repositories with timestamp based on indexing value and +categorization for faster and efficient discovery of service. The proposed +prototype focuses on quality of service issues and introduces concept of local +cache, categorization of services, indexing mechanism, CSP (Constraint +Satisfaction Problem) solver, aging and usage of translator. Performance of +proposed framework is evaluated by implementing the algorithm and correctness +of our method is shown. The results of proposed framework shows greater +performance and accuracy in dynamic discovery mechanism of web services +resolving the existing issues of flexibility, scalability, based on quality of +service, and discovers updated and most relevant services with ease of usage. +",Indexer Based Dynamic Web Services Discovery +" Fuzzy Description Logics (DLs) are a family of logics which allow the +representation of (and the reasoning with) structured knowledge affected by +vagueness. Although most of the not very expressive crisp DLs, such as ALC, +enjoy the Finite Model Property (FMP), this is not the case once we move into +the fuzzy case. In this paper we show that if we allow arbitrary knowledge +bases, then the fuzzy DLs ALC under Lukasiewicz and Product fuzzy logics do not +verify the FMP even if we restrict to witnessed models; in other words, finite +satisfiability and witnessed satisfiability are different for arbitrary +knowledge bases. The aim of this paper is to point out the failure of FMP +because it affects several algorithms published in the literature for reasoning +under fuzzy ALC. +","On the Failure of the Finite Model Property in some Fuzzy Description + Logics" +" The basic aim of our study is to give a possible model for handling uncertain +information. This model is worked out in the framework of DATALOG. At first the +concept of fuzzy Datalog will be summarized, then its extensions for +intuitionistic- and interval-valued fuzzy logic is given and the concept of +bipolar fuzzy Datalog is introduced. Based on these ideas the concept of +multivalued knowledge-base will be defined as a quadruple of any background +knowledge; a deduction mechanism; a connecting algorithm, and a function set of +the program, which help us to determine the uncertainty levels of the results. +At last a possible evaluation strategy is given. +",A multivalued knowledge-base model +" RefereeToolbox is a java package implementing combination operators for +fusing evidences. It is downloadable from: +http://refereefunction.fredericdambreville.com/releases RefereeToolbox is based +on an interpretation of the fusion rules by means of Referee Functions. This +approach implies a dissociation between the definition of the combination and +its actual implementation, which is common to all referee-based combinations. +As a result, RefereeToolbox is designed with the aim to be generic and +evolutive. +",Release ZERO.0.1 of package RefereeToolbox +" LEXSYS, (Legume Expert System) was a project conceived at IITA (International +Institute of Tropical Agriculture) Ibadan Nigeria. It was initiated by the +COMBS (Collaborative Group on Maize-Based Systems Research in the 1990. It was +meant for a general framework for characterizing on-farm testing for technology +design for sustainable cereal-based cropping system. LEXSYS is not a true +expert system as the name would imply, but simply a user-friendly information +system. This work is an attempt to give a formal representation of the existing +system and then present areas where intelligent agent can be applied. +",LEXSYS: Architecture and Implication for Intelligent Agent systems +" Computing value of information (VOI) is a crucial task in various aspects of +decision-making under uncertainty, such as in meta-reasoning for search; in +selecting measurements to make, prior to choosing a course of action; and in +managing the exploration vs. exploitation tradeoff. Since such applications +typically require numerous VOI computations during a single run, it is +essential that VOI be computed efficiently. We examine the issue of anytime +estimation of VOI, as frequently it suffices to get a crude estimate of the +VOI, thus saving considerable computational resources. As a case study, we +examine VOI estimation in the measurement selection problem. Empirical +evaluation of the proposed scheme in this domain shows that computational +resources can indeed be significantly reduced, at little cost in expected +rewards achieved in the overall decision problem. +",Rational Value of Information Estimation for Measurement Selection +" Formalism based on GA is an alternative to distributed representation models +developed so far --- Smolensky's tensor product, Holographic Reduced +Representations (HRR) and Binary Spatter Code (BSC). Convolutions are replaced +by geometric products, interpretable in terms of geometry which seems to be the +most natural language for visualization of higher concepts. This paper recalls +the main ideas behind the GA model and investigates recognition test results +using both inner product and a clipped version of matrix representation. The +influence of accidental blade equality on recognition is also studied. Finally, +the efficiency of the GA model is compared to that of previously developed +models. +",Geometric Algebra Model of Distributed Representations +" We present in this paper some examples of how to compute by hand the PCR5 +fusion rule for three sources, so the reader will better understand its +mechanism. We also take into consideration the importance of sources, which is +different from the classical discounting of sources. +","Importance of Sources using the Repeated Fusion Method and the + Proportional Conflict Redistribution Rules #5 and #6" +" Terrorism has led to many problems in Thai societies, not only property +damage but also civilian casualties. Predicting terrorism activities in advance +can help prepare and manage risk from sabotage by these activities. This paper +proposes a framework focusing on event classification in terrorism domain using +fuzzy inference systems (FISs). Each FIS is a decision-making model combining +fuzzy logic and approximate reasoning. It is generated in five main parts: the +input interface, the fuzzification interface, knowledge base unit, decision +making unit and output defuzzification interface. Adaptive neuro-fuzzy +inference system (ANFIS) is a FIS model adapted by combining the fuzzy logic +and neural network. The ANFIS utilizes automatic identification of fuzzy logic +rules and adjustment of membership function (MF). Moreover, neural network can +directly learn from data set to construct fuzzy logic rules and MF implemented +in various applications. FIS settings are evaluated based on two comparisons. +The first evaluation is the comparison between unstructured and structured +events using the same FIS setting. The second comparison is the model settings +between FIS and ANFIS for classifying structured events. The data set consists +of news articles related to terrorism events in three southern provinces of +Thailand. The experimental results show that the classification performance of +the FIS resulting from structured events achieves satisfactory accuracy and is +better than the unstructured events. In addition, the classification of +structured events using ANFIS gives higher performance than the events using +only FIS in the prediction of terrorism events. +",Terrorism Event Classification Using Fuzzy Inference Systems +" Most of the web user's requirements are search or navigation time and getting +correctly matched result. These constrains can be satisfied with some +additional modules attached to the existing search engines and web servers. +This paper proposes that powerful architecture for search engines with the +title of Probabilistic Semantic Web Mining named from the methods used. With +the increase of larger and larger collection of various data resources on the +World Wide Web (WWW), Web Mining has become one of the most important +requirements for the web users. Web servers will store various formats of data +including text, image, audio, video etc., but servers can not identify the +contents of the data. These search techniques can be improved by adding some +special techniques including semantic web mining and probabilistic analysis to +get more accurate results. Semantic web mining technique can provide meaningful +search of data resources by eliminating useless information with mining +process. In this technique web servers will maintain Meta information of each +and every data resources available in that particular web server. This will +help the search engine to retrieve information that is relevant to user given +input string. This paper proposing the idea of combing these two techniques +Semantic web mining and Probabilistic analysis for efficient and accurate +search results of web mining. SPF can be calculated by considering both +semantic accuracy and syntactic accuracy of data with the input string. This +will be the deciding factor for producing results. +",Probabilistic Semantic Web Mining Using Artificial Neural Analysis +" The Nystrom method is an efficient technique to speed up large-scale learning +applications by generating low-rank approximations. Crucial to the performance +of this technique is the assumption that a matrix can be well approximated by +working exclusively with a subset of its columns. In this work we relate this +assumption to the concept of matrix coherence and connect matrix coherence to +the performance of the Nystrom method. Making use of related work in the +compressed sensing and the matrix completion literature, we derive novel +coherence-based bounds for the Nystrom method in the low-rank setting. We then +present empirical results that corroborate these theoretical bounds. Finally, +we present more general empirical results for the full-rank setting that +convincingly demonstrate the ability of matrix coherence to measure the degree +to which information can be extracted from a subset of columns. +",Matrix Coherence and the Nystrom Method +" We define the concept of an internal symmetry. This is a symmety within a +solution of a constraint satisfaction problem. We compare this to solution +symmetry, which is a mapping between different solutions of the same problem. +We argue that we may be able to exploit both types of symmetry when finding +solutions. We illustrate the potential of exploiting internal symmetries on two +benchmark domains: Van der Waerden numbers and graceful graphs. By identifying +internal symmetries we are able to extend the state of the art in both cases. +",Symmetry within Solutions +" We study propagation algorithms for the conjunction of two AllDifferent +constraints. Solutions of an AllDifferent constraint can be seen as perfect +matchings on the variable/value bipartite graph. Therefore, we investigate the +problem of finding simultaneous bipartite matchings. We present an extension of +the famous Hall theorem which characterizes when simultaneous bipartite +matchings exists. Unfortunately, finding such matchings is NP-hard in general. +However, we prove a surprising result that finding a simultaneous matching on a +convex bipartite graph takes just polynomial time. Based on this theoretical +result, we provide the first polynomial time bound consistency algorithm for +the conjunction of two AllDifferent constraints. We identify a pathological +problem on which this propagator is exponentially faster compared to existing +propagators. Our experiments show that this new propagator can offer +significant benefits over existing methods. +",Propagating Conjunctions of AllDifferent Constraints +" The successful execution of a construction project is heavily impacted by +making the right decision during tendering processes. Managing tender +procedures is very complex and uncertain involving coordination of many tasks +and individuals with different priorities and objectives. Bias and inconsistent +decision are inevitable if the decision-making process is totally depends on +intuition, subjective judgement or emotion. In making transparent decision and +healthy competition tendering, there exists a need for flexible guidance tool +for decision support. Aim of this paper is to give a review on current +practices of Decision Support Systems (DSS) technology in construction +tendering processes. Current practices of general tendering processes as +applied to the most countries in different regions such as United States, +Europe, Middle East and Asia are comprehensively discussed. Applications of +Web-based tendering processes is also summarised in terms of its properties. +Besides that, a summary of Decision Support System (DSS) components is included +in the next section. Furthermore, prior researches on implementation of DSS +approaches in tendering processes are discussed in details. Current issues +arise from both of paper-based and Web-based tendering processes are outlined. +Finally, conclusion is included at the end of this paper. +",Decision Support Systems (DSS) in Construction Tendering Processes +" The need for integration of ontologies with nonmonotonic rules has been +gaining importance in a number of areas, such as the Semantic Web. A number of +researchers addressed this problem by proposing a unified semantics for hybrid +knowledge bases composed of both an ontology (expressed in a fragment of +first-order logic) and nonmonotonic rules. These semantics have matured over +the years, but only provide solutions for the static case when knowledge does +not need to evolve. In this paper we take a first step towards addressing the +dynamics of hybrid knowledge bases. We focus on knowledge updates and, +considering the state of the art of belief update, ontology update and rule +update, we show that current solutions are only partial and difficult to +combine. Then we extend the existing work on ABox updates with rules, provide a +semantics for such evolving hybrid knowledge bases and study its basic +properties. To the best of our knowledge, this is the first time that an update +operator is proposed for hybrid knowledge bases. +",Towards Closed World Reasoning in Dynamic Open Worlds (Extended Version) +" On the basis of an analysis of previous research, we present a generalized +approach for measuring the difference of plans with an exemplary application to +machine scheduling. Our work is motivated by the need for such measures, which +are used in dynamic scheduling and planning situations. In this context, +quantitative approaches are needed for the assessment of the robustness and +stability of schedules. Obviously, any `robustness' or `stability' of plans has +to be defined w. r. t. the particular situation and the requirements of the +human decision maker. Besides the proposition of an instability measure, we +therefore discuss possibilities of obtaining meaningful information from the +decision maker for the implementation of the introduced approach. +","On the comparison of plans: Proposition of an instability measure for + dynamic machine scheduling" +" We define an inference system to capture explanations based on causal +statements, using an ontology in the form of an IS-A hierarchy. We first +introduce a simple logical language which makes it possible to express that a +fact causes another fact and that a fact explains another fact. We present a +set of formal inference patterns from causal statements to explanation +statements. We introduce an elementary ontology which gives greater +expressiveness to the system while staying close to propositional reasoning. We +provide an inference system that captures the patterns discussed, firstly in a +purely propositional framework, then in a datalog (limited predicate) +framework. +",Ontology-based inference for causal explanation +" We report (to our knowledge) the first evaluation of Constraint Satisfaction +as a computational framework for solving closest string problems. We show that +careful consideration of symbol occurrences can provide search heuristics that +provide several orders of magnitude speedup at and above the optimal distance. +We also report (to our knowledge) the first analysis and evaluation -- using +any technique -- of the computational difficulties involved in the +identification of all closest strings for a given input set. We describe +algorithms for web-scale distributed solution of closest string problems, both +purely based on AI backtrack search and also hybrid numeric-AI methods. +",The Exact Closest String Problem as a Constraint Satisfaction Problem +" We consider the problem of jointly training structured models for extraction +from sources whose instances enjoy partial overlap. This has important +applications like user-driven ad-hoc information extraction on the web. Such +applications present new challenges in terms of the number of sources and their +arbitrary pattern of overlap not seen by earlier collective training schemes +applied on two sources. We present an agreement-based learning framework and +alternatives within it to trade-off tractability, robustness to noise, and +extent of agreement. We provide a principled scheme to discover low-noise +agreement sets in unlabeled data across the sources. Through extensive +experiments over 58 real datasets, we establish that our method of additively +rewarding agreement over maximal segments of text provides the best trade-offs, +and also scores over alternatives such as collective inference, staged +training, and multi-view learning. +",Joint Structured Models for Extraction from Overlapping Sources +" A technique to study the dynamics of solving of a research task is suggested. +The research task was based on specially developed software Right- Wrong +Responder (RWR), with the participants having to reveal the response logic of +the program. The participants interacted with the program in the form of a +semi-binary dialogue, which implies the feedback responses of only two kinds - +""right"" or ""wrong"". The technique has been applied to a small pilot group of +volunteer participants. Some of them have successfully solved the task +(solvers) and some have not (non-solvers). In the beginning of the work, the +solvers did more wrong moves than non-solvers, and they did less wrong moves +closer to the finish of the work. A phase portrait of the work both in solvers +and non-solvers showed definite cycles that may correspond to sequences of +partially true hypotheses that may be formulated by the participants during the +solving of the task. +","An approach to visualize the course of solving of a research task in + humans" +" This paper constructively proves the existence of an effective procedure +generating a computable (total) function that is not contained in any given +effectively enumerable set of such functions. The proof implies the existence +of machines that process informal concepts such as computable (total) functions +beyond the limits of any given Turing machine or formal system, that is, these +machines can, in a certain sense, ""compute"" function values beyond these +limits. We call these machines creative. We argue that any ""intelligent"" +machine should be capable of processing informal concepts such as computable +(total) functions, that is, it should be creative. Finally, we introduce +hypotheses on creative machines which were developed on the basis of +theoretical investigations and experiments with computer programs. The +hypotheses say that machine intelligence is the execution of a self-developing +procedure starting from any universal programming language and any input. +",Informal Concepts in Machines +" Mountain river torrents and snow avalanches generate human and material +damages with dramatic consequences. Knowledge about natural phenomenona is +often lacking and expertise is required for decision and risk management +purposes using multi-disciplinary quantitative or qualitative approaches. +Expertise is considered as a decision process based on imperfect information +coming from more or less reliable and conflicting sources. A methodology mixing +the Analytic Hierarchy Process (AHP), a multi-criteria aid-decision method, and +information fusion using Belief Function Theory is described. Fuzzy Sets and +Possibilities theories allow to transform quantitative and qualitative criteria +into a common frame of discernment for decision in Dempster-Shafer Theory (DST +) and Dezert-Smarandache Theory (DSmT) contexts. Main issues consist in basic +belief assignments elicitation, conflict identification and management, fusion +rule choices, results validation but also in specific needs to make a +difference between importance and reliability and uncertainty in the fusion +process. +","A two-step fusion process for multi-criteria decision applied to natural + hazards in mountains" +" A lot of mathematical knowledge has been formalized and stored in +repositories by now: different mathematical theorems and theories have been +taken into consideration and included in mathematical repositories. +Applications more distant from pure mathematics, however --- though based on +these theories --- often need more detailed knowledge about the underlying +theories. In this paper we present an example Mizar formalization from the area +of electrical engineering focusing on stability theory which is based on +complex analysis. We discuss what kind of special knowledge is necessary here +and which amount of this knowledge is included in existing repositories. +",On Building a Knowledge Base for Stability Theory +" It is hypothesized that creativity arises from the self-mending capacity of +an internal model of the world, or worldview. The uniquely honed worldview of a +creative individual results in a distinctive style that is recognizable within +and across domains. It is further hypothesized that creativity is domaingeneral +in the sense that there exist multiple avenues by which the distinctiveness of +one's worldview can be expressed. These hypotheses were tested using art +students and creative writing students. Art students guessed significantly +above chance both which painting was done by which of five famous artists, and +which artwork was done by which of their peers. Similarly, creative writing +students guessed significantly above chance both which passage was written by +which of five famous writers, and which passage was written by which of their +peers. These findings support the hypothesis that creative style is +recognizable. Moreover, creative writing students guessed significantly above +chance which of their peers produced particular works of art, supporting the +hypothesis that creative style is recognizable not just within but across +domains. +","Recognizability of Individual Creative Style Within and Across Domains: + Preliminary Studies" +" Approximate dynamic programming has been used successfully in a large variety +of domains, but it relies on a small set of provided approximation features to +calculate solutions reliably. Large and rich sets of features can cause +existing algorithms to overfit because of a limited number of samples. We +address this shortcoming using $L_1$ regularization in approximate linear +programming. Because the proposed method can automatically select the +appropriate richness of features, its performance does not degrade with an +increasing number of features. These results rely on new and stronger sampling +bounds for regularized approximate linear programs. We also propose a +computationally efficient homotopy method. The empirical evaluation of the +approach shows that the proposed method performs well on simple MDPs and +standard benchmark problems. +","Feature Selection Using Regularization in Approximate Linear Programs + for Markov Decision Processes" +" We discuss how to use a Genetic Regulatory Network as an evolutionary +representation to solve a typical GP reinforcement problem, the pole balancing. +The network is a modified version of an Artificial Regulatory Network proposed +a few years ago, and the task could be solved only by finding a proper way of +connecting inputs and outputs to the network. We show that the representation +is able to generalize well over the problem domain, and discuss the performance +of different models of this kind. +",Evolving Genes to Balance a Pole +" Programs to solve so-called constraint problems are complex pieces of +software which require many design decisions to be made more or less +arbitrarily by the implementer. These decisions affect the performance of the +finished solver significantly. Once a design decision has been made, it cannot +easily be reversed, although a different decision may be more appropriate for a +particular problem. + We investigate using machine learning to make these decisions automatically +depending on the problem to solve with the alldifferent constraint as an +example. Our system is capable of making non-trivial, multi-level decisions +that improve over always making a default choice. +","Using machine learning to make constraint solver implementation + decisions" +" In this paper the author presents a kind of Soft Computing Technique, mainly +an application of fuzzy set theory of Prof. Zadeh [16], on a problem of Medical +Experts Systems. The choosen problem is on design of a physician's decision +model which can take crisp as well as fuzzy data as input, unlike the +traditional models. The author presents a mathematical model based on fuzzy set +theory for physician aided evaluation of a complete representation of +information emanating from the initial interview including patient past +history, present symptoms, and signs observed upon physical examination and +results of clinical and diagnostic tests. +",A Soft Computing Model for Physicians' Decision Process +" An important problem in computational social choice theory is the complexity +of undesirable behavior among agents, such as control, manipulation, and +bribery in election systems. These kinds of voting strategies are often +tempting at the individual level but disastrous for the agents as a whole. +Creating election systems where the determination of such strategies is +difficult is thus an important goal. + An interesting set of elections is that of scoring protocols. Previous work +in this area has demonstrated the complexity of misuse in cases involving a +fixed number of candidates, and of specific election systems on unbounded +number of candidates such as Borda. In contrast, we take the first step in +generalizing the results of computational complexity of election misuse to +cases of infinitely many scoring protocols on an unbounded number of +candidates. Interesting families of systems include $k$-approval and $k$-veto +elections, in which voters distinguish $k$ candidates from the candidate set. + Our main result is to partition the problems of these families based on their +complexity. We do so by showing they are polynomial-time computable, NP-hard, +or polynomial-time equivalent to another problem of interest. We also +demonstrate a surprising connection between manipulation in election systems +and some graph theory problems. +",The Complexity of Manipulating $k$-Approval Elections +" In the last two decades, a number of methods have been proposed for +forecasting based on fuzzy time series. Most of the fuzzy time series methods +are presented for forecasting of car road accidents. However, the forecasting +accuracy rates of the existing methods are not good enough. In this paper, we +compared our proposed new method of fuzzy time series forecasting with existing +methods. Our method is based on means based partitioning of the historical data +of car road accidents. The proposed method belongs to the kth order and +time-variant methods. The proposed method can get the best forecasting accuracy +rate for forecasting the car road accidents than the existing methods. +","Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of + Analystical Methodology" +" In this paper, a new learning algorithm for adaptive network intrusion +detection using naive Bayesian classifier and decision tree is presented, which +performs balance detections and keeps false positives at acceptable level for +different types of network attacks, and eliminates redundant attributes as well +as contradictory examples from training data that make the detection model +complex. The proposed algorithm also addresses some difficulties of data mining +such as handling continuous attribute, dealing with missing attribute values, +and reducing noise in training data. Due to the large volumes of security audit +data as well as the complex and dynamic properties of intrusion behaviours, +several data miningbased intrusion detection techniques have been applied to +network-based traffic data and host-based data in the last decades. However, +there remain various issues needed to be examined towards current intrusion +detection systems (IDS). We tested the performance of our proposed algorithm +with existing learning algorithms by employing on the KDD99 benchmark intrusion +detection dataset. The experimental results prove that the proposed algorithm +achieved high detection rates (DR) and significant reduce false positives (FP) +for different types of network intrusions using limited computational +resources. +",Combining Naive Bayes and Decision Tree for Adaptive Intrusion Detection +" This paper presents a combination of several automated reasoning and proof +presentation tools with the Mizar system for formalization of mathematics. The +combination forms an online service called MizAR, similar to the SystemOnTPTP +service for first-order automated reasoning. The main differences to +SystemOnTPTP are the use of the Mizar language that is oriented towards human +mathematicians (rather than the pure first-order logic used in SystemOnTPTP), +and setting the service in the context of the large Mizar Mathematical Library +of previous theorems,definitions, and proofs (rather than the isolated problems +that are solved in SystemOnTPTP). These differences poses new challenges and +new opportunities for automated reasoning and for proof presentation tools. +This paper describes the overall structure of MizAR, and presents the automated +reasoning systems and proof presentation tools that are combined to make MizAR +a useful mathematical service. +","Automated Reasoning and Presentation Support for Formalizing Mathematics + in Mizar" +" Structured and semi-structured data describing entities, taxonomies and +ontologies appears in many domains. There is a huge interest in integrating +structured information from multiple sources; however integrating structured +data to infer complex common structures is a difficult task because the +integration must aggregate similar structures while avoiding structural +inconsistencies that may appear when the data is combined. In this work, we +study the integration of structured social metadata: shallow personal +hierarchies specified by many individual users on the SocialWeb, and focus on +inferring a collection of integrated, consistent taxonomies. We frame this task +as an optimization problem with structural constraints. We propose a new +inference algorithm, which we refer to as Relational Affinity Propagation (RAP) +that extends affinity propagation (Frey and Dueck 2007) by introducing +structural constraints. We validate the approach on a real-world social media +dataset, collected from the photosharing website Flickr. Our empirical results +show that our proposed approach is able to construct deeper and denser +structures compared to an approach using only the standard affinity propagation +algorithm. +",Integrating Structured Metadata with Relational Affinity Propagation +" The paper offers a mathematical formalization of the Turing test. This +formalization makes it possible to establish the conditions under which some +Turing machine will pass the Turing test and the conditions under which every +Turing machine (or every Turing machine of the special class) will fail the +Turing test. +",A Formalization of the Turing Test +" Many social Web sites allow users to annotate the content with descriptive +metadata, such as tags, and more recently to organize content hierarchically. +These types of structured metadata provide valuable evidence for learning how a +community organizes knowledge. For instance, we can aggregate many personal +hierarchies into a common taxonomy, also known as a folksonomy, that will aid +users in visualizing and browsing social content, and also to help them in +organizing their own content. However, learning from social metadata presents +several challenges, since it is sparse, shallow, ambiguous, noisy, and +inconsistent. We describe an approach to folksonomy learning based on +relational clustering, which exploits structured metadata contained in personal +hierarchies. Our approach clusters similar hierarchies using their structure +and tag statistics, then incrementally weaves them into a deeper, bushier tree. +We study folksonomy learning using social metadata extracted from the +photo-sharing site Flickr, and demonstrate that the proposed approach addresses +the challenges. Moreover, comparing to previous work, the approach produces +larger, more accurate folksonomies, and in addition, scales better. +","Growing a Tree in the Forest: Constructing Folksonomies by Integrating + Structured Metadata" +" Symmetry is an important feature of many constraint programs. We show that +any problem symmetry acting on a set of symmetry breaking constraints can be +used to break symmetry. Different symmetries pick out different solutions in +each symmetry class. This simple but powerful idea can be used in a number of +different ways. We describe one application within model restarts, a search +technique designed to reduce the conflict between symmetry breaking and the +branching heuristic. In model restarts, we restart search periodically with a +random symmetry of the symmetry breaking constraints. Experimental results show +that this symmetry breaking technique is effective in practice on some standard +benchmark problems. +",Symmetries of Symmetry Breaking Constraints +" We propose automatically learning probabilistic Hierarchical Task Networks +(pHTNs) in order to capture a user's preferences on plans, by observing only +the user's behavior. HTNs are a common choice of representation for a variety +of purposes in planning, including work on learning in planning. Our +contributions are (a) learning structure and (b) representing preferences. In +contrast, prior work employing HTNs considers learning method preconditions +(instead of structure) and representing domain physics or search control +knowledge (rather than preferences). Initially we will assume that the observed +distribution of plans is an accurate representation of user preference, and +then generalize to the situation where feasibility constraints frequently +prevent the execution of preferred plans. In order to learn a distribution on +plans we adapt an Expectation-Maximization (EM) technique from the discipline +of (probabilistic) grammar induction, taking the perspective of task reductions +as productions in a context-free grammar over primitive actions. To account for +the difference between the distributions of possible and preferred plans we +subsequently modify this core EM technique, in short, by rescaling its input. +","Learning Probabilistic Hierarchical Task Networks to Capture User + Preferences" +" Brain-Like Stochastic Search (BLiSS) refers to this task: given a family of +utility functions U(u,A), where u is a vector of parameters or task +descriptors, maximize or minimize U with respect to u, using networks (Option +Nets) which input A and learn to generate good options u stochastically. This +paper discusses why this is crucial to brain-like intelligence (an area funded +by NSF) and to many applications, and discusses various possibilities for +network design and training. The appendix discusses recent research, relations +to work on stochastic optimization in operations research, and relations to +engineering-based approaches to understanding neocortex. +","Brain-Like Stochastic Search: A Research Challenge and Funding + Opportunity" +" We introduce a framework for representing a variety of interesting problems +as inference over the execution of probabilistic model programs. We represent a +""solution"" to such a problem as a guide program which runs alongside the model +program and influences the model program's random choices, leading the model +program to sample from a different distribution than from its priors. Ideally +the guide program influences the model program to sample from the posteriors +given the evidence. We show how the KL- divergence between the true posterior +distribution and the distribution induced by the guided model program can be +efficiently estimated (up to an additive constant) by sampling multiple +executions of the guided model program. In addition, we show how to use the +guide program as a proposal distribution in importance sampling to +statistically prove lower bounds on the probability of the evidence and on the +probability of a hypothesis and the evidence. We can use the quotient of these +two bounds as an estimate of the conditional probability of the hypothesis +given the evidence. We thus turn the inference problem into a heuristic search +for better guide programs. +",Variational Program Inference +" The basic unit of meaning on the Semantic Web is the RDF statement, or +triple, which combines a distinct subject, predicate and object to make a +definite assertion about the world. A set of triples constitutes a graph, to +which they give a collective meaning. It is upon this simple foundation that +the rich, complex knowledge structures of the Semantic Web are built. Yet the +very expressiveness of RDF, by inviting comparison with real-world knowledge, +highlights a fundamental shortcoming, in that RDF is limited to statements of +absolute fact, independent of the context in which a statement is asserted. +This is in stark contrast with the thoroughly context-sensitive nature of human +thought. The model presented here provides a particularly simple means of +contextualizing an RDF triple by associating it with related statements in the +same graph. This approach, in combination with a notion of graph similarity, is +sufficient to select only those statements from an RDF graph which are +subjectively most relevant to the context of the requesting process. +",The Dilated Triple +" In this Information system age many organizations consider information system +as their weapon to compete or gain competitive advantage or give the best +services for non profit organizations. Game Information System as combining +Information System and game is breakthrough to achieve organizations' +performance. The Game Information System will run the Information System with +game and how game can be implemented to run the Information System. Game is not +only for fun and entertainment, but will be a challenge to combine fun and +entertainment with Information System. The Challenge to run the information +system with entertainment, deliver the entertainment with information system +all at once. Game information system can be implemented in many sectors as like +the information system itself but in difference's view. A view of game which +people can joy and happy and do their transaction as a fun things. +",Game Information System +" In order to get strategic positioning for competition in business +organization, the information system must be ahead in this information age +where the information as one of the weapons to win the competition and in the +right hand the information will become a right bullet. The information system +with the information technology support isn't enough if just only on internet +or implemented with internet technology. The growth of information technology +as tools for helping and making people easy to use must be accompanied by +wanting to make fun and happy when they make contact with the information +technology itself. Basically human like to play, since childhood human have +been playing, free and happy and when human grow up they can't play as much as +when human was in their childhood. We have to develop the information system +which is not perform information system itself but can help human to explore +their natural instinct for playing, making fun and happiness when they interact +with the information system. Virtual information system is the way to present +playing and having fun atmosphere on working area. +",Virtual information system on working area +" Earthquake DSS is an information technology environment which can be used by +government to sharpen, make faster and better the earthquake mitigation +decision. Earthquake DSS can be delivered as E-government which is not only for +government itself but in order to guarantee each citizen's rights for +education, training and information about earthquake and how to overcome the +earthquake. Knowledge can be managed for future use and would become mining by +saving and maintain all the data and information about earthquake and +earthquake mitigation in Indonesia. Using Web technology will enhance global +access and easy to use. Datawarehouse as unNormalized database for +multidimensional analysis will speed the query process and increase reports +variation. Link with other Disaster DSS in one national disaster DSS, link with +other government information system and international will enhance the +knowledge and sharpen the reports. +",Indonesian Earthquake Decision Support System +" Markov decision processes (MDPs) are widely used for modeling decision-making +problems in robotics, automated control, and economics. Traditional MDPs assume +that the decision maker (DM) knows all states and actions. However, this may +not be true in many situations of interest. We define a new framework, MDPs +with unawareness (MDPUs) to deal with the possibilities that a DM may not be +aware of all possible actions. We provide a complete characterization of when a +DM can learn to play near-optimally in an MDPU, and give an algorithm that +learns to play near-optimally when it is possible to do so, as efficiently as +possible. In particular, we characterize when a near-optimal solution can be +found in polynomial time. +",MDPs with Unawareness +" Existing value function approximation methods have been successfully used in +many applications, but they often lack useful a priori error bounds. We propose +a new approximate bilinear programming formulation of value function +approximation, which employs global optimization. The formulation provides +strong a priori guarantees on both robust and expected policy loss by +minimizing specific norms of the Bellman residual. Solving a bilinear program +optimally is NP-hard, but this is unavoidable because the Bellman-residual +minimization itself is NP-hard. We describe and analyze both optimal and +approximate algorithms for solving bilinear programs. The analysis shows that +this algorithm offers a convergent generalization of approximate policy +iteration. We also briefly analyze the behavior of bilinear programming +algorithms under incomplete samples. Finally, we demonstrate that the proposed +approach can consistently minimize the Bellman residual on simple benchmark +problems. +",Global Optimization for Value Function Approximation +" Different notions of equivalence, such as the prominent notions of strong and +uniform equivalence, have been studied in Answer-Set Programming, mainly for +the purpose of identifying programs that can serve as substitutes without +altering the semantics, for instance in program optimization. Such semantic +comparisons are usually characterized by various selections of models in the +logic of Here-and-There (HT). For uniform equivalence however, correct +characterizations in terms of HT-models can only be obtained for finite +theories, respectively programs. In this article, we show that a selection of +countermodels in HT captures uniform equivalence also for infinite theories. +This result is turned into coherent characterizations of the different notions +of equivalence by countermodels, as well as by a mixture of HT-models and +countermodels (so-called equivalence interpretations). Moreover, we generalize +the so-called notion of relativized hyperequivalence for programs to +propositional theories, and apply the same methodology in order to obtain a +semantic characterization which is amenable to infinite settings. This allows +for a lifting of the results to first-order theories under a very general +semantics given in terms of a quantified version of HT. We thus obtain a +general framework for the study of various notions of equivalence for theories +under answer-set semantics. Moreover, we prove an expedient property that +allows for a simplified treatment of extended signatures, and provide further +results for non-ground logic programs. In particular, uniform equivalence +coincides under open and ordinary answer-set semantics, and for finite +non-ground programs under these semantics, also the usual characterization of +uniform equivalence in terms of maximal and total HT-models of the grounding is +correct, even for infinite domains, when corresponding ground programs are +infinite. +","A General Framework for Equivalences in Answer-Set Programming by + Countermodels in the Logic of Here-and-There" +" Human disease diagnosis is a complicated process and requires high level of +expertise. Any attempt of developing a web-based expert system dealing with +human disease diagnosis has to overcome various difficulties. This paper +describes a project work aiming to develop a web-based fuzzy expert system for +diagnosing human diseases. Now a days fuzzy systems are being used successfully +in an increasing number of application areas; they use linguistic rules to +describe systems. This research project focuses on the research and development +of a web-based clinical tool designed to improve the quality of the exchange of +health information between health care professionals and patients. +Practitioners can also use this web-based tool to corroborate diagnosis. The +proposed system is experimented on various scenarios in order to evaluate it's +performance. In all the cases, proposed system exhibits satisfactory results. +",Human Disease Diagnosis Using a Fuzzy Expert System +" In the area of computer science focusing on creating machines that can engage +on behaviors that humans consider intelligent. The ability to create +intelligent machines has intrigued humans since ancient times and today with +the advent of the computer and 50 years of research into various programming +techniques, the dream of smart machines is becoming a reality. Researchers are +creating systems which can mimic human thought, understand speech, beat the +best human chessplayer, and countless other feats never before possible. +Ability of the human to estimate the information is most brightly shown in +using of natural languages. Using words of a natural language for valuation +qualitative attributes, for example, the person pawns uncertainty in form of +vagueness in itself estimations. Vague sets, vague judgments, vague conclusions +takes place there and then, where and when the reasonable subject exists and +also is interested in something. The vague sets theory has arisen as the answer +to an illegibility of language the reasonable subject speaks. Language of a +reasonable subject is generated by vague events which are created by the reason +and which are operated by the mind. The theory of vague sets represents an +attempt to find such approximation of vague grouping which would be more +convenient, than the classical theory of sets in situations where the natural +language plays a significant role. Such theory has been offered by known +American mathematician Gau and Buehrer .In our paper we are describing how +vagueness of linguistic variables can be solved by using the vague set +theory.This paper is mainly designed for one of directions of the eventology +(the theory of the random vague events), which has arisen within the limits of +the probability theory and which pursue the unique purpose to describe +eventologically a movement of reason. +",Vagueness of Linguistic variable +" Ontologies usually suffer from the semantic heterogeneity when simultaneously +used in information sharing, merging, integrating and querying processes. +Therefore, the similarity identification between ontologies being used becomes +a mandatory task for all these processes to handle the problem of semantic +heterogeneity. In this paper, we propose an efficient technique for similarity +measurement between two ontologies. The proposed technique identifies all +candidate pairs of similar concepts without omitting any similar pair. The +proposed technique can be used in different types of operations on ontologies +such as merging, mapping and aligning. By analyzing its results a reasonable +improvement in terms of completeness, correctness and overall quality of the +results has been found. +",An Efficient Technique for Similarity Identification between Ontologies +" Many formal languages have been proposed to express or represent Ontologies, +including RDF, RDFS, DAML+OIL and OWL. Most of these languages are based on XML +syntax, but with various terminologies and expressiveness. Therefore, choosing +a language for building an Ontology is the main step. The main point of +choosing language to represent Ontology is based mainly on what the Ontology +will represent or be used for. That language should have a range of quality +support features such as ease of use, expressive power, compatibility, sharing +and versioning, internationalisation. This is because different kinds of +knowledge-based applications need different language features. The main +objective of these languages is to add semantics to the existing information on +the web. The aims of this paper is to provide a good knowledge of existing +language and understanding of these languages and how could be used. +",The State of the Art: Ontology Web-Based Languages: XML Based +" Semantic Web is actually an extension of the current one in that it +represents information more meaningfully for humans and computers alike. It +enables the description of contents and services in machine-readable form, and +enables annotating, discovering, publishing, advertising and composing services +to be automated. It was developed based on Ontology, which is considered as the +backbone of the Semantic Web. In other words, the current Web is transformed +from being machine-readable to machine-understandable. In fact, Ontology is a +key technique with which to annotate semantics and provide a common, +comprehensible foundation for resources on the Semantic Web. Moreover, Ontology +can provide a common vocabulary, a grammar for publishing data, and can supply +a semantic description of data which can be used to preserve the Ontologies and +keep them ready for inference. This paper provides basic concepts of web +services and the Semantic Web, defines the structure and the main applications +of ontology, and provides many relevant terms are explained in order to provide +a basic understanding of ontologies. +",Understanding Semantic Web and Ontologies: Theory and Applications +" Finding the structure of a graphical model has been received much attention +in many fields. Recently, it is reported that the non-Gaussianity of data +enables us to identify the structure of a directed acyclic graph without any +prior knowledge on the structure. In this paper, we propose a novel +non-Gaussianity based algorithm for more general type of models; chain graphs. +The algorithm finds an ordering of the disjoint subsets of variables by +iteratively evaluating the independence between the variable subset and the +residuals when the remaining variables are regressed on those. However, its +computational cost grows exponentially according to the number of variables. +Therefore, we further discuss an efficient approximate approach for applying +the algorithm to large sized graphs. We illustrate the algorithm with +artificial and real-world datasets. +",GroupLiNGAM: Linear non-Gaussian acyclic models for sets of variables +" Notions of core, support and inversion of a soft set have been defined and +studied. Soft approximations are soft sets developed through core and support, +and are used for granulating the soft space. Membership structure of a soft set +has been probed in and many interesting properties presented. The mathematical +apparatus developed so far in this paper yields a detailed analysis of two +works viz. [N. Cagman, S. Enginoglu, Soft set theory and uni-int decision +making, European Jr. of Operational Research (article in press, available +online 12 May 2010)] and [N. Cagman, S. Enginoglu, Soft matrix theory and its +decision making, Computers and Mathematics with Applications 59 (2010) 3308 - +3314.]. We prove (Theorem 8.1) that uni-int method of Cagman is equivalent to a +core-support expression which is computationally far less expansive than +uni-int. This also highlights some shortcomings in Cagman's uni-int method and +thus motivates us to improve the method. We first suggest an improvement in +uni-int method and then present a new conjecture to solve the optimum choice +problem given by Cagman and Enginoglu. Our Example 8.6 presents a case where +the optimum choice is intuitively clear yet both uni-int methods (Cagman's and +our improved one) give wrong answer but the new conjecture solves the problem +correctly. +",Soft Approximations and uni-int Decision Making +" In recent years there has been growing interest in solutions for the delivery +of clinical care for the elderly, due to the large increase in aging +population. Monitoring a patient in his home environment is necessary to ensure +continuity of care in home settings, but, to be useful, this activity must not +be too invasive for patients and a burden for caregivers. We prototyped a +system called SINDI (Secure and INDependent lIving), focused on i) collecting a +limited amount of data about the person and the environment through Wireless +Sensor Networks (WSN), and ii) inferring from these data enough information to +support caregivers in understanding patients' well being and in predicting +possible evolutions of their health. Our hierarchical logic-based model of +health combines data from different sources, sensor data, tests results, +common-sense knowledge and patient's clinical profile at the lower level, and +correlation rules between health conditions across upper levels. The logical +formalization and the reasoning process are based on Answer Set Programming. +The expressive power of this logic programming paradigm makes it possible to +reason about health evolution even when the available information is incomplete +and potentially incoherent, while declarativity simplifies rules specification +by caregivers and allows automatic encoding of knowledge. This paper describes +how these issues have been targeted in the application scenario of the SINDI +system. +","Reasoning Support for Risk Prediction and Prevention in Independent + Living" +" Iris recognition technology, used to identify individuals by photographing +the iris of their eye, has become popular in security applications because of +its ease of use, accuracy, and safety in controlling access to high-security +areas. Fusion of multiple algorithms for biometric verification performance +improvement has received considerable attention. The proposed method combines +the zero-crossing 1 D wavelet Euler number, and genetic algorithm based for +feature extraction. The output from these three algorithms is normalized and +their score are fused to decide whether the user is genuine or imposter. This +new strategies is discussed in this paper, in order to compute a multimodal +combined score. +",Improving Iris Recognition Accuracy By Score Based Fusion Method +" We study decompositions of the global NVALUE constraint. Our main +contribution is theoretical: we show that there are propagators for global +constraints like NVALUE which decomposition can simulate with the same time +complexity but with a much greater space complexity. This suggests that the +benefit of a global propagator may often not be in saving time but in saving +space. Our other theoretical contribution is to show for the first time that +range consistency can be enforced on NVALUE with the same worst-case time +complexity as bound consistency. Finally, the decompositions we study are +readily encoded as linear inequalities. We are therefore able to use them in +integer linear programs. +",Decomposition of the NVALUE constraint +" Symmetry can be used to help solve many problems. For instance, Einstein's +famous 1905 paper (""On the Electrodynamics of Moving Bodies"") uses symmetry to +help derive the laws of special relativity. In artificial intelligence, +symmetry has played an important role in both problem representation and +reasoning. I describe recent work on using symmetry to help solve constraint +satisfaction problems. Symmetries occur within individual solutions of problems +as well as between different solutions of the same problem. Symmetry can also +be applied to the constraints in a problem to give new symmetric constraints. +Reasoning about symmetry can speed up problem solving, and has led to the +discovery of new results in both graph and number theory. +",Symmetry within and between solutions +" We propose an online form of the cake cutting problem. This models situations +where players arrive and depart during the process of dividing a resource. We +show that well known fair division procedures like cut-and-choose and the +Dubins-Spanier moving knife procedure can be adapted to apply to such online +problems. We propose some desirable properties that online cake cutting +procedures might possess like online forms of proportionality and +envy-freeness, and identify which properties are in fact possessed by the +different online cake procedures. +",Online Cake Cutting +" The stable marriage problem has a wide variety of practical applications, +ranging from matching resident doctors to hospitals, to matching students to +schools, or more generally to any two-sided market. We consider a useful +variation of the stable marriage problem, where the men and women express their +preferences using a preference list with ties over a subset of the members of +the other sex. Matchings are permitted only with people who appear in these +preference lists. In this setting, we study the problem of finding a stable +matching that marries as many people as possible. Stability is an envy-free +notion: no man and woman who are not married to each other would both prefer +each other to their partners or to being single. This problem is NP-hard. We +tackle this problem using local search, exploiting properties of the problem to +reduce the size of the neighborhood and to make local moves efficiently. +Experimental results show that this approach is able to solve large problems, +quickly returning stable matchings of large and often optimal size. +",Local search for stable marriage problems with ties and incomplete lists +" Frequent Episode Discovery framework is a popular framework in Temporal Data +Mining with many applications. Over the years many different notions of +frequencies of episodes have been proposed along with different algorithms for +episode discovery. In this paper we present a unified view of all such +frequency counting algorithms. We present a generic algorithm such that all +current algorithms are special cases of it. This unified view allows one to +gain insights into different frequencies and we present quantitative +relationships among different frequencies. Our unified view also helps in +obtaining correctness proofs for various algorithms as we show here. We also +point out how this unified view helps us to consider generalization of the +algorithm so that they can discover episodes with general partial orders. +","A unified view of Automata-based algorithms for Frequent Episode + Discovery" +" The stable marriage (SM) problem has a wide variety of practical +applications, ranging from matching resident doctors to hospitals, to matching +students to schools, or more generally to any two-sided market. In the +classical formulation, n men and n women express their preferences (via a +strict total order) over the members of the other sex. Solving a SM problem +means finding a stable marriage where stability is an envy-free notion: no man +and woman who are not married to each other would both prefer each other to +their partners or to being single. We consider both the classical stable +marriage problem and one of its useful variations (denoted SMTI) where the men +and women express their preferences in the form of an incomplete preference +list with ties over a subset of the members of the other sex. Matchings are +permitted only with people who appear in these lists, an we try to find a +stable matching that marries as many people as possible. Whilst the SM problem +is polynomial to solve, the SMTI problem is NP-hard. We propose to tackle both +problems via a local search approach, which exploits properties of the problems +to reduce the size of the neighborhood and to make local moves efficiently. We +evaluate empirically our algorithm for SM problems by measuring its runtime +behaviour and its ability to sample the lattice of all possible stable +marriages. We evaluate our algorithm for SMTI problems in terms of both its +runtime behaviour and its ability to find a maximum cardinality stable +marriage.For SM problems, the number of steps of our algorithm grows only as +O(nlog(n)), and that it samples very well the set of all stable marriages. It +is thus a fair and efficient approach to generate stable marriages.Furthermore, +our approach for SMTI problems is able to solve large problems, quickly +returning stable matchings of large and often optimal size despite the +NP-hardness of this problem. +",Local search for stable marriage problems +" From the advent of the application of satellite imagery to land cover +mapping, one of the growing areas of research interest has been in the area of +image classification. Image classifiers are algorithms used to extract land +cover information from satellite imagery. Most of the initial research has +focussed on the development and application of algorithms to better existing +and emerging classifiers. In this paper, a paradigm shift is proposed whereby a +committee of classifiers is used to determine the final classification output. +Two of the key components of an ensemble system are that there should be +diversity among the classifiers and that there should be a mechanism through +which the results are combined. In this paper, the members of the ensemble +system include: Linear SVM, Gaussian SVM and Quadratic SVM. The final output +was determined through a simple majority vote of the individual classifiers. +From the results obtained it was observed that the final derived map generated +by an ensemble system can potentially improve on the results derived from the +individual classifiers making up the ensemble system. The ensemble system +classification accuracy was, in this case, better than the linear and quadratic +SVM result. It was however less than that of the RBF SVM. Areas for further +research could focus on improving the diversity of the ensemble system used in +this research. +",An svm multiclassifier approach to land cover mapping +" A general method is given for revising degrees of belief and arriving at +consistent decisions about a system of logically constrained issues. In +contrast to other works about belief revision, here the constraints are assumed +to be fixed. The method has two variants, dual of each other, whose revised +degrees of belief are respectively above and below the original ones. The upper +[resp. lower] revised degrees of belief are uniquely characterized as the +lowest [resp. highest] ones that are invariant by a certain max-min [resp. +min-max] operation determined by the logical constraints. In both variants, +making balance between the revised degree of belief of a proposition and that +of its negation leads to decisions that are ensured to be consistent with the +logical constraints. These decisions are ensured to agree with the majority +criterion as applied to the original degrees of belief whenever this gives a +consistent result. They are also also ensured to satisfy a property of respect +for unanimity about any particular issue, as well as a property of monotonicity +with respect to the original degrees of belief. The application of the method +to certain special domains comes down to well established or increasingly +accepted methods, such as the single-link method of cluster analysis and the +method of paths in preferential voting. +",A general method for deciding about logically constrained issues +" Strategic Environmental Assessment is a procedure aimed at introducing +systematic assessment of the environmental effects of plans and programs. This +procedure is based on the so-called coaxial matrices that define dependencies +between plan activities (infrastructures, plants, resource extractions, +buildings, etc.) and positive and negative environmental impacts, and +dependencies between these impacts and environmental receptors. Up to now, this +procedure is manually implemented by environmental experts for checking the +environmental effects of a given plan or program, but it is never applied +during the plan/program construction. A decision support system, based on a +clear logic semantics, would be an invaluable tool not only in assessing a +single, already defined plan, but also during the planning process in order to +produce an optimized, environmentally assessed plan and to study possible +alternative scenarios. We propose two logic-based approaches to the problem, +one based on Constraint Logic Programming and one on Probabilistic Logic +Programming that could be, in the future, conveniently merged to exploit the +advantages of both. We test the proposed approaches on a real energy plan and +we discuss their limitations and advantages. +",Logic-Based Decision Support for Strategic Environmental Assessment +" Hybrid MKNF knowledge bases are one of the most prominent tightly integrated +combinations of open-world ontology languages with closed-world (non-monotonic) +rule paradigms. The definition of Hybrid MKNF is parametric on the description +logic (DL) underlying the ontology language, in the sense that non-monotonic +rules can extend any decidable DL language. Two related semantics have been +defined for Hybrid MKNF: one that is based on the Stable Model Semantics for +logic programs and one on the Well-Founded Semantics (WFS). Under WFS, the +definition of Hybrid MKNF relies on a bottom-up computation that has polynomial +data complexity whenever the DL language is tractable. Here we define a general +query-driven procedure for Hybrid MKNF that is sound with respect to the stable +model-based semantics, and sound and complete with respect to its WFS variant. +This procedure is able to answer a slightly restricted form of conjunctive +queries, and is based on tabled rule evaluation extended with an external +oracle that captures reasoning within the ontology. Such an (abstract) oracle +receives as input a query along with knowledge already derived, and replies +with a (possibly empty) set of atoms, defined in the rules, whose truth would +suffice to prove the initial query. With appropriate assumptions on the +complexity of the abstract oracle, the general procedure maintains the data +complexity of the WFS for Hybrid MKNF knowledge bases. + To illustrate this approach, we provide a concrete oracle for EL+, a fragment +of the light-weight DL EL++. Such an oracle has practical use, as EL++ is the +language underlying OWL 2 EL, which is part of the W3C recommendations for the +Semantic Web, and is tractable for reasoning tasks such as subsumption. We show +that query-driven Hybrid MKNF preserves polynomial data complexity when using +the EL+ oracle and WFS. +",Query-driven Procedures for Hybrid MKNF Knowledge Bases +" Answer set programming - the most popular problem solving paradigm based on +logic programs - has been recently extended to support uninterpreted function +symbols. All of these approaches have some limitation. In this paper we propose +a class of programs called FP2 that enjoys a different trade-off between +expressiveness and complexity. FP2 programs enjoy the following unique +combination of properties: (i) the ability of expressing predicates with +infinite extensions; (ii) full support for predicates with arbitrary arity; +(iii) decidability of FP2 membership checking; (iv) decidability of skeptical +and credulous stable model reasoning for call-safe queries. Odd cycles are +supported by composing FP2 programs with argument restricted programs. +",A decidable subclass of finitary programs +" This paper models a decision support system to predict the occurance of +suicide attack in a given collection of cities. The system comprises two parts. +First part analyzes and identifies the factors which affect the prediction. +Admitting incomplete information and use of linguistic terms by experts, as two +characteristic features of this peculiar prediction problem we exploit the +Theory of Fuzzy Soft Sets. Hence the Part 2 of the model is an algorithm vz. +FSP which takes the assessment of factors given in Part 1 as its input and +produces a possibility profile of cities likely to receive the accident. The +algorithm is of O(2^n) complexity. It has been illustrated by an example solved +in detail. Simulation results for the algorithm have been presented which give +insight into the strengths and weaknesses of FSP. Three different decision +making measures have been simulated and compared in our discussion. +",Predicting Suicide Attacks: A Fuzzy Soft Set Approach +" An approach to the revision of logic programs under the answer set semantics +is presented. For programs P and Q, the goal is to determine the answer sets +that correspond to the revision of P by Q, denoted P * Q. A fundamental +principle of classical (AGM) revision, and the one that guides the approach +here, is the success postulate. In AGM revision, this stipulates that A is in K +* A. By analogy with the success postulate, for programs P and Q, this means +that the answer sets of Q will in some sense be contained in those of P * Q. +The essential idea is that for P * Q, a three-valued answer set for Q, +consisting of positive and negative literals, is first determined. The positive +literals constitute a regular answer set, while the negated literals make up a +minimal set of naf literals required to produce the answer set from Q. These +literals are propagated to the program P, along with those rules of Q that are +not decided by these literals. The approach differs from work in update logic +programs in two main respects. First, we ensure that the revising logic program +has higher priority, and so we satisfy the success postulate; second, for the +preference implicit in a revision P * Q, the program Q as a whole takes +precedence over P, unlike update logic programs, since answer sets of Q are +propagated to P. We show that a core group of the AGM postulates are satisfied, +as are the postulates that have been proposed for update logic programs. +","A Program-Level Approach to Revising Logic Programs under the Answer Set + Semantics" +" We study the problem of coalitional manipulation in elections using the +unweighted Borda rule. We provide empirical evidence of the manipulability of +Borda elections in the form of two new greedy manipulation algorithms based on +intuitions from the bin-packing and multiprocessor scheduling domains. Although +we have not been able to show that these algorithms beat existing methods in +the worst-case, our empirical evaluation shows that they significantly +outperform the existing method and are able to find optimal manipulations in +the vast majority of the randomly generated elections that we tested. These +empirical results provide further evidence that the Borda rule provides little +defense against coalitional manipulation. +",An Empirical Study of Borda Manipulation +" Autonomous planetary vehicles, also known as rovers, are small autonomous +vehicles equipped with a variety of sensors used to perform exploration and +experiments on a planet's surface. Rovers work in a partially unknown +environment, with narrow energy/time/movement constraints and, typically, small +computational resources that limit the complexity of on-line planning and +scheduling, thus they represent a great challenge in the field of autonomous +vehicles. Indeed, formal models for such vehicles usually involve hybrid +systems with nonlinear dynamics, which are difficult to handle by most of the +current planning algorithms and tools. Therefore, when offline planning of the +vehicle activities is required, for example for rovers that operate without a +continuous Earth supervision, such planning is often performed on simplified +models that are not completely realistic. In this paper we show how the +UPMurphi model checking based planning tool can be used to generate +resource-optimal plans to control the engine of an autonomous planetary +vehicle, working directly on its hybrid model and taking into account several +safety constraints, thus achieving very accurate results. +",Resource-Optimal Planning For An Autonomous Planetary Vehicle +" This paper presents the solution about the threat of a VBIED (Vehicle-Born +Improvised Explosive Device) obtained with the DSmT (Dezert-Smarandache +Theory). This problem has been proposed recently to the authors by Simon +Maskell and John Lavery as a typical illustrative example to try to compare the +different approaches for dealing with uncertainty for decision-making support. +The purpose of this paper is to show in details how a solid justified solution +can be obtained from DSmT approach and its fusion rules thanks to a proper +modeling of the belief functions involved in this problem. +","Threat assessment of a possible Vehicle-Born Improvised Explosive Device + using DSmT" +" A key factor that can dramatically reduce the search space during constraint +solving is the criterion under which the variable to be instantiated next is +selected. For this purpose numerous heuristics have been proposed. Some of the +best of such heuristics exploit information about failures gathered throughout +search and recorded in the form of constraint weights, while others measure the +importance of variable assignments in reducing the search space. In this work +we experimentally evaluate the most recent and powerful variable ordering +heuristics, and new variants of them, over a wide range of benchmarks. Results +demonstrate that heuristics based on failures are in general more efficient. +Based on this, we then derive new revision ordering heuristics that exploit +recorded failures to efficiently order the propagation list when arc +consistency is maintained during search. Interestingly, in addition to reducing +the number of constraint checks and list operations, these heuristics are also +able to cut down the size of the explored search tree. +","Evaluating and Improving Modern Variable and Revision Ordering + Strategies in CSPs" +" The two standard branching schemes for CSPs are d-way and 2-way branching. +Although it has been shown that in theory the latter can be exponentially more +effective than the former, there is a lack of empirical evidence showing such +differences. To investigate this, we initially make an experimental comparison +of the two branching schemes over a wide range of benchmarks. Experimental +results verify the theoretical gap between d-way and 2-way branching as we move +from a simple variable ordering heuristic like dom to more sophisticated ones +like dom/ddeg. However, perhaps surprisingly, experiments also show that when +state-of-the-art variable ordering heuristics like dom/wdeg are used then d-way +can be clearly more efficient than 2-way branching in many cases. Motivated by +this observation, we develop two generic heuristics that can be applied at +certain points during search to decide whether 2-way branching or a restricted +version of 2-way branching, which is close to d-way branching, will be +followed. The application of these heuristics results in an adaptive branching +scheme. Experiments with instantiations of the two generic heuristics confirm +that search with adaptive branching outperforms search with a fixed branching +scheme on a wide range of problems. +",Adaptive Branching for Constraint Satisfaction Problems +" Event-driven automation of reactive functionalities for complex event +processing is an urgent need in today's distributed service-oriented +architectures and Web-based event-driven environments. An important problem to +be addressed is how to correctly and efficiently capture and process the +event-based behavioral, reactive logic embodied in reaction rules, and +combining this with other conditional decision logic embodied, e.g., in +derivation rules. This paper elaborates a homogeneous integration approach that +combines derivation rules, reaction rules and other rule types such as +integrity constraints into the general framework of logic programming, the +industrial-strength version of declarative programming. We describe syntax and +semantics of the language, implement a distributed web-based middleware using +enterprise service technologies and illustrate its adequacy in terms of +expressiveness, efficiency and scalability through examples extracted from +industrial use cases. The developed reaction rule language provides expressive +features such as modular ID-based updates with support for external imports and +self-updates of the intensional and extensional knowledge bases, transactions +including integrity testing and roll-backs of update transition paths. It also +supports distributed complex event processing, event messaging and event +querying via efficient and scalable enterprise middleware technologies and +event/action reasoning based on an event/action algebra implemented by an +interval-based event calculus variant as a logic inference formalism. +",A Homogeneous Reaction Rule Language for Complex Event Processing +" The four intensive problems to the software rose by the software industry +.i.e., User System Communication / Human Machine Interface, Meta Data +extraction, Information processing & management and Data representation are +discussed in this research paper. To contribute in the field we have proposed +and described an intelligent semantic oriented agent based search engine +including the concepts of intelligent graphical user interface, natural +language based information processing, data management and data reconstruction +for the final user end information representation. +","Semantic Oriented Agent based Approach towards Engineering Data + Management, Web Information Retrieval and User System Communication Problems" +" Web development is a challenging research area for its creativity and +complexity. The existing raised key challenge in web technology technologic +development is the presentation of data in machine read and process able format +to take advantage in knowledge based information extraction and maintenance. +Currently it is not possible to search and extract optimized results using full +text queries because there is no such mechanism exists which can fully extract +the semantic from full text queries and then look for particular knowledge +based information. +","An Agent based Approach towards Metadata Extraction, Modelling and + Information Retrieval over the Web" +" In order to study the communication between information systems, Gong and +Xiao [Z. Gong and Z. Xiao, Communicating between information systems based on +including degrees, International Journal of General Systems 39 (2010) 189--206] +proposed the concept of general relation mappings based on including degrees. +Some properties and the extension for fuzzy information systems of the general +relation mappings have been investigated there. In this paper, we point out by +counterexamples that several assertions (Lemma 3.1, Lemma 3.2, Theorem 4.1, and +Theorem 4.3) in the aforementioned work are not true in general. +","A note on communicating between information systems based on including + degrees" +" World Wide Web (WWW) is the most popular global information sharing and +communication system consisting of three standards .i.e., Uniform Resource +Identifier (URL), Hypertext Transfer Protocol (HTTP) and Hypertext Mark-up +Language (HTML). Information is provided in text, image, audio and video +formats over the web by using HTML which is considered to be unconventional in +defining and formalizing the meaning of the context... +",Role of Ontology in Semantic Web Development +" Recent research has highlighted the practical benefits of subjective +interestingness measures, which quantify the novelty or unexpectedness of a +pattern when contrasted with any prior information of the data miner +(Silberschatz and Tuzhilin, 1995; Geng and Hamilton, 2006). A key challenge +here is the formalization of this prior information in a way that lends itself +to the definition of an interestingness subjective measure that is both +meaningful and practical. + In this paper, we outline a general strategy of how this could be achieved, +before working out the details for a use case that is important in its own +right. + Our general strategy is based on considering prior information as constraints +on a probabilistic model representing the uncertainty about the data. More +specifically, we represent the prior information by the maximum entropy +(MaxEnt) distribution subject to these constraints. We briefly outline various +measures that could subsequently be used to contrast patterns with this MaxEnt +model, thus quantifying their subjective interestingness. +","Maximum entropy models and subjective interestingness: an application to + tiles in binary databases" +" We examine the practicality for a user of using Answer Set Programming (ASP) +for representing logical formalisms. Our example is a formalism aiming at +capturing causal explanations from causal information. We show the naturalness +and relative efficiency of this translation job. We are interested in the ease +for writing an ASP program. Limitations of the earlier systems made that in +practice, the ``declarative aspect'' was more theoretical than practical. We +show how recent improvements in working ASP systems facilitate the translation. +","A formalism for causal explanations with an Answer Set Programming + translation" +" Knowledge Management is a global process in companies. It includes all the +processes that allow capitalization, sharing and evolution of the Knowledge +Capital of the firm, generally recognized as a critical resource of the +organization. Several approaches have been defined to capitalize knowledge but +few of them study how to learn from this knowledge. We present in this paper an +approach that helps to enhance learning from profession knowledge in an +organisation. We apply our approach on knitting industry. +",Learning from Profession Knowledge: Application on Knitting +" Constraint solvers are complex pieces of software which require many design +decisions to be made by the implementer based on limited information. These +decisions affect the performance of the finished solver significantly. Once a +design decision has been made, it cannot easily be reversed, although a +different decision may be more appropriate for a particular problem. + We investigate using machine learning to make these decisions automatically +depending on the problem to solve. We use the alldifferent constraint as a case +study. Our system is capable of making non-trivial, multi-level decisions that +improve over always making a default choice and can be implemented as part of a +general-purpose constraint solver. +","Machine learning for constraint solver design -- A case study for the + alldifferent constraint" +" Constraint problems can be trivially solved in parallel by exploring +different branches of the search tree concurrently. Previous approaches have +focused on implementing this functionality in the solver, more or less +transparently to the user. We propose a new approach, which modifies the +constraint model of the problem. An existing model is split into new models +with added constraints that partition the search space. Optionally, additional +constraints are imposed that rule out the search already done. The advantages +of our approach are that it can be implemented easily, computations can be +stopped and restarted, moved to different machines and indeed solved on +machines which are not able to communicate with each other at all. +",Distributed solving through model splitting +" In many applications involving multi-media data, the definition of similarity +between items is integral to several key tasks, e.g., nearest-neighbor +retrieval, classification, and recommendation. Data in such regimes typically +exhibits multiple modalities, such as acoustic and visual content of video. +Integrating such heterogeneous data to form a holistic similarity space is +therefore a key challenge to be overcome in many real-world applications. + We present a novel multiple kernel learning technique for integrating +heterogeneous data into a single, unified similarity space. Our algorithm +learns an optimal ensemble of kernel transfor- mations which conform to +measurements of human perceptual similarity, as expressed by relative +comparisons. To cope with the ubiquitous problems of subjectivity and +inconsistency in multi- media similarity, we develop graph-based techniques to +filter similarity measurements, resulting in a simplified and robust training +procedure. +",Learning Multi-modal Similarity +" Consideration of the primal and dual problems together leads to important new +insights into the characteristics of boosting algorithms. In this work, we +propose a general framework that can be used to design new boosting algorithms. +A wide variety of machine learning problems essentially minimize a regularized +risk functional. We show that the proposed boosting framework, termed CGBoost, +can accommodate various loss functions and different regularizers in a +totally-corrective optimization fashion. We show that, by solving the primal +rather than the dual, a large body of totally-corrective boosting algorithms +can actually be efficiently solved and no sophisticated convex optimization +solvers are needed. We also demonstrate that some boosting algorithms like +AdaBoost can be interpreted in our framework--even their optimization is not +totally corrective. We empirically show that various boosting algorithms based +on the proposed framework perform similarly on the UCIrvine machine learning +datasets [1] that we have used in the experiments. +",Totally Corrective Boosting for Regularized Risk Minimization +" Max Restricted Path Consistency (maxRPC) is a local consistency for binary +constraints that can achieve considerably stronger pruning than arc +consistency. However, existing maxRRC algorithms suffer from overheads and +redundancies as they can repeatedly perform many constraint checks without +triggering any value deletions. In this paper we propose techniques that can +boost the performance of maxRPC algorithms. These include the combined use of +two data structures to avoid many redundant constraint checks, and heuristics +for the efficient ordering and execution of certain operations. Based on these, +we propose two closely related algorithms. The first one which is a maxRPC +algorithm with optimal O(end^3) time complexity, displays good performance when +used stand-alone, but is expensive to apply during search. The second one +approximates maxRPC and has O(en^2d^4) time complexity, but a restricted +version with O(end^4) complexity can be very efficient when used during search. +Both algorithms have O(ed) space complexity. Experimental results demonstrate +that the resulting methods constantly outperform previous algorithms for +maxRPC, often by large margins, and constitute a more than viable alternative +to arc consistency on many problems. +",Improving the Performance of maxRPC +" The technical report presents a generic exact solution approach for +minimizing the project duration of the resource-constrained project scheduling +problem with generalized precedences (Rcpsp/max). The approach uses lazy clause +generation, i.e., a hybrid of finite domain and Boolean satisfiability solving, +in order to apply nogood learning and conflict-driven search on the solution +generation. Our experiments show the benefit of lazy clause generation for +finding an optimal solutions and proving its optimality in comparison to other +state-of-the-art exact and non-exact methods. The method is highly robust: it +matched or bettered the best known results on all of the 2340 instances we +examined except 3, according to the currently available data on the PSPLib. Of +the 631 open instances in this set it closed 573 and improved the bounds of 51 +of the remaining 58 instances. +","Solving the Resource Constrained Project Scheduling Problem with + Generalized Precedences by Lazy Clause Generation" +" The search strategy of a CP solver is determined by the variable and value +ordering heuristics it employs and by the branching scheme it follows. Although +the effects of variable and value ordering heuristics on search effort have +been widely studied, the effects of different branching schemes have received +less attention. In this paper we study this effect through an experimental +evaluation that includes standard branching schemes such as 2-way, d-way, and +dichotomic domain splitting, as well as variations of set branching where +branching is performed on sets of values. We also propose and evaluate a +generic approach to set branching where the partition of a domain into sets is +created using the scores assigned to values by a value ordering heuristic, and +a clustering algorithm from machine learning. Experimental results demonstrate +that although exponential differences between branching schemes, as predicted +in theory between 2-way and d-way branching, are not very common, still the +choice of branching scheme can make quite a difference on certain classes of +problems. Set branching methods are very competitive with 2-way branching and +outperform it on some problem classes. A statistical analysis of the results +reveals that our generic clustering-based set branching method is the best +among the methods compared. +",Experimental Evaluation of Branching Schemes for the CSP +" In this paper, we address the problem of creating believable agents (virtual +characters) in video games. We consider only one meaning of believability, +``giving the feeling of being controlled by a player'', and outline the problem +of its evaluation. We present several models for agents in games which can +produce believable behaviours, both from industry and research. For high level +of believability, learning and especially imitation learning seems to be the +way to go. We make a quick overview of different approaches to make video +games' agents learn from players. To conclude we propose a two-step method to +develop new models for believable agents. First we must find the criteria for +believability for our application and define an evaluation method. Then the +model and the learning algorithm can be designed. +","The Challenge of Believability in Video Games: Definitions, Agents + Models and Imitation Learning" +" Classic evaluation methods of believable agents are time-consuming because +they involve many human to judge agents. They are well suited to validate work +on new believable behaviours models. However, during the implementation, +numerous experiments can help to improve agents' believability. We propose a +method which aim at assessing how much an agent's behaviour looks like humans' +behaviours. By representing behaviours with vectors, we can store data computed +for humans and then evaluate as many agents as needed without further need of +humans. We present a test experiment which shows that even a simple evaluation +following our method can reveal differences between quite believable agents and +humans. This method seems promising although, as shown in our experiment, +results' analysis can be difficult. +","Automatable Evaluation Method Oriented toward Behaviour Believability + for Video Games" +" In this short paper I briefly discuss 3D war Game based on artificial +intelligence concepts called AI WAR. Going in to the details, I present the +importance of CAICL language and how this language is used in AI WAR. Moreover +I also present a designed and implemented 3D War Cybug for AI WAR using CAICL +and discus the implemented strategy to defeat its enemies during the game life. +",AI 3D Cybug Gaming +" Levesque introduced the notion of only-knowing to precisely capture the +beliefs of a knowledge base. He also showed how only-knowing can be used to +formalize non-monotonic behavior within a monotonic logic. Despite its appeal, +all attempts to extend only-knowing to the many agent case have undesirable +properties. A belief model by Halpern and Lakemeyer, for instance, appeals to +proof-theoretic constructs in the semantics and needs to axiomatize validity as +part of the logic. It is also not clear how to generalize their ideas to a +first-order case. In this paper, we propose a new account of multi-agent +only-knowing which, for the first time, has a natural possible-world semantics +for a quantified language with equality. We then provide, for the propositional +fragment, a sound and complete axiomatization that faithfully lifts Levesque's +proof theory to the many agent case. We also discuss comparisons to the earlier +approach by Halpern and Lakemeyer. +",Multi-Agent Only-Knowing Revisited +" In this paper we present an optimal Bangla Keyboard Layout, which distributes +the load equally on both hands so that maximizing the ease and minimizing the +effort. Bangla alphabet has a large number of letters, for this it is difficult +to type faster using Bangla keyboard. Our proposed keyboard will maximize the +speed of operator as they can type with both hands parallel. Here we use the +association rule of data mining to distribute the Bangla characters in the +keyboard. First, we analyze the frequencies of data consisting of monograph, +digraph and trigraph, which are derived from data wire-house, and then used +association rule of data mining to distribute the Bangla characters in the +layout. Finally, we propose a Bangla Keyboard Layout. Experimental results on +several keyboard layout shows the effectiveness of the proposed approach with +better performance. +",Optimal Bangla Keyboard Layout using Association Rule of Data Mining +" This paper presents an optimal Bangla Keyboard Layout, which distributes the +load equally on both hands so that maximizing the ease and minimizing the +effort. Bangla alphabet has a large number of letters, for this it is difficult +to type faster using Bangla keyboard. Our proposed keyboard will maximize the +speed of operator as they can type with both hands parallel. Here we use the +association rule of data mining to distribute the Bangla characters in the +keyboard. First, we analyze the frequencies of data consisting of monograph, +digraph and trigraph, which are derived from data wire-house, and then used +association rule of data mining to distribute the Bangla characters in the +layout. Experimental results on several data show the effectiveness of the +proposed approach with better performance. +",Optimal Bangla Keyboard Layout using Data Mining Technique +" Bangla alphabet has a large number of letters, for this it is complicated to +type faster using Bangla keyboard. The proposed keyboard will maximize the +speed of operator as they can type with both hands parallel. Association rule +of data mining to distribute the Bangla characters in the keyboard is used +here. The frequencies of data consisting of monograph, digraph and trigraph are +analyzed, which are derived from data wire-house, and then used association +rule of data mining to distribute the Bangla characters in the layout. +Experimental results on several data show the effectiveness of the proposed +approach with better performance. This paper presents an optimal Bangla +Keyboard Layout, which distributes the load equally on both hands so that +maximizing the ease and minimizing the effort. +",The Most Advantageous Bangla Keyboard Layout Using Data Mining Technique +" Support Vector Machines (SVMs) are popular tools for data mining tasks such +as classification, regression, and density estimation. However, original SVM +(C-SVM) only considers local information of data points on or over the margin. +Therefore, C-SVM loses robustness. To solve this problem, one approach is to +translate (i.e., to move without rotation or change of shape) the hyperplane +according to the distribution of the entire data. But existing work can only be +applied for 1-D case. In this paper, we propose a simple and efficient method +called General Scaled SVM (GS-SVM) to extend the existing approach to +multi-dimensional case. Our method translates the hyperplane according to the +distribution of data projected on the normal vector of the hyperplane. Compared +with C-SVM, GS-SVM has better performance on several data sets. +",General Scaled Support Vector Machines +" The problem of measuring similarity of graphs and their nodes is important in +a range of practical problems. There is a number of proposed measures, some of +them being based on iterative calculation of similarity between two graphs and +the principle that two nodes are as similar as their neighbors are. In our +work, we propose one novel method of that sort, with a refined concept of +similarity of two nodes that involves matching of their neighbors. We prove +convergence of the proposed method and show that it has some additional +desirable properties that, to our knowledge, the existing methods lack. We +illustrate the method on two specific problems and empirically compare it to +other methods. +",Measuring Similarity of Graphs and their Nodes by Neighbor Matching +" The paper proposes artificial intelligence technique called hill climbing to +find numerical solutions of Diophantine Equations. Such equations are important +as they have many applications in fields like public key cryptography, integer +factorization, algebraic curves, projective curves and data dependency in super +computers. Importantly, it has been proved that there is no general method to +find solutions of such equations. This paper is an attempt to find numerical +solutions of Diophantine equations using steepest ascent version of Hill +Climbing. The method, which uses tree representation to depict possible +solutions of Diophantine equations, adopts a novel methodology to generate +successors. The heuristic function used help to make the process of finding +solution as a minimization process. The work illustrates the effectiveness of +the proposed methodology using a class of Diophantine equations given by a1. x1 +p1 + a2. x2 p2 + ...... + an . xn pn = N where ai and N are integers. The +experimental results validate that the procedure proposed is successful in +finding solutions of Diophantine Equations with sufficiently large powers and +large number of variables. +",Steepest Ascent Hill Climbing For A Mathematical Problem +" This paper is mainly concerned with the question of how to decompose +multiclass classification problems into binary subproblems. We extend known +Jensen-Shannon bounds on the Bayes risk of binary problems to hierarchical +multiclass problems and use these bounds to develop a heuristic procedure for +constructing hierarchical multiclass decomposition for multinomials. We test +our method and compare it to the well known ""all-pairs"" decomposition. Our +tests are performed using a new authorship determination benchmark test of +machine learning authors. The new method consistently outperforms the all-pairs +decomposition when the number of classes is small and breaks even on larger +multiclass problems. Using both methods, the classification accuracy we +achieve, using an SVM over a feature set consisting of both high frequency +single tokens and high frequency token-pairs, appears to be exceptionally high +compared to known results in authorship determination. +","Hierarchical Multiclass Decompositions with Application to Authorship + Determination" +" The iDian (previously named as the Operation Agent System) is a framework +designed to enable computer users to operate software in natural language. +Distinct from current speech-recognition systems, our solution supports +format-free combinations of orders, and is open to both developers and +customers. We used a multi-layer structure to build the entire framework, +approached rule-based natural language processing, and implemented demos +narrowing down to Windows, text-editing and a few other applications. This +essay will firstly give an overview of the entire system, and then scrutinize +the functions and structure of the system, and finally discuss the prospective +de-velopment, esp. on-line interaction functions. +",Introduction to the iDian +" In this work we present a protocol for self-synchronized duty-cycling in +wireless sensor networks with energy harvesting capabilities. The protocol is +implemented in Wiselib, a library of generic algorithms for sensor networks. +Simulations are conducted with the sensor network simulator Shawn. They are +based on the specifications of real hardware known as iSense sensor nodes. The +experimental results show that the proposed mechanism is able to adapt to +changing energy availabilities. Moreover, it is shown that the system is very +robust against packet loss. +","A Protocol for Self-Synchronized Duty-Cycling in Sensor Networks: + Generic Implementation in Wiselib" +" Active Learning Method (ALM) is a soft computing method used for modeling and +control based on fuzzy logic. All operators defined for fuzzy sets must serve +as either fuzzy S-norm or fuzzy T-norm. Despite being a powerful modeling +method, ALM does not possess operators which serve as S-norms and T-norms which +deprive it of a profound analytical expression/form. This paper introduces two +new operators based on morphology which satisfy the following conditions: +First, they serve as fuzzy S-norm and T-norm. Second, they satisfy Demorgans +law, so they complement each other perfectly. These operators are investigated +via three viewpoints: Mathematics, Geometry and fuzzy logic. +",New S-norm and T-norm Operators for Active Learning Method +" Substitutability, interchangeability and related concepts in Constraint +Programming were introduced approximately twenty years ago and have given rise +to considerable subsequent research. We survey this work, classify, and relate +the different concepts, and indicate directions for future work, in particular +with respect to making connections with research into symmetry breaking. This +paper is a condensed version of a larger work in progress. +",A Partial Taxonomy of Substitutability and Interchangeability +" Concept drift refers to a non stationary learning problem over time. The +training and the application data often mismatch in real life problems. In this +report we present a context of concept drift problem 1. We focus on the issues +relevant to adaptive training set formation. We present the framework and +terminology, and formulate a global picture of concept drift learners design. +We start with formalizing the framework for the concept drifting data in +Section 1. In Section 2 we discuss the adaptivity mechanisms of the concept +drift learners. In Section 3 we overview the principle mechanisms of concept +drift learners. In this chapter we give a general picture of the available +algorithms and categorize them based on their properties. Section 5 discusses +the related research fields and Section 5 groups and presents major concept +drift applications. This report is intended to give a bird's view of concept +drift research field, provide a context of the research and position it within +broad spectrum of research fields and applications. +",Learning under Concept Drift: an Overview +" We introduce a new perspective on spectral dimensionality reduction which +views these methods as Gaussian Markov random fields (GRFs). Our unifying +perspective is based on the maximum entropy principle which is in turn inspired +by maximum variance unfolding. The resulting model, which we call maximum +entropy unfolding (MEU) is a nonlinear generalization of principal component +analysis. We relate the model to Laplacian eigenmaps and isomap. We show that +parameter fitting in the locally linear embedding (LLE) is approximate maximum +likelihood MEU. We introduce a variant of LLE that performs maximum likelihood +exactly: Acyclic LLE (ALLE). We show that MEU and ALLE are competitive with the +leading spectral approaches on a robot navigation visualization and a human +motion capture data set. Finally the maximum likelihood perspective allows us +to introduce a new approach to dimensionality reduction based on L1 +regularization of the Gaussian random field via the graphical lasso. +","A Unifying Probabilistic Perspective for Spectral Dimensionality + Reduction: Insights and New Models" +" Human can be distinguished by different limb movements and unique ground +reaction force. Cumulative foot pressure image is a 2-D cumulative ground +reaction force during one gait cycle. Although it contains pressure spatial +distribution information and pressure temporal distribution information, it +suffers from several problems including different shoes and noise, when putting +it into practice as a new biometric for pedestrian identification. In this +paper, we propose a hierarchical translation-invariant representation for +cumulative foot pressure images, inspired by the success of Convolutional deep +belief network for digital classification. Key contribution in our approach is +discriminative hierarchical sparse coding scheme which helps to learn useful +discriminative high-level visual features. Based on the feature representation +of cumulative foot pressure images, we develop a pedestrian recognition system +which is invariant to three different shoes and slight local shape change. +Experiments are conducted on a proposed open dataset that contains more than +2800 cumulative foot pressure images from 118 subjects. Evaluations suggest the +effectiveness of the proposed method and the potential of cumulative foot +pressure images as a biometric. +",Translation-Invariant Representation for Cumulative Foot Pressure Images +" An important issue in Qualitative Spatial Reasoning is the representation of +relative direction. In this paper we present simple geometric rules that enable +reasoning about relative direction between oriented points. This framework, the +Oriented Point Algebra OPRA_m, has a scalable granularity m. We develop a +simple algorithm for computing the OPRA_m composition tables and prove its +correctness. Using a composition table, algebraic closure for a set of OPRA +statements is sufficient to solve spatial navigation tasks. And it turns out +that scalable granularity is useful in these navigation tasks. +","Qualitative Reasoning about Relative Direction on Adjustable Levels of + Granularity" +" In the article a turn-based game played on four computers connected via +network is investigated. There are three computers with natural intelligence +and one with artificial intelligence. Game table is seen by each player's own +view point in all players' monitors. Domino pieces are three dimensional. For +distributed systems TCP/IP protocol is used. In order to get 3D image, +Microsoft XNA technology is applied. Domino 101 game is nondeterministic game +that is result of the game depends on the initial random distribution of the +pieces. Number of the distributions is equal to the multiplication of following +combinations: . Moreover, in this game that is played by four people, players +are divided into 2 pairs. Accordingly, we cannot predict how the player uses +the dominoes that is according to the dominoes of his/her partner or according +to his/her own dominoes. The fact that the natural intelligence can be a player +in any level affects the outcome. These reasons make it difficult to develop an +AI. In the article four levels of AI are developed. The AI in the first level +is equivalent to the intelligence of a child who knows the rules of the game +and recognizes the numbers. The AI in this level plays if it has any domino, +suitable to play or says pass. In most of the games which can be played on the +internet, the AI does the same. But the AI in the last level is a master +player, and it can develop itself according to its competitors' levels. +",A Distributed AI Aided 3D Domino Game +" In this paper it is considered rule reduct generation problem, based on Rough +Set Theory. Rule Reduct Generation (RG) and Modified Rule Generation (MRG) +algorithms are well-known. Alternative to these algorithms Pruning Algorithm of +Generation A Minimal Set of Rule Reducts, or briefly Pruning Rule Generation +(PRG) algorithm is developed. PRG algorithm uses tree structured data type. PRG +algorithm is compared with RG and MRG algorithms +","Prunnig Algorithm of Generation a Minimal Set of Rule Reducts Based on + Rough Set Theory" +" The cardinal direction calculus (CDC) proposed by Goyal and Egenhofer is a +very expressive qualitative calculus for directional information of extended +objects. Early work has shown that consistency checking of complete networks of +basic CDC constraints is tractable while reasoning with the CDC in general is +NP-hard. This paper shows, however, if allowing some constraints unspecified, +then consistency checking of possibly incomplete networks of basic CDC +constraints is already intractable. This draws a sharp boundary between the +tractable and intractable subclasses of the CDC. The result is achieved by a +reduction from the well-known 3-SAT problem. +","Reasoning about Cardinal Directions between Extended Objects: The + Hardness Result" +" Imitation learning in robots, also called programing by demonstration, has +made important advances in recent years, allowing humans to teach context +dependant motor skills/tasks to robots. We propose to extend the usual contexts +investigated to also include acoustic linguistic expressions that might denote +a given motor skill, and thus we target joint learning of the motor skills and +their potential acoustic linguistic name. In addition to this, a modification +of a class of existing algorithms within the imitation learning framework is +made so that they can handle the unlabeled demonstration of several tasks/motor +primitives without having to inform the imitator of what task is being +demonstrated or what the number of tasks are, which is a necessity for language +learning, i.e; if one wants to teach naturally an open number of new motor +skills together with their acoustic names. Finally, a mechanism for detecting +whether or not linguistic input is relevant to the task is also proposed, and +our architecture also allows the robot to find the right framing for a given +identified motor primitive. With these additions it becomes possible to build +an imitator that bridges the gap between imitation learning and language +learning by being able to learn linguistic expressions using methods from the +imitation learning community. In this sense the imitator can learn a word by +guessing whether a certain speech pattern present in the context means that a +specific task is to be executed. The imitator is however not assumed to know +that speech is relevant and has to figure this out on its own by looking at the +demonstrations: indeed, the architecture allows the robot to transparently also +learn tasks which should not be triggered by an acoustic word, but for example +by the color or position of an object or a gesture made by someone in the +environment. To demonstrate this ability to find the ... +","Imitation learning of motor primitives and language bootstrapping in + robots" +" The aim of this study is to show the importance of two classification +techniques, viz. decision tree and clustering, in prediction of learning +disabilities (LD) of school-age children. LDs affect about 10 percent of all +children enrolled in schools. The problems of children with specific learning +disabilities have been a cause of concern to parents and teachers for some +time. Decision trees and clustering are powerful and popular tools used for +classification and prediction in Data mining. Different rules extracted from +the decision tree are used for prediction of learning disabilities. Clustering +is the assignment of a set of observations into subsets, called clusters, which +are useful in finding the different signs and symptoms (attributes) present in +the LD affected child. In this paper, J48 algorithm is used for constructing +the decision tree and K-means algorithm is used for creating the clusters. By +applying these classification techniques, LD in any child can be identified. +","Significance of Classification Techniques in Prediction of Learning + Disabilities" +" The task of verifying the compatibility between interacting web services has +traditionally been limited to checking the compatibility of the interaction +protocol in terms of message sequences and the type of data being exchanged. +Since web services are developed largely in an uncoordinated way, different +services often use independently developed ontologies for the same domain +instead of adhering to a single ontology as standard. In this work we +investigate the approaches that can be taken by the server to verify the +possibility to reach a state with semantically inconsistent results during the +execution of a protocol with a client, if the client ontology is published. +Often database is used to store the actual data along with the ontologies +instead of storing the actual data as a part of the ontology description. It is +important to observe that at the current state of the database the semantic +conflict state may not be reached even if the verification done by the server +indicates the possibility of reaching a conflict state. A relational algebra +based decision procedure is also developed to incorporate the current state of +the client and the server databases in the overall verification procedure. +","Detecting Ontological Conflicts in Protocols between Semantic Web + Services" +" The paper proposes a numerically stable recursive algorithm for the exact +computation of the linear-chain conditional random field gradient. It operates +as a forward algorithm over the log-domain expectation semiring and has the +purpose of enhancing memory efficiency when applied to long observation +sequences. Unlike the traditional algorithm based on the forward-backward +recursions, the memory complexity of our algorithm does not depend on the +sequence length. The experiments on real data show that it can be useful for +the problems which deal with long sequences. +","Gradient Computation In Linear-Chain Conditional Random Fields Using The + Entropy Message Passing Algorithm" +" In this paper, a new reinforcement learning approach is proposed which is +based on a powerful concept named Active Learning Method (ALM) in modeling. ALM +expresses any multi-input-single-output system as a fuzzy combination of some +single-input-singleoutput systems. The proposed method is an actor-critic +system similar to Generalized Approximate Reasoning based Intelligent Control +(GARIC) structure to adapt the ALM by delayed reinforcement signals. Our system +uses Temporal Difference (TD) learning to model the behavior of useful actions +of a control system. The goodness of an action is modeled on Reward- +Penalty-Plane. IDS planes will be updated according to this plane. It is shown +that the system can learn with a predefined fuzzy system or without it (through +random actions). +",Reinforcement Learning Based on Active Learning Method +" An effective approach for energy conservation in wireless sensor networks is +scheduling sleep intervals for extraneous nodes while the remaining nodes stay +active to provide continuous service. For the sensor network to operate +successfully the active nodes must maintain both sensing coverage and network +connectivity, It proved before if the communication range of nodes is at least +twice the sensing range, complete coverage of a convex area implies +connectivity among the working set of nodes. In this paper we consider a +rectangular region A = a *b, such that R a R b s s {\pounds}, {\pounds}, where +s R is the sensing range of nodes. and put a constraint on minimum allowed +distance between nodes(s). according to this constraint we present a new lower +bound for communication range relative to sensing range of sensors(s 2 + 3 *R) +that complete coverage of considered area implies connectivity among the +working set of nodes; also we present a new distribution method, that satisfy +our constraint. +",A New Sufficient Condition for 1-Coverage to Imply Connectivity +" In this paper, we propose a new approach for recommender systems based on +target tracking by Kalman filtering. We assume that users and their seen +resources are vectors in the multidimensional space of the categories of the +resources. Knowing this space, we propose an algorithm based on a Kalman filter +to track users and to predict the best prediction of their future position in +the recommendation space. +","Target tracking in the recommender space: Toward a new recommender + system based on Kalman filtering" +" We investigate projection methods, for evaluating a linear approximation of +the value function of a policy in a Markov Decision Process context. We +consider two popular approaches, the one-step Temporal Difference fix-point +computation (TD(0)) and the Bellman Residual (BR) minimization. We describe +examples, where each method outperforms the other. We highlight a simple +relation between the objective function they minimize, and show that while BR +enjoys a performance guarantee, TD(0) does not in general. We then propose a +unified view in terms of oblique projections of the Bellman equation, which +substantially simplifies and extends the characterization of (schoknecht,2002) +and the recent analysis of (Yu & Bertsekas, 2008). Eventually, we describe some +simulations that suggest that if the TD(0) solution is usually slightly better +than the BR solution, its inherent numerical instability makes it very bad in +some cases, and thus worse on average. +","Should one compute the Temporal Difference fix point or minimize the + Bellman Residual? The unified oblique projection view" +" Graph coloring, also known as vertex coloring, considers the problem of +assigning colors to the nodes of a graph such that adjacent nodes do not share +the same color. The optimization version of the problem concerns the +minimization of the number of used colors. In this paper we deal with the +problem of finding valid colorings of graphs in a distributed way, that is, by +means of an algorithm that only uses local information for deciding the color +of the nodes. Such algorithms prescind from any central control. Due to the +fact that quite a few practical applications require to find colorings in a +distributed way, the interest in distributed algorithms for graph coloring has +been growing during the last decade. As an example consider wireless ad-hoc and +sensor networks, where tasks such as the assignment of frequencies or the +assignment of TDMA slots are strongly related to graph coloring. + The algorithm proposed in this paper is inspired by the calling behavior of +Japanese tree frogs. Male frogs use their calls to attract females. +Interestingly, groups of males that are located nearby each other desynchronize +their calls. This is because female frogs are only able to correctly localize +the male frogs when their calls are not too close in time. We experimentally +show that our algorithm is very competitive with the current state of the art, +using different sets of problem instances and comparing to one of the most +competitive algorithms from the literature. +","Distributed Graph Coloring: An Approach Based on the Calling Behavior of + Japanese Tree Frogs" +" This paper describes an application of Bayesian programming to the control of +an autonomous avatar in a multiplayer role-playing game (the example is based +on World of Warcraft). We model a particular task, which consists of choosing +what to do and to select which target in a situation where allies and foes are +present. We explain the model in Bayesian programming and show how we could +learn the conditional probabilities from data gathered during human-played +sessions. +",Bayesian Modeling of a Human MMORPG Player +" We present a probabilistic logic programming framework to reinforcement +learning, by integrating reinforce-ment learning, in POMDP environments, with +normal hybrid probabilistic logic programs with probabilistic answer set +seman-tics, that is capable of representing domain-specific knowledge. We +formally prove the correctness of our approach. We show that the complexity of +finding a policy for a reinforcement learning problem in our approach is +NP-complete. In addition, we show that any reinforcement learning problem can +be encoded as a classical logic program with answer set semantics. We also show +that a reinforcement learning problem can be encoded as a SAT problem. We +present a new high level action description language that allows the factored +representation of POMDP. Moreover, we modify the original model of POMDP so +that it be able to distinguish between knowledge producing actions and actions +that change the environment. +","Reinforcement Learning in Partially Observable Markov Decision Processes + using Hybrid Probabilistic Logic Programs" +" Biometrics is the science and technology of measuring and analyzing +biological data of human body, extracting a feature set from the acquired data, +and comparing this set against to the template set in the database. +Experimental studies show that Unimodal biometric systems had many +disadvantages regarding performance and accuracy. Multimodal biometric systems +perform better than unimodal biometric systems and are popular even more +complex also. We examine the accuracy and performance of multimodal biometric +authentication systems using state of the art Commercial Off- The-Shelf (COTS) +products. Here we discuss fingerprint and face biometric systems, decision and +fusion techniques used in these systems. We also discuss their advantage over +unimodal biometric systems. +",Multimodal Biometric Systems - Study to Improve Accuracy and Performance +" Uncertainty of decisions in safety-critical engineering applications can be +estimated on the basis of the Bayesian Markov Chain Monte Carlo (MCMC) +technique of averaging over decision models. The use of decision tree (DT) +models assists experts to interpret causal relations and find factors of the +uncertainty. Bayesian averaging also allows experts to estimate the uncertainty +accurately when a priori information on the favored structure of DTs is +available. Then an expert can select a single DT model, typically the Maximum a +Posteriori model, for interpretation purposes. Unfortunately, a priori +information on favored structure of DTs is not always available. For this +reason, we suggest a new prior on DTs for the Bayesian MCMC technique. We also +suggest a new procedure of selecting a single DT and describe an application +scenario. In our experiments on the Short-Term Conflict Alert data our +technique outperforms the existing Bayesian techniques in predictive accuracy +of the selected single DTs. +","A Bayesian Methodology for Estimating Uncertainty of Decisions in + Safety-Critical Systems" +" We examine the practicality for a user of using Answer Set Programming (ASP) +for representing logical formalisms. We choose as an example a formalism aiming +at capturing causal explanations from causal information. We provide an +implementation, showing the naturalness and relative efficiency of this +translation job. We are interested in the ease for writing an ASP program, in +accordance with the claimed ``declarative'' aspect of ASP. Limitations of the +earlier systems (poor data structure and difficulty in reusing pieces of +programs) made that in practice, the ``declarative aspect'' was more +theoretical than practical. We show how recent improvements in working ASP +systems facilitate a lot the translation, even if a few improvements could +still be useful. +",Using ASP with recent extensions for causal explanations +" There are a huge number of problems, from various areas, being solved by +reducing them to SAT. However, for many applications, translation into SAT is +performed by specialized, problem-specific tools. In this paper we describe a +new system for uniform solving of a wide class of problems by reducing them to +SAT. The system uses a new specification language URSA that combines imperative +and declarative programming paradigms. The reduction to SAT is defined +precisely by the semantics of the specification language. The domain of the +approach is wide (e.g., many NP-complete problems can be simply specified and +then solved by the system) and there are problems easily solvable by the +proposed system, while they can be hardly solved by using other programming +languages or constraint programming systems. So, the system can be seen not +only as a tool for solving problems by reducing them to SAT, but also as a +general-purpose constraint solving system (for finite domains). In this paper, +we also describe an open-source implementation of the described approach. The +performed experiments suggest that the system is competitive to +state-of-the-art related modelling systems. +",URSA: A System for Uniform Reduction to SAT +" SNOMED Clinical Terms (SNOMED CT) is one of the most widespread ontologies in +the life sciences, with more than 300,000 concepts and relationships, but is +distributed with no associated software tools. In this paper we present MySNOM, +a web-based SNOMED CT browser. MySNOM allows organizations to browse their own +distribution of SNOMED CT under a controlled environment, focuses on navigating +using the structure of SNOMED CT, and has diagramming capabilities. +",Are SNOMED CT Browsers Ready for Institutions? Introducing MySNOM +" In this paper we dealt with the comparison and linking between lexical +resources with domain knowledge provided by ontologies. It is one of the issues +for the combination of the Semantic Web Ontologies and Text Mining. We +investigated the relations between the linguistics oriented and domain-specific +semantics, by associating the GO biological process concepts to the FrameNet +semantic frames. The result shows the gaps between the linguistics-oriented and +domain-specific semantics on the classification of events and the grouping of +target words. The result provides valuable information for the improvement of +domain ontologies supporting for text mining systems. And also, it will result +in benefits to language understanding technology. +","A study on the relation between linguistics-oriented and domain-specific + semantics" +" Virtual e-Science infrastructures supporting Web-based scientific workflows +are an example for knowledge-intensive collaborative and weakly-structured +processes where the interaction with the human scientists during process +execution plays a central role. In this paper we propose the lightweight +dynamic user-friendly interaction with humans during execution of scientific +workflows via the low-barrier approach of Semantic Wikis as an intuitive +interface for non-technical scientists. Our Process Makna Semantic Wiki system +is a novel combination of an business process management system adapted for +scientific workflows with a Corporate Semantic Web Wiki user interface +supporting knowledge intensive human interaction tasks during scientific +workflow execution. +",Process Makna - A Semantic Wiki for Scientific Workflows +" ChemgaPedia is a multimedia, webbased eLearning service platform that +currently contains about 18.000 pages organized in 1.700 chapters covering the +complete bachelor studies in chemistry and related topics of chemistry, +pharmacy, and life sciences. The eLearning encyclopedia contains some 25.000 +media objects and the eLearning platform provides services such as virtual and +remote labs for experiments. With up to 350.000 users per month the platform is +the most frequently used scientific educational service in the German spoken +Internet. In this demo we show the benefit of mapping the static eLearning +contents of ChemgaPedia to a Linked Data representation for Semantic Chemistry +which allows for generating dynamic eLearning paths tailored to the semantic +profiles of the users. +","Use of semantic technologies for the development of a dynamic + trajectories generator in a Semantic Chemistry eLearning platform" +" This research applies ideas from argumentation theory in the context of +semantic wikis, aiming to provide support for structured-large scale +argumentation between human agents. The implemented prototype is exemplified by +modelling the MMR vaccine controversy. +",Using Semantic Wikis for Structured Argument in Medical Domain +" Creating a new Ontology: a Modular Approach +",Creating a new Ontology: a Modular Approach +" Research in the Life Sciences depends on the integration of large, +distributed and heterogeneous data sources and web services. The discovery of +which of these resources are the most appropriate to solve a given task is a +complex research question, since there is a large amount of plausible +candidates and there is little, mostly unstructured, metadata to be able to +decide among them.We contribute a semi-automatic approach,based on semantic +techniques, to assist researchers in the discovery of the most appropriate web +services to full a set of given requirements. +","A semantic approach for the requirement-driven discovery of web services + in the Life Sciences" +" Semantic wikis, wikis enhanced with Semantic Web technologies, are +appropriate systems for community-authored knowledge models. They are +particularly suitable for scientific collaboration. This paper details the +design principles ofWikiBridge, a semantic wiki. +",Scientific Collaborations: principles of WikiBridge Design +" We present Populous, a tool for gathering content with which to populate an +ontology. Domain experts need to add content, that is often repetitive in its +form, but without having to tackle the underlying ontological representation. +Populous presents users with a table based form in which columns are +constrained to take values from particular ontologies; the user can select a +concept from an ontology via its meaningful label to give a value for a given +entity attribute. Populated tables are mapped to patterns that can then be used +to automatically generate the ontology's content. Populous's contribution is in +the knowledge gathering stage of ontology development. It separates knowledge +gathering from the conceptualisation and also separates the user from the +standard ontology authoring environments. As a result, Populous can allow +knowledge to be gathered in a straight-forward manner that can then be used to +do mass production of ontology content. +",Populous: A tool for populating ontology templates +" In this work, we develop an intelligent user interface that allows users to +enter biomedical queries in a natural language, and that presents the answers +(possibly with explanations if requested) in a natural language. We develop a +rule layer over biomedical ontologies and databases, and use automated +reasoners to answer queries considering relevant parts of the rule layer. +",Querying Biomedical Ontologies in Natural Language using Answer Set +" There has been a long history of using fuzzy language equivalence to compare +the behavior of fuzzy systems, but the comparison at this level is too coarse. +Recently, a finer behavioral measure, bisimulation, has been introduced to +fuzzy finite automata. However, the results obtained are applicable only to +finite-state systems. In this paper, we consider bisimulation for general fuzzy +systems which may be infinite-state or infinite-event, by modeling them as +fuzzy transition systems. To help understand and check bisimulation, we +characterize it in three ways by enumerating whole transitions, comparing +individual transitions, and using a monotonic function. In addition, we address +composition operations, subsystems, quotients, and homomorphisms of fuzzy +transition systems and discuss their properties connected with bisimulation. +The results presented here are useful for comparing the behavior of general +fuzzy systems. In particular, this makes it possible to relate an infinite +fuzzy system to a finite one, which is easier to analyze, with the same +behavior. +",Bisimulations for fuzzy transition systems +" Fuzzy automata have long been accepted as a generalization of +nondeterministic finite automata. A closer examination, however, shows that the +fundamental property---nondeterminism---in nondeterministic finite automata has +not been well embodied in the generalization. In this paper, we introduce +nondeterministic fuzzy automata with or without $\el$-moves and fuzzy languages +recognized by them. Furthermore, we prove that (deterministic) fuzzy automata, +nondeterministic fuzzy automata, and nondeterministic fuzzy automata with +$\el$-moves are all equivalent in the sense that they recognize the same class +of fuzzy languages. +",Nondeterministic fuzzy automata +" The previous decade has brought a remarkable increase of the interest in +applications that deal with querying and mining of time series data. Many of +the research efforts in this context have focused on introducing new +representation methods for dimensionality reduction or novel similarity +measures for the underlying data. In the vast majority of cases, each +individual work introducing a particular method has made specific claims and, +aside from the occasional theoretical justifications, provided quantitative +experimental observations. However, for the most part, the comparative aspects +of these experiments were too narrowly focused on demonstrating the benefits of +the proposed methods over some of the previously introduced ones. In order to +provide a comprehensive validation, we conducted an extensive experimental +study re-implementing eight different time series representations and nine +similarity measures and their variants, and testing their effectiveness on +thirty-eight time series data sets from a wide variety of application domains. +In this paper, we give an overview of these different techniques and present +our comparative experimental findings regarding their effectiveness. In +addition to providing a unified validation of some of the existing +achievements, our experiments also indicate that, in some cases, certain claims +in the literature may be unduly optimistic. +","Experimental Comparison of Representation Methods and Distance Measures + for Time Series Data" +" In this paper, we propose a new approach for recommender systems based on +target tracking by Kalman filtering. We assume that users and their seen +resources are vectors in the multidimensional space of the categories of the +resources. Knowing this space, we propose an algorithm based on a Kalman filter +to track users and to predict the best prediction of their future position in +the recommendation space. +","A new Recommender system based on target tracking: a Kalman Filter + approach" +" Knowledge is attributed to human whose problem-solving behavior is subjective +and complex. In today's knowledge economy, the need to manage knowledge +produced by a community of actors cannot be overemphasized. This is due to the +fact that actors possess some level of tacit knowledge which is generally +difficult to articulate. Problem-solving requires searching and sharing of +knowledge among a group of actors in a particular context. Knowledge expressed +within the context of a problem resolution must be capitalized for future +reuse. In this paper, an approach that permits dynamic capitalization of +relevant and reliable actors' knowledge in solving decision problem following +Economic Intelligence process is proposed. Knowledge annotation method and +temporal attributes are used for handling the complexity in the communication +among actors and in contextualizing expressed knowledge. A prototype is built +to demonstrate the functionalities of a collaborative Knowledge Management +system based on this approach. It is tested with sample cases and the result +showed that dynamic capitalization leads to knowledge validation hence +increasing reliability of captured knowledge for reuse. The system can be +adapted to various domains +","Dynamic Capitalization and Visualization Strategy in Collaborative + Knowledge Management System for EI Process" +" The shift from industrial economy to knowledge economy in today's world has +revolutionalized strategic planning in organizations as well as their problem +solving approaches. The point of focus today is knowledge and service +production with more emphasis been laid on knowledge capital. Many +organizations are investing on tools that facilitate knowledge sharing among +their employees and they are as well promoting and encouraging collaboration +among their staff in order to build the organization's knowledge capital with +the ultimate goal of creating a lasting competitive advantage for their +organizations. One of the current leading approaches used for solving +organization's decision problem is the Economic Intelligence (EI) approach +which involves interactions among various actors called EI actors. These actors +collaborate to ensure the overall success of the decision problem solving +process. In the course of the collaboration, the actors express knowledge which +could be capitalized for future reuse. In this paper, we propose in the first +place, an annotation model for knowledge elicitation among EI actors. Because +of the need to build a knowledge capital, we also propose a dynamic knowledge +capitalisation approach for managing knowledge produced by the actors. Finally, +the need to manage the interactions and the interdependencies among +collaborating EI actors, led to our third proposition which constitute an +awareness mechanism for group work management. +","Dynamic Knowledge Capitalization through Annotation among Economic + Intelligence Actors in a Collaborative Environment" +" A new distance function dist(A,B) for fuzzy sets A and B is introduced. It is +based on the descriptive complexity, i.e., the number of bits (on average) that +are needed to describe an element in the symmetric difference of the two sets. +The distance gives the amount of additional information needed to describe any +one of the two sets given the other. We prove its mathematical properties and +perform pattern clustering on data based on this distance. +",Descriptive-complexity based distance for fuzzy sets +" Product take-back legislation forces manufacturers to bear the costs of +collection and disposal of products that have reached the end of their useful +lives. In order to reduce these costs, manufacturers can consider reuse, +remanufacturing and/or recycling of components as an alternative to disposal. +The implementation of such alternatives usually requires an appropriate reverse +supply chain management. With the concepts of reverse supply chain are gaining +popularity in practice, the use of artificial intelligence approaches in these +areas is also becoming popular. As a result, the purpose of this paper is to +give an overview of the recent publications concerning the application of +artificial intelligence techniques to reverse supply chain with emphasis on +certain types of product returns. +","Artificial Intelligence in Reverse Supply Chain Management: The State of + the Art" +" Intersections constitute one of the most dangerous elements in road systems. +Traffic signals remain the most common way to control traffic at high-volume +intersections and offer many opportunities to apply intelligent transportation +systems to make traffic more efficient and safe. This paper describes an +automated method to estimate the temporal exposure of road users crossing the +conflict zone to lateral collision with road users originating from a different +approach. This component is part of a larger system relying on video sensors to +provide queue lengths and spatial occupancy that are used for real time traffic +control and monitoring. The method is evaluated on data collected during a real +world experiment. +","Automatic Estimation of the Exposure to Lateral Collision in Signalized + Intersections using Video Sensors" +" A conservative class of constraint satisfaction problems CSPs is a class for +which membership is preserved under arbitrary domain reductions. Many +well-known tractable classes of CSPs are conservative. It is well known that +lexleader constraints may significantly reduce the number of solutions by +excluding symmetric solutions of CSPs. We show that adding certain lexleader +constraints to any instance of any conservative class of CSPs still allows us +to find all solutions with a time which is polynomial between successive +solutions. The time is polynomial in the total size of the instance and the +additional lexleader constraints. It is well known that for complete symmetry +breaking one may need an exponential number of lexleader constraints. However, +in practice, the number of additional lexleader constraints is typically +polynomial number in the size of the instance. For polynomially many lexleader +constraints, we may in general not have complete symmetry breaking but +polynomially many lexleader constraints may provide practically useful symmetry +breaking -- and they sometimes exclude super-exponentially many solutions. We +prove that for any instance from a conservative class, the time between finding +successive solutions of the instance with polynomially many additional +lexleader constraints is polynomial even in the size of the instance without +lexleaderconstraints. +",Symmetry Breaking with Polynomial Delay +" In the interpretation of experimental data, one is actually looking for +plausible explanations. We look for a measure of plausibility, with which we +can compare different possible explanations, and which can be combined when +there are different sets of data. This is contrasted to the conventional +measure for probabilities as well as to the proposed measure of possibilities. +We define what characteristics this measure of plausibility should have. + In getting to the conception of this measure, we explore the relation of +plausibility to abductive reasoning, and to Bayesian probabilities. We also +compare with the Dempster-Schaefer theory of evidence, which also has its own +definition for plausibility. Abduction can be associated with biconditionality +in inference rules, and this provides a platform to relate to the +Collins-Michalski theory of plausibility. Finally, using a formalism for wiring +logic onto Hopfield neural networks, we ask if this is relevant in obtaining +this measure. +",Looking for plausibility +" This article deals with Part family formation problem which is believed to be +moderately complicated to be solved in polynomial time in the vicinity of Group +Technology (GT). In the past literature researchers investigated that the part +family formation techniques are principally based on production flow analysis +(PFA) which usually considers operational requirements, sequences and time. +Part Coding Analysis (PCA) is merely considered in GT which is believed to be +the proficient method to identify the part families. PCA classifies parts by +allotting them to different families based on their resemblances in: (1) design +characteristics such as shape and size, and/or (2) manufacturing +characteristics (machining requirements). A novel approach based on simulated +annealing namely SAPFOCS is adopted in this study to develop effective part +families exploiting the PCA technique. Thereafter Taguchi's orthogonal design +method is employed to solve the critical issues on the subject of parameters +selection for the proposed metaheuristic algorithm. The adopted technique is +therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9 +and the obtained results are compared with C-Linkage clustering technique. The +experimental results reported that the proposed metaheuristic algorithm is +extremely effective in terms of the quality of the solution obtained and has +outperformed C-Linkage algorithm in most instances. +","SAPFOCS: a metaheuristic based approach to part family formation + problems in group technology" +" Using the notion of an elementary loop, Gebser and Schaub refined the theorem +on loop formulas due to Lin and Zhao by considering loop formulas of elementary +loops only. In this article, we reformulate their definition of an elementary +loop, extend it to disjunctive programs, and study several properties of +elementary loops, including how maximal elementary loops are related to minimal +unfounded sets. The results provide useful insights into the stable model +semantics in terms of elementary loops. For a nondisjunctive program, using a +graph-theoretic characterization of an elementary loop, we show that the +problem of recognizing an elementary loop is tractable. On the other hand, we +show that the corresponding problem is {\sf coNP}-complete for a disjunctive +program. Based on the notion of an elementary loop, we present the class of +Head-Elementary-loop-Free (HEF) programs, which strictly generalizes the class +of Head-Cycle-Free (HCF) programs due to Ben-Eliyahu and Dechter. Like an HCF +program, an HEF program can be turned into an equivalent nondisjunctive program +in polynomial time by shifting head atoms into the body. +",On Elementary Loops of Logic Programs +" In this paper we introduce a method for extending binary qualitative +direction calculi with adjustable granularity like OPRAm or the star calculus +with a granular distance concept. This method is similar to the concept of +extending points with an internal reference direction to get oriented points +which are the basic entities in the OPRAm calculus. Even if the spatial objects +are from a geometrical point of view infinitesimal small points locally +available reference measures are attached. In the case of OPRAm, a reference +direction is attached. The same principle works also with local reference +distances which are called elevations. The principle of attaching references +features to a point is called hidden feature attachment. +","Extending Binary Qualitative Direction Calculi with a Granular Distance + Concept: Hidden Feature Attachment" +" In video games, virtual characters' decision systems often use a simplified +representation of the world. To increase both their autonomy and believability +we want those characters to be able to learn this representation from human +players. We propose to use a model called growing neural gas to learn by +imitation the topology of the environment. The implementation of the model, the +modifications and the parameters we used are detailed. Then, the quality of the +learned representations and their evolution during the learning are studied +using different measures. Improvements for the growing neural gas to give more +information to the character's model are given in the conclusion. +","Learning a Representation of a Believable Virtual Character's + Environment with an Imitation Algorithm" +" Current work in planning with preferences assume that the user's preference +models are completely specified and aim to search for a single solution plan. +In many real-world planning scenarios, however, the user probably cannot +provide any information about her desired plans, or in some cases can only +express partial preferences. In such situations, the planner has to present not +only one but a set of plans to the user, with the hope that some of them are +similar to the plan she prefers. We first propose the usage of different +measures to capture quality of plan sets that are suitable for such scenarios: +domain-independent distance measures defined based on plan elements (actions, +states, causal links) if no knowledge of the user's preferences is given, and +the Integrated Convex Preference measure in case the user's partial preference +is provided. We then investigate various heuristic approaches to find set of +plans according to these measures, and present empirical results demonstrating +the promise of our approach. +",Planning with Partial Preference Models +" Performing effective preference-based data retrieval requires detailed and +preferentially meaningful structurized information about the current user as +well as the items under consideration. A common problem is that representations +of items often only consist of mere technical attributes, which do not resemble +human perception. This is particularly true for integral items such as movies +or songs. It is often claimed that meaningful item features could be extracted +from collaborative rating data, which is becoming available through social +networking services. However, there is only anecdotal evidence supporting this +claim; but if it is true, the extracted information could very valuable for +preference-based data retrieval. In this paper, we propose a methodology to +systematically check this common claim. We performed a preliminary +investigation on a large collection of movie ratings and present initial +evidence. +",Extracting Features from Ratings: The Role of Factor Models +" We suggest a procedure that is relevant both to electronic performance and +human psychology, so that the creative logic and the respect for human nature +appear in a good agreement. The idea is to create an electronic card containing +basic information about a person's psychological behavior in order to make it +possible to quickly decide about the suitability of one for another. This +""psychological electronics"" approach could be tested via student projects. +","The ""psychological map of the brain"", as a personal information card + (file), - a project for the student of the 21st century" +" Meaning negotiation (MN) is the general process with which agents reach an +agreement about the meaning of a set of terms. Artificial Intelligence scholars +have dealt with the problem of MN by means of argumentations schemes, beliefs +merging and information fusion operators, and ontology alignment but the +proposed approaches depend upon the number of participants. In this paper, we +give a general model of MN for an arbitrary number of agents, in which each +participant discusses with the others her viewpoint by exhibiting it in an +actual set of constraints on the meaning of the negotiated terms. We call this +presentation of individual viewpoints an angle. The agents do not aim at +forming a common viewpoint but, instead, at agreeing about an acceptable common +angle. We analyze separately the process of MN by two agents (\emph{bilateral} +or \emph{pairwise} MN) and by more than two agents (\emph{multiparty} MN), and +we use game theoretic models to understand how the process develops in both +cases: the models are Bargaining Game for bilateral MN and English Auction for +multiparty MN. We formalize the process of reaching such an agreement by giving +a deduction system that comprises of rules that are consistent and adequate for +representing MN. +",Meaning Negotiation as Inference +" Although some information-theoretic measures of uncertainty or granularity +have been proposed in rough set theory, these measures are only dependent on +the underlying partition and the cardinality of the universe, independent of +the lower and upper approximations. It seems somewhat unreasonable since the +basic idea of rough set theory aims at describing vague concepts by the lower +and upper approximations. In this paper, we thus define new +information-theoretic entropy and co-entropy functions associated to the +partition and the approximations to measure the uncertainty and granularity of +an approximation space. After introducing the novel notions of entropy and +co-entropy, we then examine their properties. In particular, we discuss the +relationship of co-entropies between different universes. The theoretical +development is accompanied by illustrative numerical examples. +",Information-theoretic measures associated with rough set approximations +" One of the main research areas in Artificial Intelligence is the coding of +agents (programs) which are able to learn by themselves in any situation. This +means that agents must be useful for purposes other than those they were +created for, as, for example, playing chess. In this way we try to get closer +to the pristine goal of Artificial Intelligence. One of the problems to decide +whether an agent is really intelligent or not is the measurement of its +intelligence, since there is currently no way to measure it in a reliable way. +The purpose of this project is to create an interpreter that allows for the +execution of several environments, including those which are generated +randomly, so that an agent (a person or a program) can interact with them. Once +the interaction between the agent and the environment is over, the interpreter +will measure the intelligence of the agent according to the actions, states and +rewards the agent has undergone inside the environment during the test. As a +result we will be able to measure agents' intelligence in any possible +environment, and to make comparisons between several agents, in order to +determine which of them is the most intelligent. In order to perform the tests, +the interpreter must be able to randomly generate environments that are really +useful to measure agents' intelligence, since not any randomly generated +environment will serve that purpose. +",An architecture for the evaluation of intelligent systems +" The World Wide Web (WWW) allows the people to share the information (data) +from the large database repositories globally. The amount of information grows +billions of databases. We need to search the information will specialize tools +known generically search engine. There are many of search engines available +today, retrieving meaningful information is difficult. However to overcome this +problem in search engines to retrieve meaningful information intelligently, +semantic web technologies are playing a major role. In this paper we present +survey on the search engine generations and the role of search engines in +intelligent web and semantic search technologies. +",Intelligent Semantic Web Search Engines: A Brief Survey +" The analysis of online least squares estimation is at the heart of many +stochastic sequential decision making problems. We employ tools from the +self-normalized processes to provide a simple and self-contained proof of a +tail bound of a vector-valued martingale. We use the bound to construct a new +tighter confidence sets for the least squares estimate. + We apply the confidence sets to several online decision problems, such as the +multi-armed and the linearly parametrized bandit problems. The confidence sets +are potentially applicable to other problems such as sleeping bandits, +generalized linear bandits, and other linear control problems. + We improve the regret bound of the Upper Confidence Bound (UCB) algorithm of +Auer et al. (2002) and show that its regret is with high-probability a problem +dependent constant. In the case of linear bandits (Dani et al., 2008), we +improve the problem dependent bound in the dimension and number of time steps. +Furthermore, as opposed to the previous result, we prove that our bound holds +for small sample sizes, and at the same time the worst case bound is improved +by a logarithmic factor and the constant is improved. +","Online Least Squares Estimation with Self-Normalized Processes: An + Application to Bandit Problems" +" This paper presents a new multi-objective hybrid model that makes cooperation +between the strength of research of neighborhood methods presented by the tabu +search (TS) and the important exploration capacity of evolutionary algorithm. +This model was implemented and tested in benchmark functions (ZDT1, ZDT2, and +ZDT3), using a network of computers. +","Hybrid Model for Solving Multi-Objective Problems Using Evolutionary + Algorithm and Tabu Search" +" An algorithm running in O(1.1995n) is presented for counting models for exact +satisfiability formulae(#XSAT). This is faster than the previously best +algorithm which runs in O(1.2190n). In order to improve the efficiency of the +algorithm, a new principle, i.e. the common literals principle, is addressed to +simplify formulae. This allows us to eliminate more common literals. In +addition, we firstly inject the resolution principles into solving #XSAT +problem, and therefore this further improves the efficiency of the algorithm. +",New Worst-Case Upper Bound for #XSAT +" Rules in logic programming encode information about mutual interdependencies +between literals that is not captured by any of the commonly used semantics. +This information becomes essential as soon as a program needs to be modified or +further manipulated. + We argue that, in these cases, a program should not be viewed solely as the +set of its models. Instead, it should be viewed and manipulated as the set of +sets of models of each rule inside it. With this in mind, we investigate and +highlight relations between the SE-model semantics and individual rules. We +identify a set of representatives of rule equivalence classes induced by +SE-models, and so pinpoint the exact expressivity of this semantics with +respect to a single rule. We also characterise the class of sets of +SE-interpretations representable by a single rule. Finally, we discuss the +introduction of two notions of equivalence, both stronger than strong +equivalence [1] and weaker than strong update equivalence [2], which seem more +suitable whenever the dependency information found in rules is of interest. +",Back and Forth Between Rules and SE-Models (Extended Version) +" The global objective of this work is to provide practical optimization +methods to companies involved in inventory routing problems, taking into +account this new type of data. Also, companies are sometimes not able to deal +with changing plans every period and would like to adopt regular structures for +serving customers. +","Practical inventory routing: A problem definition and an optimization + method" +" Identification of critical or weak buses for a given operating condition is +an important task in the load dispatch centre. It has become more vital in view +of the threat of voltage instability leading to voltage collapse. This paper +presents a fuzzy approach for ranking critical buses in a power system under +normal and network contingencies based on Line Flow index and voltage profiles +at load buses. The Line Flow index determines the maximum load that is possible +to be connected to a bus in order to maintain stability before the system +reaches its bifurcation point. Line Flow index (LF index) along with voltage +profiles at the load buses are represented in Fuzzy Set notation. Further they +are evaluated using fuzzy rules to compute Criticality Index. Based on this +index, critical buses are ranked. The bus with highest rank is the weakest bus +as it can withstand a small amount of load before causing voltage collapse. The +proposed method is tested on Five Bus Test System. +","Fuzzy Approach to Critical Bus Ranking under Normal and Line Outage + Contingencies" +" The process-based semantic composition of Web Services is gaining a +considerable momentum as an approach for the effective integration of +distributed, heterogeneous, and autonomous applications. To compose Web +Services semantically, we need an ontology. There are several ways of inserting +semantics in Web Services. One of them consists of using description languages +like OWL-S. In this paper, we introduce our work which consists in the +proposition of a new model and the use of semantic matching technology for +semantic and dynamic composition of ebXML business processes. +","An Agent Based Architecture (Using Planning) for Dynamic and Semantic + Web Services Composition in an EBXML Context" +" The problem of business-IT alignment is of widespread economic concern. + As one way of addressing the problem, this paper describes an online system +that functions as a kind of Wiki -- one that supports the collaborative writing +and running of business and scientific applications, as rules in open +vocabulary, executable English, using a browser. + Since the rules are in English, they are indexed by Google and other search +engines. This is useful when looking for rules for a task that one has in mind. + The design of the system integrates the semantics of data, with a semantics +of an inference method, and also with the meanings of English sentences. As +such, the system has functionality that may be useful for the Rules, Logic, +Proof and Trust requirements of the Semantic Web. + The system accepts rules, and small numbers of facts, typed or copy-pasted +directly into a browser. One can then run the rules, again using a browser. For +larger amounts of data, the system uses information in the rules to +automatically generate and run SQL over networked databases. From a few highly +declarative rules, the system typically generates SQL that would be too +complicated to write reliably by hand. However, the system can explain its +results in step-by-step hypertexted English, at the business or scientific +level + As befits a Wiki, shared use of the system is free. +","A Wiki for Business Rules in Open Vocabulary, Executable English" +" We propose a long-term memory design for artificial general intelligence +based on Solomonoff's incremental machine learning methods. We use R5RS Scheme +and its standard library with a few omissions as the reference machine. We +introduce a Levin Search variant based on Stochastic Context Free Grammar +together with four synergistic update algorithms that use the same grammar as a +guiding probability distribution of programs. The update algorithms include +adjusting production probabilities, re-using previous solutions, learning +programming idioms and discovery of frequent subprograms. Experiments with two +training sequences demonstrate that our approach to incremental learning is +effective. +",Teraflop-scale Incremental Machine Learning +" In Artificial Intelligence with Coalition Structure Generation (CSG) one +refers to those cooperative complex problems that require to find an optimal +partition, maximising a social welfare, of a set of entities involved in a +system into exhaustive and disjoint coalitions. The solution of the CSG problem +finds applications in many fields such as Machine Learning (covering machines, +clustering), Data Mining (decision tree, discretization), Graph Theory, Natural +Language Processing (aggregation), Semantic Web (service composition), and +Bioinformatics. The problem of finding the optimal coalition structure is +NP-complete. In this paper we present a greedy adaptive search procedure +(GRASP) with path-relinking to efficiently search the space of coalition +structures. Experiments and comparisons to other algorithms prove the validity +of the proposed method in solving this hard combinatorial problem. +",GRASP and path-relinking for Coalition Structure Generation +" Signature used as a biometric is implemented in various systems as well as +every signature signed by each person is distinct at the same time. So, it is +very important to have a computerized signature verification system. In offline +signature verification system dynamic features are not available obviously, but +one can use a signature as an image and apply image processing techniques to +make an effective offline signature verification system. Author proposes a +intelligent network used directional feature and energy density both as inputs +to the same network and classifies the signature. Neural network is used as a +classifier for this system. The results are compared with both the very basic +energy density method and a simple directional feature method of offline +signature verification system and this proposed new network is found very +effective as compared to the above two methods, specially for less number of +training samples, which can be implemented practically. +","A Directional Feature with Energy based Offline Signature Verification + Network" +" Some recent works in conditional planning have proposed reachability +heuristics to improve planner scalability, but many lack a formal description +of the properties of their distance estimates. To place previous work in +context and extend work on heuristics for conditional planning, we provide a +formal basis for distance estimates between belief states. We give a definition +for the distance between belief states that relies on aggregating underlying +state distance measures. We give several techniques to aggregate state +distances and their associated properties. Many existing heuristics exhibit a +subset of the properties, but in order to provide a standardized comparison we +present several generalizations of planning graph heuristics that are used in a +single planner. We compliment our belief state distance estimate framework by +also investigating efficient planning graph data structures that incorporate +BDDs to compute the most effective heuristics. + We developed two planners to serve as test-beds for our investigation. The +first, CAltAlt, is a conformant regression planner that uses A* search. The +second, POND, is a conditional progression planner that uses AO* search. We +show the relative effectiveness of our heuristic techniques within these +planners. We also compare the performance of these planners with several state +of the art approaches in conditional planning. +",Planning Graph Heuristics for Belief Space Search +" Natural Immune system plays a vital role in the survival of the all living +being. It provides a mechanism to defend itself from external predates making +it consistent systems, capable of adapting itself for survival incase of +changes. The human immune system has motivated scientists and engineers for +finding powerful information processing algorithms that has solved complex +engineering tasks. This paper explores one of the various possibilities for +solving problem in a Multiagent scenario wherein multiple robots are deployed +to achieve a goal collectively. The final goal is dependent on the performance +of individual robot and its survival without having to lose its energy beyond a +predetermined threshold value by deploying an evolutionary computational +technique otherwise called the artificial immune system that imitates the +biological immune system. +","An Artificial Immune System Model for Multi-Agents Resource Sharing in + Distributed Environments" +" Most machine learning tools work with a single table where each row is an +instance and each column is an attribute. Each cell of the table contains an +attribute value for an instance. This representation prevents one important +form of learning, which is, classification based on groups of correlated +records, such as multiple exams of a single patient, internet customer +preferences, weather forecast or prediction of sea conditions for a given day. +To some extent, relational learning methods, such as inductive logic +programming, can capture this correlation through the use of intensional +predicates added to the background knowledge. In this work, we propose SPPAM, +an algorithm that aggregates past observations in one single record. We show +that applying SPPAM to the original correlated data, before the learning task, +can produce classifiers that are better than the ones trained using all +records. +",SPPAM - Statistical PreProcessing AlgorithM +" An emotional version of Sapir-Whorf hypothesis suggests that differences in +language emotionalities influence differences among cultures no less than +conceptual differences. Conceptual contents of languages and cultures to +significant extent are determined by words and their semantic differences; +these could be borrowed among languages and exchanged among cultures. Emotional +differences, as suggested in the paper, are related to grammar and mostly +cannot be borrowed. Conceptual and emotional mechanisms of languages are +considered here along with their functions in the mind and cultural evolution. +A fundamental contradiction in human mind is considered: language evolution +requires reduced emotionality, but ""too low"" emotionality makes language +""irrelevant to life,"" disconnected from sensory-motor experience. Neural +mechanisms of these processes are suggested as well as their mathematical +models: the knowledge instinct, the language instinct, the dual model +connecting language and cognition, dynamic logic, neural modeling fields. +Mathematical results are related to cognitive science, linguistics, and +psychology. Experimental evidence and theoretical arguments are discussed. +Approximate equations for evolution of human minds and cultures are obtained. +Their solutions identify three types of cultures: ""conceptual""-pragmatic +cultures, in which emotionality of language is reduced and differentiation +overtakes synthesis resulting in fast evolution at the price of uncertainty of +values, self doubts, and internal crises; ""traditional-emotional"" cultures +where differentiation lags behind synthesis, resulting in cultural stability at +the price of stagnation; and ""multi-cultural"" societies combining fast cultural +evolution and stability. Unsolved problems and future theoretical and +experimental directions are discussed. +","Language, Emotions, and Cultures: Emotional Sapir-Whorf Hypothesis" +" Knowledge compilation is an approach to tackle the computational +intractability of general reasoning problems. According to this approach, +knowledge bases are converted off-line into a target compilation language which +is tractable for on-line querying. Reduced ordered binary decision diagram +(ROBDD) is one of the most influential target languages. We generalize ROBDD by +associating some implied literals in each node and the new language is called +reduced ordered binary decision diagram with implied literals (ROBDD-L). Then +we discuss a kind of subsets of ROBDD-L called ROBDD-i with precisely i implied +literals (0 \leq i \leq \infty). In particular, ROBDD-0 is isomorphic to ROBDD; +ROBDD-\infty requires that each node should be associated by the implied +literals as many as possible. We show that ROBDD-i has uniqueness over some +specific variables order, and ROBDD-\infty is the most succinct subset in +ROBDD-L and can meet most of the querying requirements involved in the +knowledge compilation map. Finally, we propose an ROBDD-i compilation algorithm +for any i and a ROBDD-\infty compilation algorithm. Based on them, we implement +a ROBDD-L package called BDDjLu and then get some conclusions from preliminary +experimental results: ROBDD-\infty is obviously smaller than ROBDD for all +benchmarks; ROBDD-\infty is smaller than the d-DNNF the benchmarks whose +compilation results are relatively small; it seems that it is better to +transform ROBDDs-\infty into FBDDs and ROBDDs rather than straight compile the +benchmarks. +","Reduced Ordered Binary Decision Diagram with Implied Literals: A New + knowledge Compilation Approach" +" CHRONIOUS is an Open, Ubiquitous and Adaptive Chronic Disease Management +Platform for Chronic Obstructive Pulmonary Disease(COPD) Chronic Kidney Disease +(CKD) and Renal Insufficiency. It consists of several modules: an ontology +based literature search engine, a rule based decision support system, remote +sensors interacting with lifestyle interfaces (PDA, monitor touchscreen) and a +machine learning module. All these modules interact each other to allow the +monitoring of two types of chronic diseases and to help clinician in taking +decision for cure purpose. This paper illustrates how some machine learning +algorithms and a rule based decision support system can be used in smart +devices, to monitor chronic patient. We will analyse how a set of machine +learning algorithms can be used in smart devices to alert the clinician in case +of a patient health condition worsening trend. +","Using Soft Computer Techniques on Smart Devices for Monitoring Chronic + Diseases: the CHRONIOUS case" +" We show that several important resource allocation problems in wireless +networks fit within the common framework of Constraint Satisfaction Problems +(CSPs). Inspired by the requirements of these applications, where variables are +located at distinct network devices that may not be able to communicate but may +interfere, we define natural criteria that a CSP solver must possess in order +to be practical. We term these algorithms decentralized CSP solvers. The best +known CSP solvers were designed for centralized problems and do not meet these +criteria. We introduce a stochastic decentralized CSP solver and prove that it +will find a solution in almost surely finite time, should one exist, also +showing it has many practically desirable properties. We benchmark the +algorithm's performance on a well-studied class of CSPs, random k-SAT, +illustrating that the time the algorithm takes to find a satisfying assignment +is competitive with stochastic centralized solvers on problems with order a +thousand variables despite its decentralized nature. We demonstrate the +solver's practical utility for the problems that motivated its introduction by +using it to find a non-interfering channel allocation for a network formed from +data from downtown Manhattan. +",Decentralized Constraint Satisfaction +" this paper presents an enhancement of the medial axis algorithm to be used +for finding the optimal shortest path for developed cognitive map. The +cognitive map has been developed, based on the architectural blueprint maps. +The idea for using the medial-axis is to find main path central pixels; each +center pixel represents the center distance between two side boarder pixels. +The need for these pixels in the algorithm comes from the need of building a +network of nodes for the path, where each node represents a turning in the real +world (left, right, critical left, critical right...). The algorithm also +ignores from finding the center pixels paths that are too small for intelligent +robot navigation. The Idea of this algorithm is to find the possible shortest +path between start and end points. The goal of this research is to extract a +simple, robust representation of the shape of the cognitive map together with +the optimal shortest path between start and end points. The intelligent robot +will use this algorithm in order to decrease the time that is needed for +sweeping the targeted building. +",Finding Shortest Path for Developed Cognitive Map Using Medial Axis +" One of the first step in the realization of an automatic system of check +recognition is the extraction of the handwritten area. We propose in this paper +an hybrid method to extract these areas. This method is based on digit +recognition by Fourier descriptors and different steps of colored image +processing . It requires the bank recognition of its code which is located in +the check marking band as well as the handwritten color recognition by the +method of difference of histograms. The areas extraction is then carried out by +the use of some mathematical morphology tools. +","Extraction of handwritten areas from colored image of bank checks by an + hybrid method" +" Recently, several researchers have found that cost-based satisficing search +with A* often runs into problems. Although some ""work arounds"" have been +proposed to ameliorate the problem, there has not been any concerted effort to +pinpoint its origin. In this paper, we argue that the origins can be traced +back to the wide variance in action costs that is observed in most planning +domains. We show that such cost variance misleads A* search, and that this is +no trifling detail or accidental phenomenon, but a systemic weakness of the +very concept of ""cost-based evaluation functions + systematic search + +combinatorial graphs"". We show that satisficing search with sized-based +evaluation functions is largely immune to this problem. +",Cost Based Satisficing Search Considered Harmful +" We propose AllDiffPrecedence, a new global constraint that combines together +an AllDifferent constraint with precedence constraints that strictly order +given pairs of variables. We identify a number of applications for this global +constraint including instruction scheduling and symmetry breaking. We give an +efficient propagation algorithm that enforces bounds consistency on this global +constraint. We show how to implement this propagator using a decomposition that +extends the bounds consistency enforcing decomposition proposed for the +AllDifferent constraint. Finally, we prove that enforcing domain consistency on +this global constraint is NP-hard in general. +",The AllDifferent Constraint with Precedences +" Ontologies and rules are usually loosely coupled in knowledge representation +formalisms. In fact, ontologies use open-world reasoning while the leading +semantics for rules use non-monotonic, closed-world reasoning. One exception is +the tightly-coupled framework of Minimal Knowledge and Negation as Failure +(MKNF), which allows statements about individuals to be jointly derived via +entailment from an ontology and inferences from rules. Nonetheless, the +practical usefulness of MKNF has not always been clear, although recent work +has formalized a general resolution-based method for querying MKNF when rules +are taken to have the well-founded semantics, and the ontology is modeled by a +general oracle. That work leaves open what algorithms should be used to relate +the entailments of the ontology and the inferences of rules. In this paper we +provide such algorithms, and describe the implementation of a query-driven +system, CDF-Rules, for hybrid knowledge bases combining both (non-monotonic) +rules under the well-founded semantics and a (monotonic) ontology, represented +by a CDF Type-1 (ALQ) theory. To appear in Theory and Practice of Logic +Programming (TPLP) +","A Goal-Directed Implementation of Query Answering for Hybrid MKNF + Knowledge Bases" +" BoolVar/PB is an open source java library dedicated to the translation of +pseudo-Boolean constraints into CNF formulae. Input constraints can be +categorized with tags. Several encoding schemes are implemented in a way that +each input constraint can be translated using one or several encoders, +according to the related tags. The library can be easily extended by adding new +encoders and / or new output formats. +","BoolVar/PB v1.0, a java library for translating pseudo-Boolean + constraints into CNF formulae" +" In the present paper, we try to propose a self-similar network theory for the +basic understanding. By extending the natural languages to a kind of so called +idealy sufficient language, we can proceed a few steps to the investigation of +the language searching and the language understanding of AI. + Image understanding, and the familiarity of the brain to the surrounding +environment are also discussed. Group effects are discussed by addressing the +essense of the power of influences, and constructing the influence network of a +society. We also give a discussion of inspirations. +",On Understanding and Machine Understanding +" Phase transitions in many complex combinational problems have been widely +studied in the past decade. In this paper, we investigate phase transitions in +the knowledge compilation empirically, where DFA, OBDD and d-DNNF are chosen as +the target languages to compile random k-SAT instances. We perform intensive +experiments to analyze the sizes of compilation results and draw the following +conclusions: there exists an easy-hard-easy pattern in compilations; the peak +point of sizes in the pattern is only related to the ratio of the number of +clauses to that of variables when k is fixed, regardless of target languages; +most sizes of compilation results increase exponentially with the number of +variables growing, but there also exists a phase transition that separates a +polynomial-increment region from the exponential-increment region; Moreover, we +explain why the phase transition in compilations occurs by analyzing +microstructures of DFAs, and conclude that a kind of solution +interchangeability with more than 2 variables has a sharp transition near the +peak point of the easy-hard-easy pattern, and thus it has a great impact on +sizes of DFAs. +",Phase Transitions in Knowledge Compilation: an Experimental Study +" A definition of intelligence is given in terms of performance that can be +quantitatively measured. In this study, we have presented a conceptual model of +Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve +this goal, we have introduced several kinds of agents that exhibit intelligent +features. These are the Management agent, internal agent, External Agent, +Watcher agent and Report agent. Metrics and measurements are suggested for +evaluating the performance of Automatic Vehicle Checking Agent (VCA). Calibrate +data and test facilities are suggested to facilitate the development of +intelligent systems. +",Automatic Vehicle Checking Agent (VCA) +" This paper presents the design and development of a proposed rule based +Decision Support System that will help students in selecting the best suitable +faculty/major decision while taking admission in Gomal University, Dera Ismail +Khan, Pakistan. The basic idea of our approach is to design a model for testing +and measuring the student capabilities like intelligence, understanding, +comprehension, mathematical concepts plus his/her past academic record plus +his/her intelligence level, and applying the module results to a rule-based +decision support system to determine the compatibility of those capabilities +with the available faculties/majors in Gomal University. The result is shown as +a list of suggested faculties/majors with the student capabilities and +abilities. +","A Proposed Decision Support System/Expert System for Guiding Fresh + Students in Selecting a Faculty in Gomal University, Pakistan" +" Heuristics are crucial tools in decreasing search effort in varied fields of +AI. In order to be effective, a heuristic must be efficient to compute, as well +as provide useful information to the search algorithm. However, some well-known +heuristics which do well in reducing backtracking are so heavy that the gain of +deploying them in a search algorithm might be outweighed by their overhead. + We propose a rational metareasoning approach to decide when to deploy +heuristics, using CSP backtracking search as a case study. In particular, a +value of information approach is taken to adaptive deployment of solution-count +estimation heuristics for value ordering. Empirical results show that indeed +the proposed mechanism successfully balances the tradeoff between decreasing +backtracking and heuristic computational overhead, resulting in a significant +overall search time reduction. +",Rational Deployment of CSP Heuristics +" Regularization is a well studied problem in the context of neural networks. +It is usually used to improve the generalization performance when the number of +input samples is relatively small or heavily contaminated with noise. The +regularization of a parametric model can be achieved in different manners some +of which are early stopping (Morgan and Bourlard, 1990), weight decay, output +smoothing that are used to avoid overfitting during the training of the +considered model. From a Bayesian point of view, many regularization techniques +correspond to imposing certain prior distributions on model parameters (Krogh +and Hertz, 1991). Using Bishop's approximation (Bishop, 1995) of the objective +function when a restricted type of noise is added to the input of a parametric +function, we derive the higher order terms of the Taylor expansion and analyze +the coefficients of the regularization terms induced by the noisy input. In +particular we study the effect of penalizing the Hessian of the mapping +function with respect to the input in terms of generalization performance. We +also show how we can control independently this coefficient by explicitly +penalizing the Jacobian of the mapping function on corrupted inputs. +","Adding noise to the input of a model trained with a regularized + objective" +" We solve constraint satisfaction problems through translation to answer set +programming (ASP). Our reformulations have the property that unit-propagation +in the ASP solver achieves well defined local consistency properties like arc, +bound and range consistency. Experiments demonstrate the computational value of +this approach. +",Translation-based Constraint Answer Set Solving +" Recent papers address the issue of updating the instance level of knowledge +bases expressed in Description Logic following a model-based approach. One of +the outcomes of these papers is that the result of updating a knowledge base K +is generally not expressible in the Description Logic used to express K. In +this paper we introduce a formula-based approach to this problem, by revisiting +some research work on formula-based updates developed in the '80s, in +particular the WIDTIO (When In Doubt, Throw It Out) approach. We show that our +operator enjoys desirable properties, including that both insertions and +deletions according to such operator can be expressed in the DL used for the +original KB. Also, we present polynomial time algorithms for the evolution of +the instance level knowledge bases expressed in the most expressive Description +Logics of the DL-lite family. +",On the evolution of the instance level of DL-lite knowledge bases +" We present in this paper a novel approach for training deterministic +auto-encoders. We show that by adding a well chosen penalty term to the +classical reconstruction cost function, we can achieve results that equal or +surpass those attained by other regularized auto-encoders as well as denoising +auto-encoders on a range of datasets. This penalty term corresponds to the +Frobenius norm of the Jacobian matrix of the encoder activations with respect +to the input. We show that this penalty term results in a localized space +contraction which in turn yields robust features on the activation layer. +Furthermore, we show how this penalty term is related to both regularized +auto-encoders and denoising encoders and how it can be seen as a link between +deterministic and non-deterministic auto-encoders. We find empirically that +this penalty helps to carve a representation that better captures the local +directions of variation dictated by the data, corresponding to a +lower-dimensional non-linear manifold, while being more invariant to the vast +majority of directions orthogonal to the manifold. Finally, we show that by +using the learned features to initialize a MLP, we achieve state of the art +classification error on a range of datasets, surpassing other methods of +pre-training. +",Learning invariant features through local space contraction +" The study of arguments as abstract entities and their interaction as +introduced by Dung (Artificial Intelligence 177, 1995) has become one of the +most active research branches within Artificial Intelligence and Reasoning. A +main issue for abstract argumentation systems is the selection of acceptable +sets of arguments. Value-based argumentation, as introduced by Bench-Capon (J. +Logic Comput. 13, 2003), extends Dung's framework. It takes into account the +relative strength of arguments with respect to some ranking representing an +audience: an argument is subjectively accepted if it is accepted with respect +to some audience, it is objectively accepted if it is accepted with respect to +all audiences. Deciding whether an argument is subjectively or objectively +accepted, respectively, are computationally intractable problems. In fact, the +problems remain intractable under structural restrictions that render the main +computational problems for non-value-based argumentation systems tractable. In +this paper we identify nontrivial classes of value-based argumentation systems +for which the acceptance problems are polynomial-time tractable. The classes +are defined by means of structural restrictions in terms of the underlying +graphical structure of the value-based system. Furthermore we show that the +acceptance problems are intractable for two classes of value-based systems that +where conjectured to be tractable by Dunne (Artificial Intelligence 171, 2007). +",Algorithms and Complexity Results for Persuasive Argumentation +" In this paper, we investigate the hybrid tractability of binary Quantified +Constraint Satisfaction Problems (QCSPs). First, a basic tractable class of +binary QCSPs is identified by using the broken-triangle property. In this +class, the variable ordering for the broken-triangle property must be same as +that in the prefix of the QCSP. Second, we break this restriction to allow that +existentially quantified variables can be shifted within or out of their +blocks, and thus identify some novel tractable classes by introducing the +broken-angle property. Finally, we identify a more generalized tractable class, +i.e., the min-of-max extendable class for QCSPs. +","Hybrid Tractable Classes of Binary Quantified Constraint Satisfaction + Problems" +" Most current planners assume complete domain models and focus on generating +correct plans. Unfortunately, domain modeling is a laborious and error-prone +task. While domain experts cannot guarantee completeness, often they are able +to circumscribe the incompleteness of the model by providing annotations as to +which parts of the domain model may be incomplete. In such cases, the goal +should be to generate plans that are robust with respect to any known +incompleteness of the domain. In this paper, we first introduce annotations +expressing the knowledge of the domain incompleteness, and formalize the notion +of plan robustness with respect to an incomplete domain model. We then propose +an approach to compiling the problem of finding robust plans to the conformant +probabilistic planning problem. We present experimental results with +Probabilistic-FF, a state-of-the-art planner, showing the promise of our +approach. +",Synthesizing Robust Plans under Incomplete Domain Models +" Over the years, nonmonotonic rules have proven to be a very expressive and +useful knowledge representation paradigm. They have recently been used to +complement the expressive power of Description Logics (DLs), leading to the +study of integrative formal frameworks, generally referred to as hybrid +knowledge bases, where both DL axioms and rules can be used to represent +knowledge. The need to use these hybrid knowledge bases in dynamic domains has +called for the development of update operators, which, given the substantially +different way Description Logics and rules are usually updated, has turned out +to be an extremely difficult task. + In [SL10], a first step towards addressing this problem was taken, and an +update operator for hybrid knowledge bases was proposed. Despite its +significance -- not only for being the first update operator for hybrid +knowledge bases in the literature, but also because it has some applications - +this operator was defined for a restricted class of problems where only the +ABox was allowed to change, which considerably diminished its applicability. +Many applications that use hybrid knowledge bases in dynamic scenarios require +both DL axioms and rules to be updated. + In this paper, motivated by real world applications, we introduce an update +operator for a large class of hybrid knowledge bases where both the DL +component as well as the rule component are allowed to dynamically change. We +introduce splitting sequences and splitting theorem for hybrid knowledge bases, +use them to define a modular update semantics, investigate its basic +properties, and illustrate its use on a realistic example about cargo imports. +",Splitting and Updating Hybrid Knowledge Bases (Extended Version) +" A fundamental task for propositional logic is to compute models of +propositional formulas. Programs developed for this task are called +satisfiability solvers. We show that transition systems introduced by +Nieuwenhuis, Oliveras, and Tinelli to model and analyze satisfiability solvers +can be adapted for solvers developed for two other propositional formalisms: +logic programming under the answer-set semantics, and the logic PC(ID). We show +that in each case the task of computing models can be seen as ""satisfiability +modulo answer-set programming,"" where the goal is to find a model of a theory +that also is an answer set of a certain program. The unifying perspective we +develop shows, in particular, that solvers CLASP and MINISATID are closely +related despite being developed for different formalisms, one for answer-set +programming and the latter for the logic PC(ID). +",Transition Systems for Model Generators - A Unifying Approach +" This paper describes a graph clustering algorithm that aims to minimize the +normalized cut criterion and has a model order selection procedure. The +performance of the proposed algorithm is comparable to spectral approaches in +terms of minimizing normalized cut. However, unlike spectral approaches, the +proposed algorithm scales to graphs with millions of nodes and edges. The +algorithm consists of three components that are processed sequentially: a +greedy agglomerative hierarchical clustering procedure, model order selection, +and a local refinement. + For a graph of n nodes and O(n) edges, the computational complexity of the +algorithm is O(n log^2 n), a major improvement over the O(n^3) complexity of +spectral methods. Experiments are performed on real and synthetic networks to +demonstrate the scalability of the proposed approach, the effectiveness of the +model order selection procedure, and the performance of the proposed algorithm +in terms of minimizing the normalized cut metric. +",GANC: Greedy Agglomerative Normalized Cut +" Machine-part cell formation is used in cellular manufacturing in order to +process a large variety, quality, lower work in process levels, reducing +manufacturing lead-time and customer response time while retaining flexibility +for new products. This paper presents a new and novel approach for obtaining +machine cells and part families. In the cellular manufacturing the fundamental +problem is the formation of part families and machine cells. The present paper +deals with the Self Organising Map (SOM) method an unsupervised learning +algorithm in Artificial Intelligence, and has been used as a visually +decipherable clustering tool of machine-part cell formation. The objective of +the paper is to cluster the binary machine-part matrix through visually +decipherable cluster of SOM color-coding and labelling via the SOM map nodes in +such a way that the part families are processed in that machine cells. The +Umatrix, component plane, principal component projection, scatter plot and +histogram of SOM have been reported in the present work for the successful +visualization of the machine-part cell formation. Computational result with the +proposed algorithm on a set of group technology problems available in the +literature is also presented. The proposed SOM approach produced solutions with +a grouping efficacy that is at least as good as any results earlier reported in +the literature and improved the grouping efficacy for 70% of the problems and +found immensely useful to both industry practitioners and researchers. +","Machine-Part cell formation through visual decipherable clustering of + Self Organizing Map" +" Rubik's Cube is an easily-understood puzzle, which is originally called the +""magic cube"". It is a well-known planning problem, which has been studied for a +long time. Yet many simple properties remain unknown. This paper studies +whether modern SAT solvers are applicable to this puzzle. To our best +knowledge, we are the first to translate Rubik's Cube to a SAT problem. To +reduce the number of variables and clauses needed for the encoding, we replace +a naive approach of 6 Boolean variables to represent each color on each facelet +with a new approach of 3 or 2 Boolean variables. In order to be able to solve +quickly Rubik's Cube, we replace the direct encoding of 18 turns with the layer +encoding of 18-subtype turns based on 6-type turns. To speed up the solving +further, we encode some properties of two-phase algorithm as an additional +constraint, and restrict some move sequences by adding some constraint clauses. +Using only efficient encoding cannot solve this puzzle. For this reason, we +improve the existing SAT solvers, and develop a new SAT solver based on +PrecoSAT, though it is suited only for Rubik's Cube. The new SAT solver +replaces the lookahead solving strategy with an ALO (\emph{at-least-one}) +solving strategy, and decomposes the original problem into sub-problems. Each +sub-problem is solved by PrecoSAT. The empirical results demonstrate both our +SAT translation and new solving technique are efficient. Without the efficient +SAT encoding and the new solving technique, Rubik's Cube will not be able to be +solved still by any SAT solver. Using the improved SAT solver, we can find +always a solution of length 20 in a reasonable time. Although our solver is +slower than Kociemba's algorithm using lookup tables, but does not require a +huge lookup table. +",Solving Rubik's Cube Using SAT Solvers +" The World Wide Web no longer consists just of HTML pages. Our work sheds +light on a number of trends on the Internet that go beyond simple Web pages. +The hidden Web provides a wealth of data in semi-structured form, accessible +through Web forms and Web services. These services, as well as numerous other +applications on the Web, commonly use XML, the eXtensible Markup Language. XML +has become the lingua franca of the Internet that allows customized markups to +be defined for specific domains. On top of XML, the Semantic Web grows as a +common structured data source. In this work, we first explain each of these +developments in detail. Using real-world examples from scientific domains of +great interest today, we then demonstrate how these new developments can assist +the managing, harvesting, and organization of data on the Web. On the way, we +also illustrate the current research avenues in these domains. We believe that +this effort would help bridge multiple database tracks, thereby attracting +researchers with a view to extend database technology. +","The Hidden Web, XML and Semantic Web: A Scientific Data Management + Perspective" +" Developing smart house systems has been a great challenge for researchers and +engineers in this area because of the high cost of implementation and +evaluation process of these systems, while being very time consuming. Testing a +designed smart house before actually building it is considered as an obstacle +towards an efficient smart house project. This is because of the variety of +sensors, home appliances and devices available for a real smart environment. In +this paper, we present the design and implementation of a multi-purpose smart +house simulation system for designing and simulating all aspects of a smart +house environment. This simulator provides the ability to design the house plan +and different virtual sensors and appliances in a two dimensional model of the +virtual house environment. This simulator can connect to any external smart +house remote controlling system, providing evaluation capabilities to their +system much easier than before. It also supports detailed adding of new +emerging sensors and devices to help maintain its compatibility with future +simulation needs. Scenarios can also be defined for testing various possible +combinations of device states; so different criteria and variables can be +simply evaluated without the need of experimenting on a real environment. +",A Multi-Purpose Scenario-based Simulator for Smart House Environments +" We introduce the Xapagy cognitive architecture: a software system designed to +perform narrative reasoning. The architecture has been designed from scratch to +model and mimic the activities performed by humans when witnessing, reading, +recalling, narrating and talking about stories. +",Xapagy: a cognitive architecture for narrative reasoning +" This paper presents a general and efficient framework for probabilistic +inference and learning from arbitrary uncertain information. It exploits the +calculation properties of finite mixture models, conjugate families and +factorization. Both the joint probability density of the variables and the +likelihood function of the (objective or subjective) observation are +approximated by a special mixture model, in such a way that any desired +conditional distribution can be directly obtained without numerical +integration. We have developed an extended version of the expectation +maximization (EM) algorithm to estimate the parameters of mixture models from +uncertain training examples (indirect observations). As a consequence, any +piece of exact or uncertain information about both input and output values is +consistently handled in the inference and learning stages. This ability, +extremely useful in certain situations, is not found in most alternative +methods. The proposed framework is formally justified from standard +probabilistic principles and illustrative examples are provided in the fields +of nonparametric pattern classification, nonlinear regression and pattern +completion. Finally, experiments on a real application and comparative results +over standard databases provide empirical evidence of the utility of the method +in a wide range of applications. +","Probabilistic Inference from Arbitrary Uncertainty using Mixtures of + Factorized Generalized Gaussians" +" Decision-theoretic agents predict and evaluate the results of their actions +using a model, or ontology, of their environment. An agent's goal, or utility +function, may also be specified in terms of the states of, or entities within, +its ontology. If the agent may upgrade or replace its ontology, it faces a +crisis: the agent's original goal may not be well-defined with respect to its +new ontology. This crisis must be resolved before the agent can make plans +towards achieving its goals. + We discuss in this paper which sorts of agents will undergo ontological +crises and why we may want to create such agents. We present some concrete +examples, and argue that a well-defined procedure for resolving ontological +crises is needed. We point to some possible approaches to solving this problem, +and evaluate these methods on our examples. +",Ontological Crises in Artificial Agents' Value Systems +" A knowledge system S describing a part of real world does in general not +contain complete information. Reasoning with incomplete information is prone to +errors since any belief derived from S may be false in the present state of the +world. A false belief may suggest wrong decisions and lead to harmful actions. +So an important goal is to make false beliefs as unlikely as possible. This +work introduces the notions of ""typical atoms"" and ""typical models"", and shows +that reasoning with typical models minimizes the expected number of false +beliefs over all ways of using incomplete information. Various properties of +typical models are studied, in particular, correctness and stability of beliefs +suggested by typical models, and their connection to oblivious reasoning. +",Typical models: minimizing false beliefs +" We present a new approach to path planning, called the ""Ariadne's clew +algorithm"". It is designed to find paths in high-dimensional continuous spaces +and applies to robots with many degrees of freedom in static, as well as +dynamic environments - ones where obstacles may move. The Ariadne's clew +algorithm comprises two sub-algorithms, called Search and Explore, applied in +an interleaved manner. Explore builds a representation of the accessible space +while Search looks for the target. Both are posed as optimization problems. We +describe a real implementation of the algorithm to plan paths for a six degrees +of freedom arm in a dynamic environment where another six degrees of freedom +arm is used as a moving obstacle. Experimental results show that a path is +found in about one second without any pre-processing. +",The Ariadne's Clew Algorithm +" This article studies the problem of modifying the action ordering of a plan +in order to optimise the plan according to various criteria. One of these +criteria is to make a plan less constrained and the other is to minimize its +parallel execution time. Three candidate definitions are proposed for the first +of these criteria, constituting a sequence of increasing optimality guarantees. +Two of these are based on deordering plans, which means that ordering relations +may only be removed, not added, while the third one uses reordering, where +arbitrary modifications to the ordering are allowed. It is shown that only the +weakest one of the three criteria is tractable to achieve, the other two being +NP-hard and even difficult to approximate. Similarly, optimising the parallel +execution time of a plan is studied both for deordering and reordering of +plans. In the general case, both of these computations are NP-hard. However, it +is shown that optimal deorderings can be computed in polynomial time for a +class of planning languages based on the notions of producers, consumers and +threats, which includes most of the commonly used planning languages. Computing +optimal reorderings can potentially lead to even faster parallel executions, +but this problem remains NP-hard and difficult to approximate even under quite +severe restrictions. +",Computational Aspects of Reordering Plans +" It is common to view programs as a combination of logic and control: the +logic part defines what the program must do, the control part -- how to do it. +The Logic Programming paradigm was developed with the intention of separating +the logic from the control. Recently, extensive research has been conducted on +automatic generation of control for logic programs. Only a few of these works +considered the issue of automatic generation of control for improving the +efficiency of logic programs. In this paper we present a novel algorithm for +automatic finding of lowest-cost subgoal orderings. The algorithm works using +the divide-and-conquer strategy. The given set of subgoals is partitioned into +smaller sets, based on co-occurrence of free variables. The subsets are ordered +recursively and merged, yielding a provably optimal order. We experimentally +demonstrate the utility of the algorithm by testing it in several domains, and +discuss the possibilities of its cooperation with other existing methods. +","The Divide-and-Conquer Subgoal-Ordering Algorithm for Speeding up Logic + Inference" +" Using an improved backtrack algorithm with sophisticated pruning techniques, +we revise previous observations correlating a high frequency of hard to solve +Hamiltonian Cycle instances with the Gn,m phase transition between +Hamiltonicity and non-Hamiltonicity. Instead all tested graphs of 100 to 1500 +vertices are easily solved. When we artificially restrict the degree sequence +with a bounded maximum degree, although there is some increase in difficulty, +the frequency of hard graphs is still low. When we consider more regular graphs +based on a generalization of knight's tours, we observe frequent instances of +really hard graphs, but on these the average degree is bounded by a constant. +We design a set of graphs with a feature our algorithm is unable to detect and +so are very hard for our algorithm, but in these we can vary the average degree +from O(1) to O(n). We have so far found no class of graphs correlated with the +Gn,m phase transition which asymptotically produces a high frequency of hard +instances. +","The Gn,m Phase Transition is Not Hard for the Hamiltonian Cycle Problem" +" This article presents a measure of semantic similarity in an IS-A taxonomy +based on the notion of shared information content. Experimental evaluation +against a benchmark set of human similarity judgments demonstrates that the +measure performs better than the traditional edge-counting approach. The +article presents algorithms that take advantage of taxonomic similarity in +resolving syntactic and semantic ambiguity, along with experimental results +demonstrating their effectiveness. +","Semantic Similarity in a Taxonomy: An Information-Based Measure and its + Application to Problems of Ambiguity in Natural Language" +" A class of interval-based temporal languages for uniformly representing and +reasoning about actions and plans is presented. Actions are represented by +describing what is true while the action itself is occurring, and plans are +constructed by temporally relating actions and world states. The temporal +languages are members of the family of Description Logics, which are +characterized by high expressivity combined with good computational properties. +The subsumption problem for a class of temporal Description Logics is +investigated and sound and complete decision procedures are given. The basic +language TL-F is considered first: it is the composition of a temporal logic TL +-- able to express interval temporal networks -- together with the non-temporal +logic F -- a Feature Description Logic. It is proven that subsumption in this +language is an NP-complete problem. Then it is shown how to reason with the +more expressive languages TLU-FU and TL-ALCF. The former adds disjunction both +at the temporal and non-temporal sides of the language, the latter extends the +non-temporal side with set-valued features (i.e., roles) and a propositionally +complete language. +",A Temporal Description Logic for Reasoning about Actions and Plans +" Many of the artificial intelligence techniques developed to date rely on +heuristic search through large spaces. Unfortunately, the size of these spaces +and the corresponding computational effort reduce the applicability of +otherwise novel and effective algorithms. A number of parallel and distributed +approaches to search have considerably improved the performance of the search +process. Our goal is to develop an architecture that automatically selects +parallel search strategies for optimal performance on a variety of search +problems. In this paper we describe one such architecture realized in the +Eureka system, which combines the benefits of many different approaches to +parallel heuristic search. Through empirical and theoretical analyses we +observe that features of the problem space directly affect the choice of +optimal parallel search strategy. We then employ machine learning techniques to +select the optimal parallel search strategy for a given problem space. When a +new search task is input to the system, Eureka uses features describing the +search space and the chosen architecture to automatically select the +appropriate search strategy. Eureka has been tested on a MIMD parallel +processor, a distributed network of workstations, and a single workstation +using multithreading. Results generated from fifteen puzzle problems, robot arm +motion problems, artificial search spaces, and planning problems indicate that +Eureka outperforms any of the tested strategies used exclusively for all +problem instances and is able to greatly reduce the search time for these +applications. +",Adaptive Parallel Iterative Deepening Search +" Order of magnitude reasoning - reasoning by rough comparisons of the sizes of +quantities - is often called 'back of the envelope calculation', with the +implication that the calculations are quick though approximate. This paper +exhibits an interesting class of constraint sets in which order of magnitude +reasoning is demonstrably fast. Specifically, we present a polynomial-time +algorithm that can solve a set of constraints of the form 'Points a and b are +much closer together than points c and d.' We prove that this algorithm can be +applied if `much closer together' is interpreted either as referring to an +infinite difference in scale or as referring to a finite difference in scale, +as long as the difference in scale is greater than the number of variables in +the constraint set. We also prove that the first-order theory over such +constraints is decidable. +",Order of Magnitude Comparisons of Distance +" This paper introduces AntNet, a novel approach to the adaptive learning of +routing tables in communications networks. AntNet is a distributed, mobile +agents based Monte Carlo system that was inspired by recent work on the ant +colony metaphor for solving optimization problems. AntNet's agents concurrently +explore the network and exchange collected information. The communication among +the agents is indirect and asynchronous, mediated by the network itself. This +form of communication is typical of social insects and is called stigmergy. We +compare our algorithm with six state-of-the-art routing algorithms coming from +the telecommunications and machine learning fields. The algorithms' performance +is evaluated over a set of realistic testbeds. We run many experiments over +real and artificial IP datagram networks with increasing number of nodes and +under several paradigmatic spatial and temporal traffic distributions. Results +are very encouraging. AntNet showed superior performance under all the +experimental conditions with respect to its competitors. We analyze the main +characteristics of the algorithm and try to explain the reasons for its +superiority. +",AntNet: Distributed Stigmergetic Control for Communications Networks +" Cox's well-known theorem justifying the use of probability is shown not to +hold in finite domains. The counterexample also suggests that Cox's assumptions +are insufficient to prove the result even in infinite domains. The same +counterexample is used to disprove a result of Fine on comparative conditional +probability. +",A Counter Example to Theorems of Cox and Fine +" As planning is applied to larger and richer domains the effort involved in +constructing domain descriptions increases and becomes a significant burden on +the human application designer. If general planners are to be applied +successfully to large and complex domains it is necessary to provide the domain +designer with some assistance in building correctly encoded domains. One way of +doing this is to provide domain-independent techniques for extracting, from a +domain description, knowledge that is implicit in that description and that can +assist domain designers in debugging domain descriptions. This knowledge can +also be exploited to improve the performance of planners: several researchers +have explored the potential of state invariants in speeding up the performance +of domain-independent planners. In this paper we describe a process by which +state invariants can be extracted from the automatically inferred type +structure of a domain. These techniques are being developed for exploitation by +STAN, a Graphplan based planner that employs state analysis techniques to +enhance its performance. +",The Automatic Inference of State Invariants in TIM +" The notion of class is ubiquitous in computer science and is central in many +formalisms for the representation of structured knowledge used both in +knowledge representation and in databases. In this paper we study the basic +issues underlying such representation formalisms and single out both their +common characteristics and their distinguishing features. Such investigation +leads us to propose a unifying framework in which we are able to capture the +fundamental aspects of several representation languages used in different +contexts. The proposed formalism is expressed in the style of description +logics, which have been introduced in knowledge representation as a means to +provide a semantically well-founded basis for the structural aspects of +knowledge representation systems. The description logic considered in this +paper is a subset of first order logic with nice computational characteristics. +It is quite expressive and features a novel combination of constructs that has +not been studied before. The distinguishing constructs are number restrictions, +which generalize existence and functional dependencies, inverse roles, which +allow one to refer to the inverse of a relationship, and possibly cyclic +assertions, which are necessary for capturing real world domains. We are able +to show that it is precisely such combination of constructs that makes our +logic powerful enough to model the essential set of features for defining class +structures that are common to frame systems, object-oriented database +languages, and semantic data models. As a consequence of the established +correspondences, several significant extensions of each of the above formalisms +become available. The high expressiveness of the logic we propose and the need +for capturing the reasoning in different contexts forces us to distinguish +between unrestricted and finite model reasoning. A notable feature of our +proposal is that reasoning in both cases is decidable. We argue that, by virtue +of the high expressive power and of the associated reasoning capabilities on +both unrestricted and finite models, our logic provides a common core for +class-based representation formalisms. +",Unifying Class-Based Representation Formalisms +" In default reasoning, usually not all possible ways of resolving conflicts +between default rules are acceptable. Criteria expressing acceptable ways of +resolving the conflicts may be hardwired in the inference mechanism, for +example specificity in inheritance reasoning can be handled this way, or they +may be given abstractly as an ordering on the default rules. In this article we +investigate formalizations of the latter approach in Reiter's default logic. +Our goal is to analyze and compare the computational properties of three such +formalizations in terms of their computational complexity: the prioritized +default logics of Baader and Hollunder, and Brewka, and a prioritized default +logic that is based on lexicographic comparison. The analysis locates the +propositional variants of these logics on the second and third levels of the +polynomial hierarchy, and identifies the boundary between tractable and +intractable inference for restricted classes of prioritized default theories. +",Complexity of Prioritized Default Logics +" We describe a general approach to optimization which we term `Squeaky Wheel' +Optimization (SWO). In SWO, a greedy algorithm is used to construct a solution +which is then analyzed to find the trouble spots, i.e., those elements, that, +if improved, are likely to improve the objective function score. The results of +the analysis are used to generate new priorities that determine the order in +which the greedy algorithm constructs the next solution. This +Construct/Analyze/Prioritize cycle continues until some limit is reached, or an +acceptable solution is found. SWO can be viewed as operating on two search +spaces: solutions and prioritizations. Successive solutions are only indirectly +related, via the re-prioritization that results from analyzing the prior +solution. Similarly, successive prioritizations are generated by constructing +and analyzing solutions. This `coupled search' has some interesting properties, +which we discuss. We report encouraging experimental results on two domains, +scheduling problems that arise in fiber-optic cable manufacturing, and graph +coloring problems. The fact that these domains are very different supports our +claim that SWO is a general technique for optimization. +",Squeaky Wheel Optimization +" Intractable distributions present a common difficulty in inference within the +probabilistic knowledge representation framework and variational methods have +recently been popular in providing an approximate solution. In this article, we +describe a perturbational approach in the form of a cumulant expansion which, +to lowest order, recovers the standard Kullback-Leibler variational bound. +Higher-order terms describe corrections on the variational approach without +incurring much further computational cost. The relationship to other +perturbational approaches such as TAP is also elucidated. We demonstrate the +method on a particular class of undirected graphical models, Boltzmann +machines, for which our simulation results confirm improved accuracy and +enhanced stability during learning. +",Variational Cumulant Expansions for Intractable Distributions +" STAN is a Graphplan-based planner, so-called because it uses a variety of +STate ANalysis techniques to enhance its performance. STAN competed in the +AIPS-98 planning competition where it compared well with the other competitors +in terms of speed, finding solutions fastest to many of the problems posed. +Although the domain analysis techniques STAN exploits are an important factor +in its overall performance, we believe that the speed at which STAN solved the +competition problems is largely due to the implementation of its plan graph. +The implementation is based on two insights: that many of the graph +construction operations can be implemented as bit-level logical operations on +bit vectors, and that the graph should not be explicitly constructed beyond the +fix point. This paper describes the implementation of STAN's plan graph and +provides experimental results which demonstrate the circumstances under which +advantages can be obtained from using this implementation. +",Efficient Implementation of the Plan Graph in STAN +" Top-down and bottom-up theorem proving approaches each have specific +advantages and disadvantages. Bottom-up provers profit from strong redundancy +control but suffer from the lack of goal-orientation, whereas top-down provers +are goal-oriented but often have weak calculi when their proof lengths are +considered. In order to integrate both approaches, we try to achieve +cooperation between a top-down and a bottom-up prover in two different ways: +The first technique aims at supporting a bottom-up with a top-down prover. A +top-down prover generates subgoal clauses, they are then processed by a +bottom-up prover. The second technique deals with the use of bottom-up +generated lemmas in a top-down prover. We apply our concept to the areas of +model elimination and superposition. We discuss the ability of our techniques +to shorten proofs as well as to reorder the search space in an appropriate +manner. Furthermore, in order to identify subgoal clauses and lemmas which are +actually relevant for the proof task, we develop methods for a relevancy-based +filtering. Experiments with the provers SETHEO and SPASS performed in the +problem library TPTP reveal the high potential of our cooperation approaches. +",Cooperation between Top-Down and Bottom-Up Theorem Provers +" A previously developed quantum search algorithm for solving 1-SAT problems in +a single step is generalized to apply to a range of highly constrained k-SAT +problems. We identify a bound on the number of clauses in satisfiability +problems for which the generalized algorithm can find a solution in a constant +number of steps as the number of variables increases. This performance +contrasts with the linear growth in the number of steps required by the best +classical algorithms, and the exponential number required by classical and +quantum methods that ignore the problem structure. In some cases, the algorithm +can also guarantee that insoluble problems in fact have no solutions, unlike +previously proposed quantum search algorithms. +",Solving Highly Constrained Search Problems with Quantum Computers +" Planning under uncertainty is a central problem in the study of automated +sequential decision making, and has been addressed by researchers in many +different fields, including AI planning, decision analysis, operations +research, control theory and economics. While the assumptions and perspectives +adopted in these areas often differ in substantial ways, many planning problems +of interest to researchers in these fields can be modeled as Markov decision +processes (MDPs) and analyzed using the techniques of decision theory. This +paper presents an overview and synthesis of MDP-related methods, showing how +they provide a unifying framework for modeling many classes of planning +problems studied in AI. It also describes structural properties of MDPs that, +when exhibited by particular classes of problems, can be exploited in the +construction of optimal or approximately optimal policies or plans. Planning +problems commonly possess structure in the reward and value functions used to +describe performance criteria, in the functions used to describe state +transitions and observations, and in the relationships among features used to +describe states, actions, rewards, and observations. Specialized +representations, and algorithms employing these representations, can achieve +computational leverage by exploiting these various forms of structure. Certain +AI techniques -- in particular those based on the use of structured, +intensional representations -- can be viewed in this way. This paper surveys +several types of representations for both classical and decision-theoretic +planning problems, and planning algorithms that exploit these representations +in a number of different ways to ease the computational burden of constructing +policies or plans. It focuses primarily on abstraction, aggregation and +decomposition techniques based on AI-style representations. +","Decision-Theoretic Planning: Structural Assumptions and Computational + Leverage" +" We study the problem of probabilistic deduction with conditional constraints +over basic events. We show that globally complete probabilistic deduction with +conditional constraints over basic events is NP-hard. We then concentrate on +the special case of probabilistic deduction in conditional constraint trees. We +elaborate very efficient techniques for globally complete probabilistic +deduction. In detail, for conditional constraint trees with point +probabilities, we present a local approach to globally complete probabilistic +deduction, which runs in linear time in the size of the conditional constraint +trees. For conditional constraint trees with interval probabilities, we show +that globally complete probabilistic deduction can be done in a global approach +by solving nonlinear programs. We show how these nonlinear programs can be +transformed into equivalent linear programs, which are solvable in polynomial +time in the size of the conditional constraint trees. +",Probabilistic Deduction with Conditional Constraints over Basic Events +" We describe a variational approximation method for efficient inference in +large-scale probabilistic models. Variational methods are deterministic +procedures that provide approximations to marginal and conditional +probabilities of interest. They provide alternatives to approximate inference +methods based on stochastic sampling or search. We describe a variational +approach to the problem of diagnostic inference in the `Quick Medical +Reference' (QMR) network. The QMR network is a large-scale probabilistic +graphical model built on statistical and expert knowledge. Exact probabilistic +inference is infeasible in this model for all but a small set of cases. We +evaluate our variational inference algorithm on a large set of diagnostic test +cases, comparing the algorithm to a state-of-the-art stochastic sampling +method. +",Variational Probabilistic Inference and the QMR-DT Network +" This paper offers an approach to extensible knowledge representation and +reasoning for a family of formalisms known as Description Logics. The approach +is based on the notion of adding new concept constructors, and includes a +heuristic methodology for specifying the desired extensions, as well as a +modularized software architecture that supports implementing extensions. The +architecture detailed here falls in the normalize-compared paradigm, and +supports both intentional reasoning (subsumption) involving concepts, and +extensional reasoning involving individuals after incremental updates to the +knowledge base. The resulting approach can be used to extend the reasoner with +specialized notions that are motivated by specific problems or application +areas, such as reasoning about dates, plans, etc. In addition, it provides an +opportunity to implement constructors that are not currently yet sufficiently +well understood theoretically, but are needed in practice. Also, for +constructors that are provably hard to reason with (e.g., ones whose presence +would lead to undecidability), it allows the implementation of incomplete +reasoners where the incompleteness is tailored to be acceptable for the +application at hand. +",Extensible Knowledge Representation: the Case of Description Reasoners +" The research on conditional planning rejects the assumptions that there is no +uncertainty or incompleteness of knowledge with respect to the state and +changes of the system the plans operate on. Without these assumptions the +sequences of operations that achieve the goals depend on the initial state and +the outcomes of nondeterministic changes in the system. This setting raises the +questions of how to represent the plans and how to perform plan search. The +answers are quite different from those in the simpler classical framework. In +this paper, we approach conditional planning from a new viewpoint that is +motivated by the use of satisfiability algorithms in classical planning. +Translating conditional planning to formulae in the propositional logic is not +feasible because of inherent computational limitations. Instead, we translate +conditional planning to quantified Boolean formulae. We discuss three +formalizations of conditional planning as quantified Boolean formulae, and +present experimental results obtained with a theorem-prover. +",Constructing Conditional Plans by a Theorem-Prover +" Stacked generalization is a general method of using a high-level model to +combine lower-level models to achieve greater predictive accuracy. In this +paper we address two crucial issues which have been considered to be a `black +art' in classification tasks ever since the introduction of stacked +generalization in 1992 by Wolpert: the type of generalizer that is suitable to +derive the higher-level model, and the kind of attributes that should be used +as its input. We find that best results are obtained when the higher-level +model combines the confidence (and not just the predictions) of the lower-level +ones. We demonstrate the effectiveness of stacked generalization for combining +three different types of learning algorithms for classification tasks. We also +compare the performance of stacked generalization with majority vote and +published results of arcing and bagging. +",Issues in Stacked Generalization +" We present PARIS, an approach for the automatic alignment of ontologies. +PARIS aligns not only instances, but also relations and classes. Alignments at +the instance-level cross-fertilize with alignments at the schema-level. +Thereby, our system provides a truly holistic solution to the problem of +ontology alignment. The heart of the approach is probabilistic. This allows +PARIS to run without any parameter tuning. We demonstrate the efficiency of the +algorithm and its precision through extensive experiments. In particular, we +obtain a precision of around 90% in experiments with two of the world's largest +ontologies. +",Ontology Alignment at the Instance and Schema Level +" We prove that it is NP-hard for a coalition of two manipulators to compute +how to manipulate the Borda voting rule. This resolves one of the last open +problems in the computational complexity of manipulating common voting rules. +Because of this NP-hardness, we treat computing a manipulation as an +approximation problem where we try to minimize the number of manipulators. +Based on ideas from bin packing and multiprocessor scheduling, we propose two +new approximation methods to compute manipulations of the Borda rule. +Experiments show that these methods significantly outperform the previous best +known %existing approximation method. We are able to find optimal manipulations +in almost all the randomly generated elections tested. Our results suggest +that, whilst computing a manipulation of the Borda rule by a coalition is +NP-hard, computational complexity may provide only a weak barrier against +manipulation in practice. +",Complexity of and Algorithms for Borda Manipulation +" We introduce a temporal model for reasoning on disjunctive metric constraints +on intervals and time points in temporal contexts. This temporal model is +composed of a labeled temporal algebra and its reasoning algorithms. The +labeled temporal algebra defines labeled disjunctive metric point-based +constraints, where each disjunct in each input disjunctive constraint is +univocally associated to a label. Reasoning algorithms manage labeled +constraints, associated label lists, and sets of mutually inconsistent +disjuncts. These algorithms guarantee consistency and obtain a minimal network. +Additionally, constraints can be organized in a hierarchy of alternative +temporal contexts. Therefore, we can reason on context-dependent disjunctive +metric constraints on intervals and points. Moreover, the model is able to +represent non-binary constraints, such that logical dependencies on disjuncts +in constraints can be handled. The computational cost of reasoning algorithms +is exponential in accordance with the underlying problem complexity, although +some improvements are proposed. +","Reasoning on Interval and Point-based Disjunctive Metric Constraints in + Temporal Contexts" +" Negation as failure and incomplete information in logic programs have been +studied by many researchers In order to explains HOW a negated conclusion was +reached, we introduce and proof a different way for negating facts to +overcoming misleads in logic programs. Negating facts can be achieved by asking +the user for constants that do not appear elsewhere in the knowledge base. +",Overcoming Misleads In Logic Programs by Redefining Negation +" Despite the prevalence of the Computational Theory of Mind and the +Connectionist Model, the establishing of the key principles of the Cognitive +Science are still controversy and inconclusive. This paper proposes the concept +of Pattern Recognition as Necessary and Sufficient Principle for a general +cognitive science modeling, in a very ambitious scientific proposal. A formal +physical definition of the pattern recognition concept is also proposed to +solve many key conceptual gaps on the field. +","Proposal of Pattern Recognition as a necessary and sufficient Principle + to Cognitive Science" +" As was shown recently, many important AI problems require counting the number +of models of propositional formulas. The problem of counting models of such +formulas is, according to present knowledge, computationally intractable in a +worst case. Based on the Davis-Putnam procedure, we present an algorithm, CDP, +that computes the exact number of models of a propositional CNF or DNF formula +F. Let m and n be the number of clauses and variables of F, respectively, and +let p denote the probability that a literal l of F occurs in a clause C of F, +then the average running time of CDP is shown to be O(nm^d), where +d=-1/log(1-p). The practical performance of CDP has been estimated in a series +of experiments on a wide variety of CNF formulas. +",The Good Old Davis-Putnam Procedure Helps Counting Models +" This paper presents a new approach to identifying and eliminating mislabeled +training instances for supervised learning. The goal of this approach is to +improve classification accuracies produced by learning algorithms by improving +the quality of the training data. Our approach uses a set of learning +algorithms to create classifiers that serve as noise filters for the training +data. We evaluate single algorithm, majority vote and consensus filters on five +datasets that are prone to labeling errors. Our experiments illustrate that +filtering significantly improves classification accuracy for noise levels up to +30 percent. An analytical and empirical evaluation of the precision of our +approach shows that consensus filters are conservative at throwing away good +data at the expense of retaining bad data and that majority filters are better +at detecting bad data at the expense of throwing away good data. This suggests +that for situations in which there is a paucity of data, consensus filters are +preferable, whereas majority vote filters are preferable for situations with an +abundance of data. +",Identifying Mislabeled Training Data +" In many real-world learning tasks, it is expensive to acquire a sufficient +number of labeled examples for training. This paper investigates methods for +reducing annotation cost by `sample selection'. In this approach, during +training the learning program examines many unlabeled examples and selects for +labeling only those that are most informative at each stage. This avoids +redundantly labeling examples that contribute little new information. Our work +follows on previous research on Query By Committee, extending the +committee-based paradigm to the context of probabilistic classification. We +describe a family of empirical methods for committee-based sample selection in +probabilistic classification models, which evaluate the informativeness of an +example by measuring the degree of disagreement between several model variants. +These variants (the committee) are drawn randomly from a probability +distribution conditioned by the training set labeled so far. The method was +applied to the real-world natural language processing task of stochastic +part-of-speech tagging. We find that all variants of the method achieve a +significant reduction in annotation cost, although their computational +efficiency differs. In particular, the simplest variant, a two member committee +with no parameters to tune, gives excellent results. We also show that sample +selection yields a significant reduction in the size of the model used by the +tagger. +",Committee-Based Sample Selection for Probabilistic Classifiers +" We investigate the problem of reasoning in the propositional fragment of +MBNF, the logic of minimal belief and negation as failure introduced by +Lifschitz, which can be considered as a unifying framework for several +nonmonotonic formalisms, including default logic, autoepistemic logic, +circumscription, epistemic queries, and logic programming. We characterize the +complexity and provide algorithms for reasoning in propositional MBNF. In +particular, we show that entailment in propositional MBNF lies at the third +level of the polynomial hierarchy, hence it is harder than reasoning in all the +above mentioned propositional formalisms for nonmonotonic reasoning. We also +prove the exact correspondence between negation as failure in MBNF and negative +introspection in Moore's autoepistemic logic. +",Reasoning about Minimal Belief and Negation as Failure +" We show how to find a minimum weight loop cutset in a Bayesian network with +high probability. Finding such a loop cutset is the first step in the method of +conditioning for inference. Our randomized algorithm for finding a loop cutset +outputs a minimum loop cutset after O(c 6^k kn) steps with probability at least +1 - (1 - 1/(6^k))^c6^k, where c > 1 is a constant specified by the user, k is +the minimal size of a minimum weight loop cutset, and n is the number of +vertices. We also show empirically that a variant of this algorithm often finds +a loop cutset that is closer to the minimum weight loop cutset than the ones +found by the best deterministic algorithms known. +",Randomized Algorithms for the Loop Cutset Problem +" Recently model checking representation and search techniques were shown to be +efficiently applicable to planning, in particular to non-deterministic +planning. Such planning approaches use Ordered Binary Decision Diagrams (OBDDs) +to encode a planning domain as a non-deterministic finite automaton and then +apply fast algorithms from model checking to search for a solution. OBDDs can +effectively scale and can provide universal plans for complex planning domains. +We are particularly interested in addressing the complexities arising in +non-deterministic, multi-agent domains. In this article, we present UMOP, a new +universal OBDD-based planning framework for non-deterministic, multi-agent +domains. We introduce a new planning domain description language, NADL, to +specify non-deterministic, multi-agent domains. The language contributes the +explicit definition of controllable agents and uncontrollable environment +agents. We describe the syntax and semantics of NADL and show how to build an +efficient OBDD-based representation of an NADL description. The UMOP planning +system uses NADL and different OBDD-based universal planning algorithms. It +includes the previously developed strong and strong cyclic planning algorithms. +In addition, we introduce our new optimistic planning algorithm that relaxes +optimality guarantees and generates plausible universal plans in some domains +where no strong nor strong cyclic solution exists. We present empirical results +applying UMOP to domains ranging from deterministic and single-agent with no +environment actions to non-deterministic and multi-agent with complex +environment actions. UMOP is shown to be a rich and efficient planning system. +","OBDD-based Universal Planning for Synchronized Agents in + Non-Deterministic Domains" +" This paper reviews the connections between Graphplan's planning-graph and the +dynamic constraint satisfaction problem and motivates the need for adapting CSP +search techniques to the Graphplan algorithm. It then describes how explanation +based learning, dependency directed backtracking, dynamic variable ordering, +forward checking, sticky values and random-restart search strategies can be +adapted to Graphplan. Empirical results are provided to demonstrate that these +augmentations improve Graphplan's performance significantly (up to 1000x +speedups) on several benchmark problems. Special attention is paid to the +explanation-based learning and dependency directed backtracking techniques as +they are empirically found to be most useful in improving the performance of +Graphplan. +","Planning Graph as a (Dynamic) CSP: Exploiting EBL, DDB and other CSP + Search Techniques in Graphplan" +" We investigate the space efficiency of a Propositional Knowledge +Representation (PKR) formalism. Intuitively, the space efficiency of a +formalism F in representing a certain piece of knowledge A, is the size of the +shortest formula of F that represents A. In this paper we assume that knowledge +is either a set of propositional interpretations (models) or a set of +propositional formulae (theorems). We provide a formal way of talking about the +relative ability of PKR formalisms to compactly represent a set of models or a +set of theorems. We introduce two new compactness measures, the corresponding +classes, and show that the relative space efficiency of a PKR formalism in +representing models/theorems is directly related to such classes. In +particular, we consider formalisms for nonmonotonic reasoning, such as +circumscription and default logic, as well as belief revision operators and the +stable model semantics for logic programs with negation. One interesting result +is that formalisms with the same time complexity do not necessarily belong to +the same space efficiency class. +",Space Efficiency of Propositional Knowledge Representation Formalisms +" Partially observable Markov decision processes (POMDPs) provide an elegant +mathematical framework for modeling complex decision and planning problems in +stochastic domains in which states of the system are observable only +indirectly, via a set of imperfect or noisy observations. The modeling +advantage of POMDPs, however, comes at a price -- exact methods for solving +them are computationally very expensive and thus applicable in practice only to +very simple problems. We focus on efficient approximation (heuristic) methods +that attempt to alleviate the computational problem and trade off accuracy for +speed. We have two objectives here. First, we survey various approximation +methods, analyze their properties and relations and provide some new insights +into their differences. Second, we present a number of new approximation +methods and novel refinements of existing techniques. The theoretical results +are supported by experiments on a problem from the agent navigation domain. +","Value-Function Approximations for Partially Observable Markov Decision + Processes" +" Pearl and Dechter (1996) claimed that the d-separation criterion for +conditional independence in acyclic causal networks also applies to networks of +discrete variables that have feedback cycles, provided that the variables of +the system are uniquely determined by the random disturbances. I show by +example that this is not true in general. Some condition stronger than +uniqueness is needed, such as the existence of a causal dynamics guaranteed to +lead to the unique solution. +","On Deducing Conditional Independence from d-Separation in Causal Graphs + with Feedback (Research Note)" +" Functional relationships between objects, called `attributes', are of +considerable importance in knowledge representation languages, including +Description Logics (DLs). A study of the literature indicates that papers have +made, often implicitly, different assumptions about the nature of attributes: +whether they are always required to have a value, or whether they can be +partial functions. The work presented here is the first explicit study of this +difference for subclasses of the CLASSIC DL, involving the same-as concept +constructor. It is shown that although determining subsumption between concept +descriptions has the same complexity (though requiring different algorithms), +the story is different in the case of determining the least common subsumer +(lcs). For attributes interpreted as partial functions, the lcs exists and can +be computed relatively easily; even in this case our results correct and extend +three previous papers about the lcs of DLs. In the case where attributes must +have a value, the lcs may not exist, and even if it exists it may be of +exponential size. Interestingly, it is possible to decide in polynomial time if +the lcs exists. +",What's in an Attribute? Consequences for the Least Common Subsumer +" We study the complexity of the combination of the Description Logics ALCQ and +ALCQI with a terminological formalism based on cardinality restrictions on +concepts. These combinations can naturally be embedded into C^2, the two +variable fragment of predicate logic with counting quantifiers, which yields +decidability in NExpTime. We show that this approach leads to an optimal +solution for ALCQI, as ALCQI with cardinality restrictions has the same +complexity as C^2 (NExpTime-complete). In contrast, we show that for ALCQ, the +problem can be solved in ExpTime. This result is obtained by a reduction of +reasoning with cardinality restrictions to reasoning with the (in general +weaker) terminological formalism of general axioms for ALCQ extended with +nominals. Using the same reduction, we show that, for the extension of ALCQI +with nominals, reasoning with general axioms is a NExpTime-complete problem. +Finally, we sharpen this result and show that pure concept satisfiability for +ALCQI with nominals is NExpTime-complete. Without nominals, this problem is +known to be PSpace-complete. +","The Complexity of Reasoning with Cardinality Restrictions and Nominals + in Expressive Description Logics" +" The local search algorithm WSat is one of the most successful algorithms for +solving the satisfiability (SAT) problem. It is notably effective at solving +hard Random 3-SAT instances near the so-called `satisfiability threshold', but +still shows a peak in search cost near the threshold and large variations in +cost over different instances. We make a number of significant contributions to +the analysis of WSat on high-cost random instances, using the +recently-introduced concept of the backbone of a SAT instance. The backbone is +the set of literals which are entailed by an instance. We find that the number +of solutions predicts the cost well for small-backbone instances but is much +less relevant for the large-backbone instances which appear near the threshold +and dominate in the overconstrained region. We show a very strong correlation +between search cost and the Hamming distance to the nearest solution early in +WSat's search. This pattern leads us to introduce a measure of the backbone +fragility of an instance, which indicates how persistent the backbone is as +clauses are removed. We propose that high-cost random instances for local +search are those with very large backbones which are also backbone-fragile. We +suggest that the decay in cost beyond the satisfiability threshold is due to +increasing backbone robustness (the opposite of backbone fragility). Our +hypothesis makes three correct predictions. First, that the backbone robustness +of an instance is negatively correlated with the local search cost when other +factors are controlled for. Second, that backbone-minimal instances (which are +3-SAT instances altered so as to be more backbone-fragile) are unusually hard +for WSat. Third, that the clauses most often unsatisfied during search are +those whose deletion has the most effect on the backbone. In understanding the +pathologies of local search methods, we hope to contribute to the development +of new and better techniques. +",Backbone Fragility and the Local Search Cost Peak +" This paper describes a novel method by which a spoken dialogue system can +learn to choose an optimal dialogue strategy from its experience interacting +with human users. The method is based on a combination of reinforcement +learning and performance modeling of spoken dialogue systems. The reinforcement +learning component applies Q-learning (Watkins, 1989), while the performance +modeling component applies the PARADISE evaluation framework (Walker et al., +1997) to learn the performance function (reward) used in reinforcement +learning. We illustrate the method with a spoken dialogue system named ELVIS +(EmaiL Voice Interactive System), that supports access to email over the phone. +We conduct a set of experiments for training an optimal dialogue strategy on a +corpus of 219 dialogues in which human users interact with ELVIS over the +phone. We then test that strategy on a corpus of 18 dialogues. We show that +ELVIS can learn to optimize its strategy selection for agent initiative, for +reading messages, and for summarizing email folders. +","An Application of Reinforcement Learning to Dialogue Strategy Selection + in a Spoken Dialogue System for Email" +" We show that for several variations of partially observable Markov decision +processes, polynomial-time algorithms for finding control policies are unlikely +to or simply don't have guarantees of finding policies within a constant factor +or a constant summand of optimal. Here ""unlikely"" means ""unless some complexity +classes collapse,"" where the collapses considered are P=NP, P=PSPACE, or P=EXP. +Until or unless these collapses are shown to hold, any control-policy designer +must choose between such performance guarantees and efficient computation. +","Nonapproximability Results for Partially Observable Markov Decision + Processes" +" The paper addresses the problem of computing goal orderings, which is one of +the longstanding issues in AI planning. It makes two new contributions. First, +it formally defines and discusses two different goal orderings, which are +called the reasonable and the forced ordering. Both orderings are defined for +simple STRIPS operators as well as for more complex ADL operators supporting +negation and conditional effects. The complexity of these orderings is +investigated and their practical relevance is discussed. Secondly, two +different methods to compute reasonable goal orderings are developed. One of +them is based on planning graphs, while the other investigates the set of +actions directly. Finally, it is shown how the ordering relations, which have +been derived for a given set of goals G, can be used to compute a so-called +goal agenda that divides G into an ordered set of subgoals. Any planner can +then, in principle, use the goal agenda to plan for increasing sets of +subgoals. This can lead to an exponential complexity reduction, as the solution +to a complex planning problem is found by solving easier subproblems. Since +only a polynomial overhead is caused by the goal agenda computation, a +potential exists to dramatically speed up planning algorithms as we demonstrate +in the empirical evaluation, where we use this method in the IPP planner. +","On Reasonable and Forced Goal Orderings and their Use in an + Agenda-Driven Planning Algorithm" +" The goal of this research is to develop agents that are adaptive and +predictable and timely. At first blush, these three requirements seem +contradictory. For example, adaptation risks introducing undesirable side +effects, thereby making agents' behavior less predictable. Furthermore, +although formal verification can assist in ensuring behavioral predictability, +it is known to be time-consuming. Our solution to the challenge of satisfying +all three requirements is the following. Agents have finite-state automaton +plans, which are adapted online via evolutionary learning (perturbation) +operators. To ensure that critical behavioral constraints are always satisfied, +agents' plans are first formally verified. They are then reverified after every +adaptation. If reverification concludes that constraints are violated, the +plans are repaired. The main objective of this paper is to improve the +efficiency of reverification after learning, so that agents have a sufficiently +rapid response time. We present two solutions: positive results that certain +learning operators are a priori guaranteed to preserve useful classes of +behavioral assurance constraints (which implies that no reverification is +needed for these operators), and efficient incremental reverification +algorithms for those learning operators that have negative a priori results. +",Asimovian Adaptive Agents +" A major problem in machine learning is that of inductive bias: how to choose +a learner's hypothesis space so that it is large enough to contain a solution +to the problem being learnt, yet small enough to ensure reliable generalization +from reasonably-sized training sets. Typically such bias is supplied by hand +through the skill and insights of experts. In this paper a model for +automatically learning bias is investigated. The central assumption of the +model is that the learner is embedded within an environment of related learning +tasks. Within such an environment the learner can sample from multiple tasks, +and hence it can search for a hypothesis space that contains good solutions to +many of the problems in the environment. Under certain restrictions on the set +of all hypothesis spaces available to the learner, we show that a hypothesis +space that performs well on a sufficiently large number of training tasks will +also perform well when learning novel tasks in the same environment. Explicit +bounds are also derived demonstrating that learning multiple tasks within an +environment of related tasks can potentially give much better generalization +than learning a single task. +",A Model of Inductive Bias Learning +" The chief aim of this paper is to propose mean-field approximations for a +broad class of Belief networks, of which sigmoid and noisy-or networks can be +seen as special cases. The approximations are based on a powerful mean-field +theory suggested by Plefka. We show that Saul, Jaakkola and Jordan' s approach +is the first order approximation in Plefka's approach, via a variational +derivation. The application of Plefka's theory to belief networks is not +computationally tractable. To tackle this problem we propose new approximations +based on Taylor series. Small scale experiments show that the proposed schemes +are attractive. +",Mean Field Methods for a Special Class of Belief Networks +" The recent approaches of extending the GRAPHPLAN algorithm to handle more +expressive planning formalisms raise the question of what the formal meaning of +""expressive power"" is. We formalize the intuition that expressive power is a +measure of how concisely planning domains and plans can be expressed in a +particular formalism by introducing the notion of ""compilation schemes"" between +planning formalisms. Using this notion, we analyze the expressiveness of a +large family of propositional planning formalisms, ranging from basic STRIPS to +a formalism with conditional effects, partial state specifications, and +propositional formulae in the preconditions. One of the results is that +conditional effects cannot be compiled away if plan size should grow only +linearly but can be compiled away if we allow for polynomial growth of the +resulting plans. This result confirms that the recently proposed extensions to +the GRAPHPLAN algorithm concerning conditional effects are optimal with respect +to the ""compilability"" framework. Another result is that general propositional +formulae cannot be compiled into conditional effects if the plan size should be +preserved linearly. This implies that allowing general propositional formulae +in preconditions and effect conditions adds another level of difficulty in +generating a plan. +","On the Compilability and Expressive Power of Propositional Planning + Formalisms" +" In order to generate plans for agents with multiple actuators, agent teams, +or distributed controllers, we must be able to represent and plan using +concurrent actions with interacting effects. This has historically been +considered a challenging task requiring a temporal planner with the ability to +reason explicitly about time. We show that with simple modifications, the +STRIPS action representation language can be used to represent interacting +actions. Moreover, algorithms for partial-order planning require only small +modifications in order to be applied in such multiagent domains. We demonstrate +this fact by developing a sound and complete partial-order planner for planning +with concurrent interacting actions, POMP, that extends existing partial-order +planners in a straightforward way. These results open the way to the use of +partial-order planners for the centralized control of cooperative multiagent +systems. +",Partial-Order Planning with Concurrent Interacting Actions +" Domain-independent planning is a hard combinatorial problem. Taking into +account plan quality makes the task even more difficult. This article +introduces Planning by Rewriting (PbR), a new paradigm for efficient +high-quality domain-independent planning. PbR exploits declarative +plan-rewriting rules and efficient local search techniques to transform an +easy-to-generate, but possibly suboptimal, initial plan into a high-quality +plan. In addition to addressing the issues of planning efficiency and plan +quality, this framework offers a new anytime planning algorithm. We have +implemented this planner and applied it to several existing domains. The +experimental results show that the PbR approach provides significant savings in +planning effort while generating high-quality plans. +",Planning by Rewriting +" Partially observable Markov decision processes (POMDPs) have recently become +popular among many AI researchers because they serve as a natural model for +planning under uncertainty. Value iteration is a well-known algorithm for +finding optimal policies for POMDPs. It typically takes a large number of +iterations to converge. This paper proposes a method for accelerating the +convergence of value iteration. The method has been evaluated on an array of +benchmark problems and was found to be very effective: It enabled value +iteration to converge after only a few iterations on all the test problems. +","Speeding Up the Convergence of Value Iteration in Partially Observable + Markov Decision Processes" +" We tackle the problem of planning in nondeterministic domains, by presenting +a new approach to conformant planning. Conformant planning is the problem of +finding a sequence of actions that is guaranteed to achieve the goal despite +the nondeterminism of the domain. Our approach is based on the representation +of the planning domain as a finite state automaton. We use Symbolic Model +Checking techniques, in particular Binary Decision Diagrams, to compactly +represent and efficiently search the automaton. In this paper we make the +following contributions. First, we present a general planning algorithm for +conformant planning, which applies to fully nondeterministic domains, with +uncertainty in the initial condition and in action effects. The algorithm is +based on a breadth-first, backward search, and returns conformant plans of +minimal length, if a solution to the planning problem exists, otherwise it +terminates concluding that the problem admits no conformant solution. Second, +we provide a symbolic representation of the search space based on Binary +Decision Diagrams (BDDs), which is the basis for search techniques derived from +symbolic model checking. The symbolic representation makes it possible to +analyze potentially large sets of states and transitions in a single +computation step, thus providing for an efficient implementation. Third, we +present CMBP (Conformant Model Based Planner), an efficient implementation of +the data structures and algorithm described above, directly based on BDD +manipulations, which allows for a compact representation of the search layers +and an efficient implementation of the search steps. Finally, we present an +experimental comparison of our approach with the state-of-the-art conformant +planners CGP, QBFPLAN and GPT. Our analysis includes all the planning problems +from the distribution packages of these systems, plus other problems defined to +stress a number of specific factors. Our approach appears to be the most +effective: CMBP is strictly more expressive than QBFPLAN and CGP and, in all +the problems where a comparison is possible, CMBP outperforms its competitors, +sometimes by orders of magnitude. +",Conformant Planning via Symbolic Model Checking +" Stochastic sampling algorithms, while an attractive alternative to exact +algorithms in very large Bayesian network models, have been observed to perform +poorly in evidential reasoning with extremely unlikely evidence. To address +this problem, we propose an adaptive importance sampling algorithm, AIS-BN, +that shows promising convergence rates even under extreme conditions and seems +to outperform the existing sampling algorithms consistently. Three sources of +this performance improvement are (1) two heuristics for initialization of the +importance function that are based on the theoretical properties of importance +sampling in finite-dimensional integrals and the structural advantages of +Bayesian networks, (2) a smooth learning method for the importance function, +and (3) a dynamic weighting function for combining samples from different +stages of the algorithm. We tested the performance of the AIS-BN algorithm +along with two state of the art general purpose sampling algorithms, likelihood +weighting (Fung and Chang, 1989; Shachter and Peot, 1989) and self-importance +sampling (Shachter and Peot, 1989). We used in our tests three large real +Bayesian network models available to the scientific community: the CPCS network +(Pradhan et al., 1994), the PathFinder network (Heckerman, Horvitz, and +Nathwani, 1990), and the ANDES network (Conati, Gertner, VanLehn, and Druzdzel, +1997), with evidence as unlikely as 10^-41. While the AIS-BN algorithm always +performed better than the other two algorithms, in the majority of the test +cases it achieved orders of magnitude improvement in precision of the results. +Improvement in speed given a desired precision is even more dramatic, although +we are unable to report numerical results here, as the other algorithms almost +never achieved the precision reached even by the first few iterations of the +AIS-BN algorithm. +","AIS-BN: An Adaptive Importance Sampling Algorithm for Evidential + Reasoning in Large Bayesian Networks" +" In recent years, many improvements to backtracking algorithms for solving +constraint satisfaction problems have been proposed. The techniques for +improving backtracking algorithms can be conveniently classified as look-ahead +schemes and look-back schemes. Unfortunately, look-ahead and look-back schemes +are not entirely orthogonal as it has been observed empirically that the +enhancement of look-ahead techniques is sometimes counterproductive to the +effects of look-back techniques. In this paper, we focus on the relationship +between the two most important look-ahead techniques---using a variable +ordering heuristic and maintaining a level of local consistency during the +backtracking search---and the look-back technique of conflict-directed +backjumping (CBJ). We show that there exists a ""perfect"" dynamic variable +ordering such that CBJ becomes redundant. We also show theoretically that as +the level of local consistency that is maintained in the backtracking search is +increased, the less that backjumping will be an improvement. Our theoretical +results partially explain why a backtracking algorithm doing more in the +look-ahead phase cannot benefit more from the backjumping look-back scheme. +Finally, we show empirically that adding CBJ to a backtracking algorithm that +maintains generalized arc consistency (GAC), an algorithm that we refer to as +GAC-CBJ, can still provide orders of magnitude speedups. Our empirical results +contrast with Bessiere and Regin's conclusion (1996) that CBJ is useless to an +algorithm that maintains arc consistency. +",Conflict-Directed Backjumping Revisited +" This paper presents an implemented system for recognizing the occurrence of +events described by simple spatial-motion verbs in short image sequences. The +semantics of these verbs is specified with event-logic expressions that +describe changes in the state of force-dynamic relations between the +participants of the event. An efficient finite representation is introduced for +the infinite sets of intervals that occur when describing liquid and +semi-liquid events. Additionally, an efficient procedure using this +representation is presented for inferring occurrences of compound events, +described with event-logic expressions, from occurrences of primitive events. +Using force dynamics and event logic to specify the lexical semantics of events +allows the system to be more robust than prior systems based on motion profile. +","Grounding the Lexical Semantics of Verbs in Visual Perception using + Force Dynamics and Event Logic" +" An ensemble consists of a set of individually trained classifiers (such as +neural networks or decision trees) whose predictions are combined when +classifying novel instances. Previous research has shown that an ensemble is +often more accurate than any of the single classifiers in the ensemble. Bagging +(Breiman, 1996c) and Boosting (Freund and Shapire, 1996; Shapire, 1990) are two +relatively new but popular methods for producing ensembles. In this paper we +evaluate these methods on 23 data sets using both neural networks and decision +trees as our classification algorithm. Our results clearly indicate a number of +conclusions. First, while Bagging is almost always more accurate than a single +classifier, it is sometimes much less accurate than Boosting. On the other +hand, Boosting can create ensembles that are less accurate than a single +classifier -- especially when using neural networks. Analysis indicates that +the performance of the Boosting methods is dependent on the characteristics of +the data set being examined. In fact, further results show that Boosting +ensembles may overfit noisy data sets, thus decreasing its performance. +Finally, consistent with previous studies, our work suggests that most of the +gain in an ensemble's performance comes in the first few classifiers combined; +however, relatively large gains can be seen up to 25 classifiers when Boosting +decision trees. +",Popular Ensemble Methods: An Empirical Study +" This paper presents an evolutionary algorithm with a new goal-sequence +domination scheme for better decision support in multi-objective optimization. +The approach allows the inclusion of advanced hard/soft priority and constraint +information on each objective component, and is capable of incorporating +multiple specifications with overlapping or non-overlapping objective functions +via logical 'OR' and 'AND' connectives to drive the search towards multiple +regions of trade-off. In addition, we propose a dynamic sharing scheme that is +simple and adaptively estimated according to the on-line population +distribution without needing any a priori parameter setting. Each feature in +the proposed algorithm is examined to show its respective contribution, and the +performance of the algorithm is compared with other evolutionary optimization +methods. It is shown that the proposed algorithm has performed well in the +diversity of evolutionary search and uniform distribution of non-dominated +individuals along the final trade-offs, without significant computational +effort. The algorithm is also applied to the design optimization of a practical +servo control system for hard disk drives with a single voice-coil-motor +actuator. Results of the evolutionary designed servo control system show a +superior closed-loop performance compared to classical PID or RPT approaches. +","An Evolutionary Algorithm with Advanced Goal and Priority Specification + for Multi-objective Optimization" +" This paper presents GRT, a domain-independent heuristic planning system for +STRIPS worlds. GRT solves problems in two phases. In the pre-processing phase, +it estimates the distance between each fact and the goals of the problem, in a +backward direction. Then, in the search phase, these estimates are used in +order to further estimate the distance between each intermediate state and the +goals, guiding so the search process in a forward direction and on a best-first +basis. The paper presents the benefits from the adoption of opposite directions +between the preprocessing and the search phases, discusses some difficulties +that arise in the pre-processing phase and introduces techniques to cope with +them. Moreover, it presents several methods of improving the efficiency of the +heuristic, by enriching the representation and by reducing the size of the +problem. Finally, a method of overcoming local optimal states, based on domain +axioms, is proposed. According to it, difficult problems are decomposed into +easier sub-problems that have to be solved sequentially. The performance +results from various domains, including those of the recent planning +competitions, show that GRT is among the fastest planners. +","The GRT Planning System: Backward Heuristic Construction in Forward + State-Space Planning" +" In the recent literature of Artificial Intelligence, an intensive research +effort has been spent, for various algebras of qualitative relations used in +the representation of temporal and spatial knowledge, on the problem of +classifying the computational complexity of reasoning problems for subsets of +algebras. The main purpose of these researches is to describe a restricted set +of maximal tractable subalgebras, ideally in an exhaustive fashion with respect +to the hosting algebras. In this paper we introduce a novel algebra for +reasoning about Spatial Congruence, show that the satisfiability problem in the +spatial algebra MC-4 is NP-complete, and present a complete classification of +tractability in the algebra, based on the individuation of three maximal +tractable subclasses, one containing the basic relations. The three algebras +are formed by 14, 10 and 9 relations out of 16 which form the full algebra. +",The Complexity of Reasoning about Spatial Congruence +" Gradient-based approaches to direct policy search in reinforcement learning +have received much recent attention as a means to solve problems of partial +observability and to avoid some of the problems associated with policy +degradation in value-function methods. In this paper we introduce GPOMDP, a +simulation-based algorithm for generating a {\em biased} estimate of the +gradient of the {\em average reward} in Partially Observable Markov Decision +Processes (POMDPs) controlled by parameterized stochastic policies. A similar +algorithm was proposed by Kimura, Yamamura, and Kobayashi (1995). The +algorithm's chief advantages are that it requires storage of only twice the +number of policy parameters, uses one free parameter $\beta\in [0,1)$ (which +has a natural interpretation in terms of bias-variance trade-off), and requires +no knowledge of the underlying state. We prove convergence of GPOMDP, and show +how the correct choice of the parameter $\beta$ is related to the {\em mixing +time} of the controlled POMDP. We briefly describe extensions of GPOMDP to +controlled Markov chains, continuous state, observation and control spaces, +multiple-agents, higher-order derivatives, and a version for training +stochastic policies with internal states. In a companion paper (Baxter, +Bartlett, & Weaver, 2001) we show how the gradient estimates generated by +GPOMDP can be used in both a traditional stochastic gradient algorithm and a +conjugate-gradient procedure to find local optima of the average reward +",Infinite-Horizon Policy-Gradient Estimation +" Description Logics (DLs) are suitable, well-known, logics for managing +structured knowledge. They allow reasoning about individuals and well defined +concepts, i.e., set of individuals with common properties. The experience in +using DLs in applications has shown that in many cases we would like to extend +their capabilities. In particular, their use in the context of Multimedia +Information Retrieval (MIR) leads to the convincement that such DLs should +allow the treatment of the inherent imprecision in multimedia object content +representation and retrieval. In this paper we will present a fuzzy extension +of ALC, combining Zadeh's fuzzy logic with a classical DL. In particular, +concepts becomes fuzzy and, thus, reasoning about imprecise concepts is +supported. We will define its syntax, its semantics, describe its properties +and present a constraint propagation calculus for reasoning in it. +",Reasoning within Fuzzy Description Logics +" Top-down induction of decision trees has been observed to suffer from the +inadequate functioning of the pruning phase. In particular, it is known that +the size of the resulting tree grows linearly with the sample size, even though +the accuracy of the tree does not improve. Reduced Error Pruning is an +algorithm that has been used as a representative technique in attempts to +explain the problems of decision tree learning. In this paper we present +analyses of Reduced Error Pruning in three different settings. First we study +the basic algorithmic properties of the method, properties that hold +independent of the input decision tree and pruning examples. Then we examine a +situation that intuitively should lead to the subtree under consideration to be +replaced by a leaf node, one in which the class label and attribute values of +the pruning examples are independent of each other. This analysis is conducted +under two different assumptions. The general analysis shows that the pruning +probability of a node fitting pure noise is bounded by a function that +decreases exponentially as the size of the tree grows. In a specific analysis +we assume that the examples are distributed uniformly to the tree. This +assumption lets us approximate the number of subtrees that are pruned because +they do not receive any pruning examples. This paper clarifies the different +variants of the Reduced Error Pruning algorithm, brings new insight to its +algorithmic properties, analyses the algorithm with less imposed assumptions +than before, and includes the previously overlooked empty subtrees to the +analysis. +",An Analysis of Reduced Error Pruning +" This paper investigates the problems arising in the construction of a program +to play the game of contract bridge. These problems include both the difficulty +of solving the game's perfect information variant, and techniques needed to +address the fact that bridge is not, in fact, a perfect information game. GIB, +the program being described, involves five separate technical advances: +partition search, the practical application of Monte Carlo techniques to +realistic problems, a focus on achievable sets to solve problems inherent in +the Monte Carlo approach, an extension of alpha-beta pruning from total orders +to arbitrary distributive lattices, and the use of squeaky wheel optimization +to find approximately optimal solutions to cardplay problems. GIB is currently +believed to be of approximately expert caliber, and is currently the strongest +computer bridge program in the world. +",GIB: Imperfect Information in a Computationally Challenging Game +" Enforcing local consistencies is one of the main features of constraint +reasoning. Which level of local consistency should be used when searching for +solutions in a constraint network is a basic question. Arc consistency and +partial forms of arc consistency have been widely studied, and have been known +for sometime through the forward checking or the MAC search algorithms. Until +recently, stronger forms of local consistency remained limited to those that +change the structure of the constraint graph, and thus, could not be used in +practice, especially on large networks. This paper focuses on the local +consistencies that are stronger than arc consistency, without changing the +structure of the network, i.e., only removing inconsistent values from the +domains. In the last five years, several such local consistencies have been +proposed by us or by others. We make an overview of all of them, and highlight +some relations between them. We compare them both theoretically and +experimentally, considering their pruning efficiency and the time required to +enforce them. +",Domain Filtering Consistencies +" In this paper, we present a method for recognising an agent's behaviour in +dynamic, noisy, uncertain domains, and across multiple levels of abstraction. +We term this problem on-line plan recognition under uncertainty and view it +generally as probabilistic inference on the stochastic process representing the +execution of the agent's plan. Our contributions in this paper are twofold. In +terms of probabilistic inference, we introduce the Abstract Hidden Markov Model +(AHMM), a novel type of stochastic processes, provide its dynamic Bayesian +network (DBN) structure and analyse the properties of this network. We then +describe an application of the Rao-Blackwellised Particle Filter to the AHMM +which allows us to construct an efficient, hybrid inference method for this +model. In terms of plan recognition, we propose a novel plan recognition +framework based on the AHMM as the plan execution model. The Rao-Blackwellised +hybrid inference for AHMM can take advantage of the independence properties +inherent in a model of plan execution, leading to an algorithm for online +probabilistic plan recognition that scales well with the number of levels in +the plan hierarchy. This illustrates that while stochastic models for plan +execution can be complex, they exhibit special structures which, if exploited, +can lead to efficient plan recognition algorithms. We demonstrate the +usefulness of the AHMM framework via a behaviour recognition system in a +complex spatial environment using distributed video surveillance data. +",Policy Recognition in the Abstract Hidden Markov Model +" We describe and evaluate the algorithmic techniques that are used in the FF +planning system. Like the HSP system, FF relies on forward state space search, +using a heuristic that estimates goal distances by ignoring delete lists. +Unlike HSP's heuristic, our method does not assume facts to be independent. We +introduce a novel search strategy that combines hill-climbing with systematic +search, and we show how other powerful heuristic information can be extracted +and used to prune the search space. FF was the most successful automatic +planner at the recent AIPS-2000 planning competition. We review the results of +the competition, give data for other benchmark domains, and investigate the +reasons for the runtime performance of FF compared to HSP. +",The FF Planning System: Fast Plan Generation Through Heuristic Search +" The First Trading Agent Competition (TAC) was held from June 22nd to July +8th, 2000. TAC was designed to create a benchmark problem in the complex domain +of e-marketplaces and to motivate researchers to apply unique approaches to a +common task. This article describes ATTac-2000, the first-place finisher in +TAC. ATTac-2000 uses a principled bidding strategy that includes several +elements of adaptivity. In addition to the success at the competition, isolated +empirical results are presented indicating the robustness and effectiveness of +ATTac-2000's adaptive strategy. +",ATTac-2000: An Adaptive Autonomous Bidding Agent +" The theoretical properties of qualitative spatial reasoning in the RCC8 +framework have been analyzed extensively. However, no empirical investigation +has been made yet. Our experiments show that the adaption of the algorithms +used for qualitative temporal reasoning can solve large RCC8 instances, even if +they are in the phase transition region -- provided that one uses the maximal +tractable subsets of RCC8 that have been identified by us. In particular, we +demonstrate that the orthogonal combination of heuristic methods is successful +in solving almost all apparently hard instances in the phase transition region +up to a certain size in reasonable time. +",Efficient Methods for Qualitative Spatial Reasoning +" This paper presents our work on development of OWL-driven systems for formal +representation and reasoning about terminological knowledge and facts in +petrology. The long-term aim of our project is to provide solid foundations for +a large-scale integration of various kinds of knowledge, including basic terms, +rock classification algorithms, findings and reports. We describe three steps +we have taken towards that goal here. First, we develop a semi-automated +procedure for transforming a database of igneous rock samples to texts in a +controlled natural language (CNL), and then a collection of OWL ontologies. +Second, we create an OWL ontology of important petrology terms currently +described in natural language thesauri. We describe a prototype of a tool for +collecting definitions from domain experts. Third, we present an approach to +formalization of current industrial standards for classification of rock +samples, which requires linear equations in OWL 2. In conclusion, we discuss a +range of opportunities arising from the use of semantic technologies in +petrology and outline the future work in this area. +",Towards OWL-based Knowledge Representation in Petrology +" This paper discusses a system that accelerates reinforcement learning by +using transfer from related tasks. Without such transfer, even if two tasks are +very similar at some abstract level, an extensive re-learning effort is +required. The system achieves much of its power by transferring parts of +previously learned solutions rather than a single complete solution. The system +exploits strong features in the multi-dimensional function produced by +reinforcement learning in solving a particular task. These features are stable +and easy to recognize early in the learning process. They generate a +partitioning of the state space and thus the function. The partition is +represented as a graph. This is used to index and compose functions stored in a +case base to form a close approximation to the solution of the new task. +Experiments demonstrate that function composition often produces more than an +order of magnitude increase in learning rate compared to a basic reinforcement +learning algorithm. +","Accelerating Reinforcement Learning by Composing Solutions of + Automatically Identified Subtasks" +" We propose a logical/mathematical framework for statistical parameter +learning of parameterized logic programs, i.e. definite clause programs +containing probabilistic facts with a parameterized distribution. It extends +the traditional least Herbrand model semantics in logic programming to +distribution semantics, possible world semantics with a probability +distribution which is unconditionally applicable to arbitrary logic programs +including ones for HMMs, PCFGs and Bayesian networks. We also propose a new EM +algorithm, the graphical EM algorithm, that runs for a class of parameterized +logic programs representing sequential decision processes where each decision +is exclusive and independent. It runs on a new data structure called support +graphs describing the logical relationship between observations and their +explanations, and learns parameters by computing inside and outside probability +generalized for logic programs. The complexity analysis shows that when +combined with OLDT search for all explanations for observations, the graphical +EM algorithm, despite its generality, has the same time complexity as existing +EM algorithms, i.e. the Baum-Welch algorithm for HMMs, the Inside-Outside +algorithm for PCFGs, and the one for singly connected Bayesian networks that +have been developed independently in each research field. Learning experiments +with PCFGs using two corpora of moderate size indicate that the graphical EM +algorithm can significantly outperform the Inside-Outside algorithm. +",Parameter Learning of Logic Programs for Symbolic-Statistical Modeling +" I consider the problem of learning an optimal path graphical model from data +and show the problem to be NP-hard for the maximum likelihood and minimum +description length approaches and a Bayesian approach. This hardness result +holds despite the fact that the problem is a restriction of the polynomially +solvable problem of finding the optimal tree graphical model. +",Finding a Path is Harder than Finding a Tree +" Simple conceptual graphs are considered as the kernel of most knowledge +representation formalisms built upon Sowa's model. Reasoning in this model can +be expressed by a graph homomorphism called projection, whose semantics is +usually given in terms of positive, conjunctive, existential FOL. We present +here a family of extensions of this model, based on rules and constraints, +keeping graph homomorphism as the basic operation. We focus on the formal +definitions of the different models obtained, including their operational +semantics and relationships with FOL, and we analyze the decidability and +complexity of the associated problems (consistency and deduction). As soon as +rules are involved in reasonings, these problems are not decidable, but we +exhibit a condition under which they fall in the polynomial hierarchy. These +results extend and complete the ones already published by the authors. Moreover +we systematically study the complexity of some particular cases obtained by +restricting the form of constraints and/or rules. +","Extensions of Simple Conceptual Graphs: the Complexity of Rules and + Constraints" +" Fusions are a simple way of combining logics. For normal modal logics, +fusions have been investigated in detail. In particular, it is known that, +under certain conditions, decidability transfers from the component logics to +their fusion. Though description logics are closely related to modal logics, +they are not necessarily normal. In addition, ABox reasoning in description +logics is not covered by the results from modal logics. In this paper, we +extend the decidability transfer results from normal modal logics to a large +class of description logics. To cover different description logics in a uniform +way, we introduce abstract description systems, which can be seen as a common +generalization of description and modal logics, and show the transfer results +in this general setting. +",Fusions of Description Logics and Abstract Description Systems +" Inductive logic programming, or relational learning, is a powerful paradigm +for machine learning or data mining. However, in order for ILP to become +practically useful, the efficiency of ILP systems must improve substantially. +To this end, the notion of a query pack is introduced: it structures sets of +similar queries. Furthermore, a mechanism is described for executing such query +packs. A complexity analysis shows that considerable efficiency improvements +can be achieved through the use of this query pack execution mechanism. This +claim is supported by empirical results obtained by incorporating support for +query pack execution in two existing learning systems. +","Improving the Efficiency of Inductive Logic Programming Through the Use + of Query Packs" +" Recent trends in planning research have led to empirical comparison becoming +commonplace. The field has started to settle into a methodology for such +comparisons, which for obvious practical reasons requires running a subset of +planners on a subset of problems. In this paper, we characterize the +methodology and examine eight implicit assumptions about the problems, planners +and metrics used in many of these comparisons. The problem assumptions are: +PR1) the performance of a general purpose planner should not be +penalized/biased if executed on a sampling of problems and domains, PR2) minor +syntactic differences in representation do not affect performance, and PR3) +problems should be solvable by STRIPS capable planners unless they require ADL. +The planner assumptions are: PL1) the latest version of a planner is the best +one to use, PL2) default parameter settings approximate good performance, and +PL3) time cut-offs do not unduly bias outcome. The metrics assumptions are: M1) +performance degrades similarly for each planner when run on degraded runtime +environments (e.g., machine platform) and M2) the number of plan steps +distinguishes performance. We find that most of these assumptions are not +supported empirically; in particular, that planners are affected differently by +these assumptions. We conclude with a call to the community to devote research +resources to improving the state of the practice and especially to enhancing +the available benchmark problems. +",A Critical Assessment of Benchmark Comparison in Planning +" An approach to the construction of classifiers from imbalanced datasets is +described. A dataset is imbalanced if the classification categories are not +approximately equally represented. Often real-world data sets are predominately +composed of ""normal"" examples with only a small percentage of ""abnormal"" or +""interesting"" examples. It is also the case that the cost of misclassifying an +abnormal (interesting) example as a normal example is often much higher than +the cost of the reverse error. Under-sampling of the majority (normal) class +has been proposed as a good means of increasing the sensitivity of a classifier +to the minority class. This paper shows that a combination of our method of +over-sampling the minority (abnormal) class and under-sampling the majority +(normal) class can achieve better classifier performance (in ROC space) than +only under-sampling the majority class. This paper also shows that a +combination of our method of over-sampling the minority class and +under-sampling the majority class can achieve better classifier performance (in +ROC space) than varying the loss ratios in Ripper or class priors in Naive +Bayes. Our method of over-sampling the minority class involves creating +synthetic minority class examples. Experiments are performed using C4.5, Ripper +and a Naive Bayes classifier. The method is evaluated using the area under the +Receiver Operating Characteristic curve (AUC) and the ROC convex hull strategy. +",SMOTE: Synthetic Minority Over-sampling Technique +" Common wisdom has it that small distinctions in the probabilities +(parameters) quantifying a belief network do not matter much for the results of +probabilistic queries. Yet, one can develop realistic scenarios under which +small variations in network parameters can lead to significant changes in +computed queries. A pending theoretical question is then to analytically +characterize parameter changes that do or do not matter. In this paper, we +study the sensitivity of probabilistic queries to changes in network parameters +and prove some tight bounds on the impact that such parameters can have on +queries. Our analytic results pinpoint some interesting situations under which +parameter changes do or do not matter. These results are important for +knowledge engineers as they help them identify influential network parameters. +They also help explain some of the previous experimental results and +observations with regards to network robustness against parameter changes. +",When do Numbers Really Matter? +" Recent years are seeing an increasing need for on-line monitoring of teams of +cooperating agents, e.g., for visualization, or performance tracking. However, +in monitoring deployed teams, we often cannot rely on the agents to always +communicate their state to the monitoring system. This paper presents a +non-intrusive approach to monitoring by 'overhearing', where the monitored +team's state is inferred (via plan-recognition) from team-members' routine +communications, exchanged as part of their coordinated task execution, and +observed (overheard) by the monitoring system. Key challenges in this approach +include the demanding run-time requirements of monitoring, the scarceness of +observations (increasing monitoring uncertainty), and the need to scale-up +monitoring to address potentially large teams. To address these, we present a +set of complementary novel techniques, exploiting knowledge of the social +structures and procedures in the monitored team: (i) an efficient probabilistic +plan-recognition algorithm, well-suited for processing communications as +observations; (ii) an approach to exploiting knowledge of the team's social +behavior to predict future observations during execution (reducing monitoring +uncertainty); and (iii) monitoring algorithms that trade expressivity for +scalability, representing only certain useful monitoring hypotheses, but +allowing for any number of agents and their different activities to be +represented in a single coherent entity. We present an empirical evaluation of +these techniques, in combination and apart, in monitoring a deployed team of +agents, running on machines physically distributed across the country, and +engaged in complex, dynamic task execution. We also compare the performance of +these techniques to human expert and novice monitors, and show that the +techniques presented are capable of monitoring at human-expert levels, despite +the difficulty of the task. +",Monitoring Teams by Overhearing: A Multi-Agent Plan-Recognition Approach +" Spoken dialogue systems promise efficient and natural access to a large +variety of information sources and services from any phone. However, current +spoken dialogue systems are deficient in their strategies for preventing, +identifying and repairing problems that arise in the conversation. This paper +reports results on automatically training a Problematic Dialogue Predictor to +predict problematic human-computer dialogues using a corpus of 4692 dialogues +collected with the 'How May I Help You' (SM) spoken dialogue system. The +Problematic Dialogue Predictor can be immediately applied to the system's +decision of whether to transfer the call to a human customer care agent, or be +used as a cue to the system's dialogue manager to modify its behavior to repair +problems, and even perhaps, to prevent them. We show that a Problematic +Dialogue Predictor using automatically-obtainable features from the first two +exchanges in the dialogue can predict problematic dialogues 13.2% more +accurately than the baseline. +","Automatically Training a Problematic Dialogue Predictor for a Spoken + Dialogue System" +" Recent advances in the study of voting classification algorithms have brought +empirical and theoretical results clearly showing the discrimination power of +ensemble classifiers. It has been previously argued that the search of this +classification power in the design of the algorithms has marginalized the need +to obtain interpretable classifiers. Therefore, the question of whether one +might have to dispense with interpretability in order to keep classification +strength is being raised in a growing number of machine learning or data mining +papers. The purpose of this paper is to study both theoretically and +empirically the problem. First, we provide numerous results giving insight into +the hardness of the simplicity-accuracy tradeoff for voting classifiers. Then +we provide an efficient ""top-down and prune"" induction heuristic, WIDC, mainly +derived from recent results on the weak learning and boosting frameworks. It is +to our knowledge the first attempt to build a voting classifier as a base +formula using the weak learning framework (the one which was previously highly +successful for decision tree induction), and not the strong learning framework +(as usual for such classifiers with boosting-like approaches). While it uses a +well-known induction scheme previously successful in other classes of concept +representations, thus making it easy to implement and compare, WIDC also relies +on recent or new results we give about particular cases of boosting known as +partition boosting and ranking loss boosting. Experimental results on +thirty-one domains, most of which readily available, tend to display the +ability of WIDC to produce small, accurate, and interpretable decision +committees. +","Inducing Interpretable Voting Classifiers without Trading Accuracy for + Simplicity: Theoretical Results, Approximation Algorithms" +" We propose a perspective on knowledge compilation which calls for analyzing +different compilation approaches according to two key dimensions: the +succinctness of the target compilation language, and the class of queries and +transformations that the language supports in polytime. We then provide a +knowledge compilation map, which analyzes a large number of existing target +compilation languages according to their succinctness and their polytime +transformations and queries. We argue that such analysis is necessary for +placing new compilation approaches within the context of existing ones. We also +go beyond classical, flat target compilation languages based on CNF and DNF, +and consider a richer, nested class based on directed acyclic graphs (such as +OBDDs), which we show to include a relatively large number of target +compilation languages. +",A Knowledge Compilation Map +" The problem of organizing information for multidocument summarization so that +the generated summary is coherent has received relatively little attention. +While sentence ordering for single document summarization can be determined +from the ordering of sentences in the input article, this is not the case for +multidocument summarization where summary sentences may be drawn from different +input articles. In this paper, we propose a methodology for studying the +properties of ordering information in the news genre and describe experiments +done on a corpus of multiple acceptable orderings we developed for the task. +Based on these experiments, we implemented a strategy for ordering information +that combines constraints from chronological order of events and topical +relatedness. Evaluation of our augmented algorithm shows a significant +improvement of the ordering over two baseline strategies. +","Inferring Strategies for Sentence Ordering in Multidocument News + Summarization" +" We consider the problem of designing the the utility functions of the +utility-maximizing agents in a multi-agent system so that they work +synergistically to maximize a global utility. The particular problem domain we +explore is the control of network routing by placing agents on all the routers +in the network. Conventional approaches to this task have the agents all use +the Ideal Shortest Path routing Algorithm (ISPA). We demonstrate that in many +cases, due to the side-effects of one agent's actions on another agent's +performance, having agents use ISPA's is suboptimal as far as global aggregate +cost is concerned, even when they are only used to route infinitesimally small +amounts of traffic. The utility functions of the individual agents are not +""aligned"" with the global utility, intuitively speaking. As a particular +example of this we present an instance of Braess' paradox in which adding new +links to a network whose agents all use the ISPA results in a decrease in +overall throughput. We also demonstrate that load-balancing, in which the +agents' decisions are collectively made to optimize the global cost incurred by +all traffic currently being routed, is suboptimal as far as global cost +averaged across time is concerned. This is also due to 'side-effects', in this +case of current routing decision on future traffic. The mathematics of +Collective Intelligence (COIN) is concerned precisely with the issue of +avoiding such deleterious side-effects in multi-agent systems, both over time +and space. We present key concepts from that mathematics and use them to derive +an algorithm whose ideal version should have better performance than that of +having all agents use the ISPA, even in the infinitesimal limit. We present +experiments verifying this, and also showing that a machine-learning-based +version of this COIN algorithm in which costs are only imprecisely estimated +via empirical means (a version potentially applicable in the real world) also +outperforms the ISPA, despite having access to less information than does the +ISPA. In particular, this COIN algorithm almost always avoids Braess' paradox. +","Collective Intelligence, Data Routing and Braess' Paradox" +" This paper addresses the problem of planning under uncertainty in large +Markov Decision Processes (MDPs). Factored MDPs represent a complex state space +using state variables and the transition model using a dynamic Bayesian +network. This representation often allows an exponential reduction in the +representation size of structured MDPs, but the complexity of exact solution +algorithms for such MDPs can grow exponentially in the representation size. In +this paper, we present two approximate solution algorithms that exploit +structure in factored MDPs. Both use an approximate value function represented +as a linear combination of basis functions, where each basis function involves +only a small subset of the domain variables. A key contribution of this paper +is that it shows how the basic operations of both algorithms can be performed +efficiently in closed form, by exploiting both additive and context-specific +structure in a factored MDP. A central element of our algorithms is a novel +linear program decomposition technique, analogous to variable elimination in +Bayesian networks, which reduces an exponentially large LP to a provably +equivalent, polynomial-sized one. One algorithm uses approximate linear +programming, and the second approximate dynamic programming. Our dynamic +programming algorithm is novel in that it uses an approximation based on +max-norm, a technique that more directly minimizes the terms that appear in +error bounds for approximate MDP algorithms. We provide experimental results on +problems with over 10^40 states, demonstrating a promising indication of the +scalability of our approach, and compare our algorithm to an existing +state-of-the-art approach, showing, in some problems, exponential gains in +computation time. +",Efficient Solution Algorithms for Factored MDPs +" The human intelligence lies in the algorithm, the nature of algorithm lies in +the classification, and the classification is equal to outlier detection. A lot +of algorithms have been proposed to detect outliers, meanwhile a lot of +definitions. Unsatisfying point is that definitions seem vague, which makes the +solution an ad hoc one. We analyzed the nature of outliers, and give two clear +definitions. We then develop an efficient RDD algorithm, which converts outlier +problem to pattern and degree problem. Furthermore, a collapse mechanism was +introduced by IIR algorithm, which can be united seamlessly with the RDD +algorithm and serve for the final decision. Both algorithms are originated from +the study on general AI. The combined edition is named as Pe algorithm, which +is the basis of the intelligent decision. Here we introduce longest k-turn +subsequence problem and corresponding solution as an example to interpret the +function of Pe algorithm in detecting curve-type outliers. We also give a +comparison between IIR algorithm and Pe algorithm, where we can get a better +understanding at both algorithms. A short discussion about intelligence is +added to demonstrate the function of the Pe algorithm. Related experimental +results indicate its robustness. +",Intelligent decision: towards interpreting the Pe Algorithm +" We present a novel Natural Evolution Strategy (NES) variant, the Rank-One NES +(R1-NES), which uses a low rank approximation of the search distribution +covariance matrix. The algorithm allows computation of the natural gradient +with cost linear in the dimensionality of the parameter space, and excels in +solving high-dimensional non-separable problems, including the best result to +date on the Rosenbrock function (512 dimensions). +",A Linear Time Natural Evolution Strategy for Non-Separable Functions +" Galles and Pearl claimed that ""for recursive models, the causal model +framework does not add any restrictions to counterfactuals, beyond those +imposed by Lewis's [possible-worlds] framework."" This claim is examined +carefully, with the goal of clarifying the exact relationship between causal +models and Lewis's framework. Recursive models are shown to correspond +precisely to a subclass of (possible-world) counterfactual structures. On the +other hand, a slight generalization of recursive models, models where all +equations have unique solutions, is shown to be incomparable in expressive +power to counterfactual structures, despite the fact that the Galles and Pearl +arguments should apply to them as well. The problem with the Galles and Pearl +argument is identified: an axiom that they viewed as irrelevant, because it +involved disjunction (which was not in their language), is not irrelevant at +all. +",From Causal Models To Counterfactual Structures +" We look more carefully at the modeling of causality using structural +equations. It is clear that the structural equations can have a major impact on +the conclusions we draw about causality. In particular, the choice of variables +and their values can also have a significant impact on causality. These choices +are, to some extent, subjective. We consider what counts as an appropriate +choice. More generally, we consider what makes a model an appropriate model, +especially if we want to take defaults into account, as was argued is necessary +in recent work. +",Actual causation and the art of modeling +" Two distinct algorithms are presented to extract (schemata of) resolution +proofs from closed tableaux for propositional schemata. The first one handles +the most efficient version of the tableau calculus but generates very complex +derivations (denoted by rather elaborate rewrite systems). The second one has +the advantage that much simpler systems can be obtained, however the considered +proof procedure is less efficient. +",Generating Schemata of Resolution Proofs +" In the current study we examine an application of the machine learning +methods to model the retention constants in the thin layer chromatography +(TLC). This problem can be described with hundreds or even thousands of +descriptors relevant to various molecular properties, most of them redundant +and not relevant for the retention constant prediction. Hence we employed +feature selection to significantly reduce the number of attributes. +Additionally we have tested application of the bagging procedure to the feature +selection. The random forest regression models were built using selected +variables. The resulting models have better correlation with the experimental +data than the reference models obtained with linear regression. The +cross-validation confirms robustness of the models. +","Random forest models of the retention constants in the thin layer + chromatography" +" Nowadays ontologies present a growing interest in Data Fusion applications. +As a matter of fact, the ontologies are seen as a semantic tool for describing +and reasoning about sensor data, objects, relations and general domain +theories. In addition, uncertainty is perhaps one of the most important +characteristics of the data and information handled by Data Fusion. However, +the fundamental nature of ontologies implies that ontologies describe only +asserted and veracious facts of the world. Different probabilistic, fuzzy and +evidential approaches already exist to fill this gap; this paper recaps the +most popular tools. However none of the tools meets exactly our purposes. +Therefore, we constructed a Dempster-Shafer ontology that can be imported into +any specific domain ontology and that enables us to instantiate it in an +uncertain manner. We also developed a Java application that enables reasoning +about these uncertain ontological instances. +","Uncertainty in Ontologies: Dempster-Shafer Theory for Data Fusion + Applications" +" Individuals have an intuitive perception of what makes a good coincidence. +Though the sensitivity to coincidences has often been presented as resulting +from an erroneous assessment of probability, it appears to be a genuine +competence, based on non-trivial computations. The model presented here +suggests that coincidences occur when subjects perceive complexity drops. +Co-occurring events are, together, simpler than if considered separately. This +model leads to a possible redefinition of subjective probability. +",Coincidences and the encounter problem: A formal account +" The study of opinions, their formation and change, is one of the defining +topics addressed by social psychology, but in recent years other disciplines, +like computer science and complexity, have tried to deal with this issue. +Despite the flourishing of different models and theories in both fields, +several key questions still remain unanswered. The understanding of how +opinions change and the way they are affected by social influence are +challenging issues requiring a thorough analysis of opinion per se but also of +the way in which they travel between agents' minds and are modulated by these +exchanges. To account for the two-faceted nature of opinions, which are mental +entities undergoing complex social processes, we outline a preliminary model in +which a cognitive theory of opinions is put forward and it is paired with a +formal description of them and of their spreading among minds. Furthermore, +investigating social influence also implies the necessity to account for the +way in which people change their minds, as a consequence of interacting with +other people, and the need to explain the higher or lower persistence of such +changes. +","Rooting opinions in the minds: a cognitive model and a formal account of + opinions and their dynamics" +" The study of opinions, their formation and change, is one of the defining +topics addressed by social psychology, but in recent years other disciplines, +as computer science and complexity, have addressed this challenge. Despite the +flourishing of different models and theories in both fields, several key +questions still remain unanswered. The aim of this paper is to challenge the +current theories on opinion by putting forward a cognitively grounded model +where opinions are described as specific mental representations whose main +properties are put forward. A comparison with reputation will be also +presented. +",Understanding opinions. A cognitive and formal account +" For large, real-world inductive learning problems, the number of training +examples often must be limited due to the costs associated with procuring, +preparing, and storing the training examples and/or the computational costs +associated with learning from them. In such circumstances, one question of +practical importance is: if only n training examples can be selected, in what +proportion should the classes be represented? In this article we help to answer +this question by analyzing, for a fixed training-set size, the relationship +between the class distribution of the training data and the performance of +classification trees induced from these data. We study twenty-six data sets +and, for each, determine the best class distribution for learning. The +naturally occurring class distribution is shown to generally perform well when +classifier performance is evaluated using undifferentiated error rate (0/1 +loss). However, when the area under the ROC curve is used to evaluate +classifier performance, a balanced distribution is shown to perform well. Since +neither of these choices for class distribution always generates the +best-performing classifier, we introduce a budget-sensitive progressive +sampling algorithm for selecting training examples based on the class +associated with each example. An empirical analysis of this algorithm shows +that the class distribution of the resulting training set yields classifiers +with good (nearly-optimal) classification performance. +","Learning When Training Data are Costly: The Effect of Class Distribution + on Tree Induction" +" In recent years research in the planning community has moved increasingly +toward s application of planners to realistic problems involving both time and +many typ es of resources. For example, interest in planning demonstrated by the +space res earch community has inspired work in observation scheduling, +planetary rover ex ploration and spacecraft control domains. Other temporal and +resource-intensive domains including logistics planning, plant control and +manufacturing have also helped to focus the community on the modelling and +reasoning issues that must be confronted to make planning technology meet the +challenges of application. The International Planning Competitions have acted +as an important motivating fo rce behind the progress that has been made in +planning since 1998. The third com petition (held in 2002) set the planning +community the challenge of handling tim e and numeric resources. This +necessitated the development of a modelling langua ge capable of expressing +temporal and numeric properties of planning domains. In this paper we describe +the language, PDDL2.1, that was used in the competition. We describe the syntax +of the language, its formal semantics and the validation of concurrent plans. +We observe that PDDL2.1 has considerable modelling power --- exceeding the +capabilities of current planning technology --- and presents a number of +important challenges to the research community. +",PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains +" Despite the significant progress in multiagent teamwork, existing research +does not address the optimality of its prescriptions nor the complexity of the +teamwork problem. Without a characterization of the optimality-complexity +tradeoffs, it is impossible to determine whether the assumptions and +approximations made by a particular theory gain enough efficiency to justify +the losses in overall performance. To provide a tool for use by multiagent +researchers in evaluating this tradeoff, we present a unified framework, the +COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model +combines and extends existing multiagent theories, such as decentralized +partially observable Markov decision processes and economic team theory. In +addition to their generality of representation, COM-MTDPs also support the +analysis of both the optimality of team performance and the computational +complexity of the agents' decision problem. In analyzing complexity, we present +a breakdown of the computational complexity of constructing optimal teams under +various classes of problem domains, along the dimensions of observability and +communication cost. In analyzing optimality, we exploit the COM-MTDP's ability +to encode existing teamwork theories and models to encode two instantiations of +joint intentions theory taken from the literature. Furthermore, the COM-MTDP +model provides a basis for the development of novel team coordination +algorithms. We derive a domain-independent criterion for optimal communication +and provide a comparative analysis of the two joint intentions instantiations +with respect to this optimal policy. We have implemented a reusable, +domain-independent software package based on COM-MTDPs to analyze teamwork +coordination strategies, and we demonstrate its use by encoding and evaluating +the two joint intentions strategies within an example domain. +","The Communicative Multiagent Team Decision Problem: Analyzing Teamwork + Theories and Models" +" Adjustable autonomy refers to entities dynamically varying their own +autonomy, transferring decision-making control to other entities (typically +agents transferring control to human users) in key situations. Determining +whether and when such transfers-of-control should occur is arguably the +fundamental research problem in adjustable autonomy. Previous work has +investigated various approaches to addressing this problem but has often +focused on individual agent-human interactions. Unfortunately, domains +requiring collaboration between teams of agents and humans reveal two key +shortcomings of these previous approaches. First, these approaches use rigid +one-shot transfers of control that can result in unacceptable coordination +failures in multiagent settings. Second, they ignore costs (e.g., in terms of +time delays or effects on actions) to an agent's team due to such +transfers-of-control. To remedy these problems, this article presents a novel +approach to adjustable autonomy, based on the notion of a transfer-of-control +strategy. A transfer-of-control strategy consists of a conditional sequence of +two types of actions: (i) actions to transfer decision-making control (e.g., +from an agent to a user or vice versa) and (ii) actions to change an agent's +pre-specified coordination constraints with team members, aimed at minimizing +miscoordination costs. The goal is for high-quality individual decisions to be +made with minimal disruption to the coordination of the team. We present a +mathematical model of transfer-of-control strategies. The model guides and +informs the operationalization of the strategies using Markov Decision +Processes, which select an optimal strategy, given an uncertain environment and +costs to the individuals and teams. The approach has been carefully evaluated, +including via its use in a real-world, deployed multi-agent system that assists +a research group in its daily activities. +",Towards Adjustable Autonomy for the Real World +" In this paper, we analyze the decision version of the NK landscape model from +the perspective of threshold phenomena and phase transitions under two random +distributions, the uniform probability model and the fixed ratio model. For the +uniform probability model, we prove that the phase transition is easy in the +sense that there is a polynomial algorithm that can solve a random instance of +the problem with the probability asymptotic to 1 as the problem size tends to +infinity. For the fixed ratio model, we establish several upper bounds for the +solubility threshold, and prove that random instances with parameters above +these upper bounds can be solved polynomially. This, together with our +empirical study for random instances generated below and in the phase +transition region, suggests that the phase transition of the fixed ratio model +is also easy. +",An Analysis of Phase Transition in NK Landscapes +" This paper presents an approach to expert-guided subgroup discovery. The main +step of the subgroup discovery process, the induction of subgroup descriptions, +is performed by a heuristic beam search algorithm, using a novel parametrized +definition of rule quality which is analyzed in detail. The other important +steps of the proposed subgroup discovery process are the detection of +statistically significant properties of selected subgroups and subgroup +visualization: statistically significant properties are used to enrich the +descriptions of induced subgroups, while the visualization shows subgroup +properties in the form of distributions of the numbers of examples in the +subgroups. The approach is illustrated by the results obtained for a medical +problem of early detection of patient risk groups. +",Expert-Guided Subgroup Discovery: Methodology and Application +" Independence -- the study of what is relevant to a given problem of reasoning +-- has received an increasing attention from the AI community. In this paper, +we consider two basic forms of independence, namely, a syntactic one and a +semantic one. We show features and drawbacks of them. In particular, while the +syntactic form of independence is computationally easy to check, there are +cases in which things that intuitively are not relevant are not recognized as +such. We also consider the problem of forgetting, i.e., distilling from a +knowledge base only the part that is relevant to the set of queries constructed +from a subset of the alphabet. While such process is computationally hard, it +allows for a simplification of subsequent reasoning, and can thus be viewed as +a form of compilation: once the relevant part of a knowledge base has been +extracted, all reasoning tasks to be performed can be simplified. +","Propositional Independence - Formula-Variable Independence and + Forgetting" +" We present a probabilistic generative model for timing deviations in +expressive music performance. The structure of the proposed model is equivalent +to a switching state space model. The switch variables correspond to discrete +note locations as in a musical score. The continuous hidden variables denote +the tempo. We formulate two well known music recognition problems, namely tempo +tracking and automatic transcription (rhythm quantization) as filtering and +maximum a posteriori (MAP) state estimation tasks. Exact computation of +posterior features such as the MAP state is intractable in this model class, so +we introduce Monte Carlo methods for integration and optimization. We compare +Markov Chain Monte Carlo (MCMC) methods (such as Gibbs sampling, simulated +annealing and iterative improvement) and sequential Monte Carlo methods +(particle filters). Our simulation results suggest better results with +sequential methods. The methods can be applied in both online and batch +scenarios such as tempo tracking and transcription and are thus potentially +useful in a number of music applications such as adaptive automatic +accompaniment, score typesetting and music information retrieval. +",Monte Carlo Methods for Tempo Tracking and Rhythm Quantization +" Bayesian belief networks have grown to prominence because they provide +compact representations for many problems for which probabilistic inference is +appropriate, and there are algorithms to exploit this compactness. The next +step is to allow compact representations of the conditional probabilities of a +variable given its parents. In this paper we present such a representation that +exploits contextual independence in terms of parent contexts; which variables +act as parents may depend on the value of other variables. The internal +representation is in terms of contextual factors (confactors) that is simply a +pair of a context and a table. The algorithm, contextual variable elimination, +is based on the standard variable elimination algorithm that eliminates the +non-query variables in turn, but when eliminating a variable, the tables that +need to be multiplied can depend on the context. This algorithm reduces to +standard variable elimination when there is no contextual independence +structure to exploit. We show how this can be much more efficient than variable +elimination when there is structure to exploit. We explain why this new method +can exploit more structure than previous methods for structured belief network +inference and an analogous algorithm that uses trees. +",Exploiting Contextual Independence In Probabilistic Inference +" In this article we present an algorithm to compute bounds on the marginals of +a graphical model. For several small clusters of nodes upper and lower bounds +on the marginal values are computed independently of the rest of the network. +The range of allowed probability distributions over the surrounding nodes is +restricted using earlier computed bounds. As we will show, this can be +considered as a set of constraints in a linear programming problem of which the +objective function is the marginal probability of the center nodes. In this way +knowledge about the maginals of neighbouring clusters is passed to other +clusters thereby tightening the bounds on their marginals. We show that sharp +bounds can be obtained for undirected and directed graphs that are used for +practical applications, but for which exact computations are infeasible. +",Bound Propagation +" Policies of Markov Decision Processes (MDPs) determine the next action to +execute from the current state and, possibly, the history (the past states). +When the number of states is large, succinct representations are often used to +compactly represent both the MDPs and the policies in a reduced amount of +space. In this paper, some problems related to the size of succinctly +represented policies are analyzed. Namely, it is shown that some MDPs have +policies that can only be represented in space super-polynomial in the size of +the MDP, unless the polynomial hierarchy collapses. This fact motivates the +study of the problem of deciding whether a given MDP has a policy of a given +size and reward. Since some algorithms for MDPs work by finding a succinct +representation of the value function, the problem of deciding the existence of +a succinct representation of a value function of a given size and reward is +also considered. +",On Polynomial Sized MDP Succinct Policies +" We describe a system for specifying the effects of actions. Unlike those +commonly used in AI planning, our system uses an action description language +that allows one to specify the effects of actions using domain rules, which are +state constraints that can entail new action effects from old ones. +Declaratively, an action domain in our language corresponds to a nonmonotonic +causal theory in the situation calculus. Procedurally, such an action domain is +compiled into a set of logical theories, one for each action in the domain, +from which fully instantiated successor state-like axioms and STRIPS-like +systems are then generated. We expect the system to be a useful tool for +knowledge engineers writing action specifications for classical AI planning +systems, GOLOG systems, and other systems where formal specifications of +actions are needed. +","Compiling Causal Theories to Successor State Axioms and STRIPS-Like + Systems" +" VHPOP is a partial order causal link (POCL) planner loosely based on UCPOP. +It draws from the experience gained in the early to mid 1990's on flaw +selection strategies for POCL planning, and combines this with more recent +developments in the field of domain independent planning such as distance based +heuristics and reachability analysis. We present an adaptation of the additive +heuristic for plan space planning, and modify it to account for possible reuse +of existing actions in a plan. We also propose a large set of novel flaw +selection strategies, and show how these can help us solve more problems than +previously possible by POCL planners. VHPOP also supports planning with +durative actions by incorporating standard techniques for temporal constraint +reasoning. We demonstrate that the same heuristic techniques used to boost the +performance of classical POCL planning can be effective in domains with +durative actions as well. The result is a versatile heuristic POCL planner +competitive with established CSP-based and heuristic state space planners. +",VHPOP: Versatile Heuristic Partial Order Planner +" The SHOP2 planning system received one of the awards for distinguished +performance in the 2002 International Planning Competition. This paper +describes the features of SHOP2 which enabled it to excel in the competition, +especially those aspects of SHOP2 that deal with temporal and metric planning +domains. +",SHOP2: An HTN Planning System +" Hierarchical task decomposition is a method used in many agent systems to +organize agent knowledge. This work shows how the combination of a hierarchy +and persistent assertions of knowledge can lead to difficulty in maintaining +logical consistency in asserted knowledge. We explore the problematic +consequences of persistent assumptions in the reasoning process and introduce +novel potential solutions. Having implemented one of the possible solutions, +Dynamic Hierarchical Justification, its effectiveness is demonstrated with an +empirical analysis. +","An Architectural Approach to Ensuring Consistency in Hierarchical + Execution" +" The proliferation of online information sources has led to an increased use +of wrappers for extracting data from Web sources. While most of the previous +research has focused on quick and efficient generation of wrappers, the +development of tools for wrapper maintenance has received less attention. This +is an important research problem because Web sources often change in ways that +prevent the wrappers from extracting data correctly. We present an efficient +algorithm that learns structural information about data from positive examples +alone. We describe how this information can be used for two wrapper maintenance +applications: wrapper verification and reinduction. The wrapper verification +system detects when a wrapper is not extracting correct data, usually because +the Web source has changed its format. The reinduction algorithm automatically +recovers from changes in the Web source by identifying data on Web pages so +that a new wrapper may be generated for this source. To validate our approach, +we monitored 27 wrappers over a period of a year. The verification algorithm +correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, +resulting in precision of 0.73 and recall of 0.95. We validated the reinduction +algorithm on ten Web sources. We were able to successfully reinduce the +wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data +extraction task. +",Wrapper Maintenance: A Machine Learning Approach +" The cognitive research on reputation has shown several interesting properties +that can improve both the quality of services and the security in distributed +electronic environments. In this paper, the impact of reputation on +decision-making under scarcity of information will be shown. First, a cognitive +theory of reputation will be presented, then a selection of simulation +experimental results from different studies will be discussed. Such results +concern the benefits of reputation when agents need to find out good sellers in +a virtual market-place under uncertainty and informational cheating. +",Exploiting Reputation in Distributed Virtual Environments +" In this paper we examine the application of the random forest classifier for +the all relevant feature selection problem. To this end we first examine two +recently proposed all relevant feature selection algorithms, both being a +random forest wrappers, on a series of synthetic data sets with varying size. +We show that reasonable accuracy of predictions can be achieved and that +heuristic algorithms that were designed to handle the all relevant problem, +have performance that is close to that of the reference ideal algorithm. Then, +we apply one of the algorithms to four families of semi-synthetic data sets to +assess how the properties of particular data set influence results of feature +selection. Finally we test the procedure using a well-known gene expression +data set. The relevance of nearly all previously established important genes +was confirmed, moreover the relevance of several new ones is discovered. +",The All Relevant Feature Selection using Random Forest +" Unary operator domains -- i.e., domains in which operators have a single +effect -- arise naturally in many control problems. In its most general form, +the problem of STRIPS planning in unary operator domains is known to be as hard +as the general STRIPS planning problem -- both are PSPACE-complete. However, +unary operator domains induce a natural structure, called the domain's causal +graph. This graph relates between the preconditions and effect of each domain +operator. Causal graphs were exploited by Williams and Nayak in order to +analyze plan generation for one of the controllers in NASA's Deep-Space One +spacecraft. There, they utilized the fact that when this graph is acyclic, a +serialization ordering over any subgoal can be obtained quickly. In this paper +we conduct a comprehensive study of the relationship between the structure of a +domain's causal graph and the complexity of planning in this domain. On the +positive side, we show that a non-trivial polynomial time plan generation +algorithm exists for domains whose causal graph induces a polytree with a +constant bound on its node indegree. On the negative side, we show that even +plan existence is hard when the graph is a directed-path singly connected DAG. +More generally, we show that the number of paths in the causal graph is closely +related to the complexity of planning in the associated domain. Finally we +relate our results to the question of complexity of planning with serializable +subgoals. +",Structure and Complexity in Planning with Unary Operators +" Recently, planning based on answer set programming has been proposed as an +approach towards realizing declarative planning systems. In this paper, we +present the language Kc, which extends the declarative planning language K by +action costs. Kc provides the notion of admissible and optimal plans, which are +plans whose overall action costs are within a given limit resp. minimum over +all plans (i.e., cheapest plans). As we demonstrate, this novel language allows +for expressing some nontrivial planning tasks in a declarative way. +Furthermore, it can be utilized for representing planning problems under other +optimality criteria, such as computing ``shortest'' plans (with the least +number of steps), and refinement combinations of cheapest and fastest plans. We +study complexity aspects of the language Kc and provide a transformation to +logic programs, such that planning problems are solved via answer set +programming. Furthermore, we report experimental results on selected problems. +Our experience is encouraging that answer set planning may be a valuable +approach to expressive planning systems in which intricate planning problems +can be naturally specified and solved. +",Answer Set Planning Under Action Costs +" In common-interest stochastic games all players receive an identical payoff. +Players participating in such games must learn to coordinate with each other in +order to receive the highest-possible value. A number of reinforcement learning +algorithms have been proposed for this problem, and some have been shown to +converge to good solutions in the limit. In this paper we show that using very +simple model-based algorithms, much better (i.e., polynomial) convergence rates +can be attained. Moreover, our model-based algorithms are guaranteed to +converge to the optimal value, unlike many of the existing algorithms. +",Learning to Coordinate Efficiently: A Model-based Approach +" SAPA is a domain-independent heuristic forward chaining planner that can +handle durative actions, metric resource constraints, and deadline goals. It is +designed to be capable of handling the multi-objective nature of metric +temporal planning. Our technical contributions include (i) planning-graph based +methods for deriving heuristics that are sensitive to both cost and makespan +(ii) techniques for adjusting the heuristic estimates to take action +interactions and metric resource limitations into account and (iii) a linear +time greedy post-processing technique to improve execution flexibility of the +solution plans. An implementation of SAPA using many of the techniques +presented in this paper was one of the best domain independent planners for +domains with metric and temporal constraints in the third International +Planning Competition, held at AIPS-02. We describe the technical details of +extracting the heuristics and present an empirical evaluation of the current +implementation of SAPA. +",SAPA: A Multi-objective Metric Temporal Planner +" The recent emergence of heavily-optimized modal decision procedures has +highlighted the key role of empirical testing in this domain. Unfortunately, +the introduction of extensive empirical tests for modal logics is recent, and +so far none of the proposed test generators is very satisfactory. To cope with +this fact, we present a new random generation method that provides benefits +over previous methods for generating empirical tests. It fixes and much +generalizes one of the best-known methods, the random CNF_[]m test, allowing +for generating a much wider variety of problems, covering in principle the +whole input space. Our new method produces much more suitable test sets for the +current generation of modal decision procedures. We analyze the features of the +new method by means of an extensive collection of empirical tests. +","A New General Method to Generate Random Modal Formulae for Testing + Decision Procedures" +" Despite their near dominance, heuristic state search planners still lag +behind disjunctive planners in the generation of parallel plans in classical +planning. The reason is that directly searching for parallel solutions in state +space planners would require the planners to branch on all possible subsets of +parallel actions, thus increasing the branching factor exponentially. We +present a variant of our heuristic state search planner AltAlt, called AltAltp +which generates parallel plans by using greedy online parallelization of +partial plans. The greedy approach is significantly informed by the use of +novel distance heuristics that AltAltp derives from a graphplan-style planning +graph for the problem. While this approach is not guaranteed to provide optimal +parallel plans, empirical results show that AltAltp is capable of generating +good quality parallel plans at a fraction of the cost incurred by the +disjunctive planners. +",AltAltp: Online Parallelization of Plans with Heuristic State Search +" We address the problem of propositional logic-based abduction, i.e., the +problem of searching for a best explanation for a given propositional +observation according to a given propositional knowledge base. We give a +general algorithm, based on the notion of projection; then we study +restrictions over the representations of the knowledge base and of the query, +and find new polynomial classes of abduction problems. +",New Polynomial Classes for Logic-Based Abduction +" We present some techniques for planning in domains specified with the recent +standard language PDDL2.1, supporting 'durative actions' and numerical +quantities. These techniques are implemented in LPG, a domain-independent +planner that took part in the 3rd International Planning Competition (IPC). LPG +is an incremental, any time system producing multi-criteria quality plans. The +core of the system is based on a stochastic local search method and on a +graph-based representation called 'Temporal Action Graphs' (TA-graphs). This +paper focuses on temporal planning, introducing TA-graphs and proposing some +techniques to guide the search in LPG using this representation. The +experimental results of the 3rd IPC, as well as further results presented in +this paper, show that our techniques can be very effective. Often LPG +outperforms all other fully-automated planners of the 3rd IPC in terms of speed +to derive a solution, or quality of the solutions that can be produced. +","Planning Through Stochastic Local Search and Temporal Action Graphs in + LPG" +" TALplanner is a forward-chaining planner that relies on domain knowledge in +the shape of temporal logic formulas in order to prune irrelevant parts of the +search space. TALplanner recently participated in the third International +Planning Competition, which had a clear emphasis on increasing the complexity +of the problem domains being used as benchmark tests and the expressivity +required to represent these domains in a planning system. Like many other +planners, TALplanner had support for some but not all aspects of this increase +in expressivity, and a number of changes to the planner were required. After a +short introduction to TALplanner, this article describes some of the changes +that were made before and during the competition. We also describe the process +of introducing suitable domain knowledge for several of the competition +domains. +",TALplanner in IPC-2002: Extensions and Control Rules +" The automatic generation of decision trees based on off-line reasoning on +models of a domain is a reasonable compromise between the advantages of using a +model-based approach in technical domains and the constraints imposed by +embedded applications. In this paper we extend the approach to deal with +temporal information. We introduce a notion of temporal decision tree, which is +designed to make use of relevant information as long as it is acquired, and we +present an algorithm for compiling such trees from a model-based reasoning +system. +","Temporal Decision Trees: Model-based Diagnosis of Dynamic Systems + On-Board" +" The performance of anytime algorithms can be improved by simultaneously +solving several instances of algorithm-problem pairs. These pairs may include +different instances of a problem (such as starting from a different initial +state), different algorithms (if several alternatives exist), or several runs +of the same algorithm (for non-deterministic algorithms). In this paper we +present a methodology for designing an optimal scheduling policy based on the +statistical characteristics of the algorithms involved. We formally analyze the +case where the processes share resources (a single-processor model), and +provide an algorithm for optimal scheduling. We analyze, theoretically and +empirically, the behavior of our scheduling algorithm for various distribution +types. Finally, we present empirical results of applying our scheduling +algorithm to the Latin Square problem. +","Optimal Schedules for Parallelizing Anytime Algorithms: The Case of + Shared Resources" +" Auctions are becoming an increasingly popular method for transacting +business, especially over the Internet. This article presents a general +approach to building autonomous bidding agents to bid in multiple simultaneous +auctions for interacting goods. A core component of our approach learns a model +of the empirical price dynamics based on past data and uses the model to +analytically calculate, to the greatest extent possible, optimal bids. We +introduce a new and general boosting-based algorithm for conditional density +estimation problems of this kind, i.e., supervised learning problems in which +the goal is to estimate the entire conditional distribution of the real-valued +label. This approach is fully implemented as ATTac-2001, a top-scoring agent in +the second Trading Agent Competition (TAC-01). We present experiments +demonstrating the effectiveness of our boosting-based price predictor relative +to several reasonable alternatives. +","Decision-Theoretic Bidding Based on Learned Density Models in + Simultaneous, Interacting Auctions" +" Planning with numeric state variables has been a challenge for many years, +and was a part of the 3rd International Planning Competition (IPC-3). Currently +one of the most popular and successful algorithmic techniques in STRIPS +planning is to guide search by a heuristic function, where the heuristic is +based on relaxing the planning task by ignoring the delete lists of the +available actions. We present a natural extension of ``ignoring delete lists'' +to numeric state variables, preserving the relevant theoretical properties of +the STRIPS relaxation under the condition that the numeric task at hand is +``monotonic''. We then identify a subset of the numeric IPC-3 competition +language, ``linear tasks'', where monotonicity can be achieved by +pre-processing. Based on that, we extend the algorithms used in the heuristic +planning system FF to linear tasks. The resulting system Metric-FF is, +according to the IPC-3 results which we discuss, one of the two currently most +efficient numeric planners. +","The Metric-FF Planning System: Translating ""Ignoring Delete Lists"" to + Numeric State Variables" +" Nanson's and Baldwin's voting rules select a winner by successively +eliminating candidates with low Borda scores. We show that these rules have a +number of desirable computational properties. In particular, with unweighted +votes, it is NP-hard to manipulate either rule with one manipulator, whilst +with weighted votes, it is NP-hard to manipulate either rule with a small +number of candidates and a coalition of manipulators. As only a couple of other +voting rules are known to be NP-hard to manipulate with a single manipulator, +Nanson's and Baldwin's rules appear to be particularly resistant to +manipulation from a theoretical perspective. We also propose a number of +approximation methods for manipulating these two rules. Experiments demonstrate +that both rules are often difficult to manipulate in practice. These results +suggest that elimination style voting rules deserve further study. +",Manipulation of Nanson's and Baldwin's Rules +" Search is a major technique for planning. It amounts to exploring a state +space of planning domains typically modeled as a directed graph. However, +prohibitively large sizes of the search space make search expensive. Developing +better heuristic functions has been the main technique for improving search +efficiency. Nevertheless, recent studies have shown that improving heuristics +alone has certain fundamental limits on improving search efficiency. Recently, +a new direction of research called partial order based reduction (POR) has been +proposed as an alternative to improving heuristics. POR has shown promise in +speeding up searches. + POR has been extensively studied in model checking research and is a key +enabling technique for scalability of model checking systems. Although the POR +theory has been extensively studied in model checking, it has never been +developed systematically for planning before. In addition, the conditions for +POR in the model checking theory are abstract and not directly applicable in +planning. Previous works on POR algorithms for planning did not establish the +connection between these algorithms and existing theory in model checking. + In this paper, we develop a theory for POR in planning. The new theory we +develop connects the stubborn set theory in model checking and POR methods in +planning. We show that previous POR algorithms in planning can be explained by +the new theory. Based on the new theory, we propose a new, stronger POR +algorithm. Experimental results on various planning domains show further search +cost reduction using the new algorithm. +",Theory and Algorithms for Partial Order Based Reduction in Planning +" Set and multiset variables in constraint programming have typically been +represented using subset bounds. However, this is a weak representation that +neglects potentially useful information about a set such as its cardinality. +For set variables, the length-lex (LL) representation successfully provides +information about the length (cardinality) and position in the lexicographic +ordering. For multiset variables, where elements can be repeated, we consider +richer representations that take into account additional information. We study +eight different representations in which we maintain bounds according to one of +the eight different orderings: length-(co)lex (LL/LC), variety-(co)lex (VL/VC), +length-variety-(co)lex (LVL/LVC), and variety-length-(co)lex (VLL/VLC) +orderings. These representations integrate together information about the +cardinality, variety (number of distinct elements in the multiset), and +position in some total ordering. Theoretical and empirical comparisons of +expressiveness and compactness of the eight representations suggest that +length-variety-(co)lex (LVL/LVC) and variety-length-(co)lex (VLL/VLC) usually +give tighter bounds after constraint propagation. We implement the eight +representations and evaluate them against the subset bounds representation with +cardinality and variety reasoning. Results demonstrate that they offer +significantly better pruning and runtime. +","A Comparison of Lex Bounds for Multiset Variables in Constraint + Programming" +" This paper reports the outcome of the third in the series of biennial +international planning competitions, held in association with the International +Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to +describing the domains, the planners and the objectives of the competition, the +paper includes analysis of the results. The results are analysed from several +perspectives, in order to address the questions of comparative performance +between planners, comparative difficulty of domains, the degree of agreement +between planners about the relative difficulty of individual problem instances +and the question of how well planners scale relative to one another over +increasingly difficult problems. The paper addresses these questions through +statistical analysis of the raw results of the competition, in order to +determine which results can be considered to be adequately supported by the +data. The paper concludes with a discussion of some challenges for the future +of the competition series. +",The 3rd International Planning Competition: Results and Analysis +" As computational agents are developed for increasingly complicated e-commerce +applications, the complexity of the decisions they face demands advances in +artificial intelligence techniques. For example, an agent representing a seller +in an auction should try to maximize the seller's profit by reasoning about a +variety of possibly uncertain pieces of information, such as the maximum prices +various buyers might be willing to pay, the possible prices being offered by +competing sellers, the rules by which the auction operates, the dynamic arrival +and matching of offers to buy and sell, and so on. A naive application of +multiagent reasoning techniques would require the seller's agent to explicitly +model all of the other agents through an extended time horizon, rendering the +problem intractable for many realistically-sized problems. We have instead +devised a new strategy that an agent can use to determine its bid price based +on a more tractable Markov chain model of the auction process. We have +experimentally identified the conditions under which our new strategy works +well, as well as how well it works in comparison to the optimal performance the +agent could have achieved had it known the future. Our results show that our +new strategy in general performs well, outperforming other tractable heuristic +strategies in a majority of experiments, and is particularly effective in a +'seller?s market', where many buy offers are available. +","Use of Markov Chains to Design an Agent Bidding Strategy for Continuous + Double Auctions" +" This paper presents a new classifier combination technique based on the +Dempster-Shafer theory of evidence. The Dempster-Shafer theory of evidence is a +powerful method for combining measures of evidence from different classifiers. +However, since each of the available methods that estimates the evidence of +classifiers has its own limitations, we propose here a new implementation which +adapts to training data so that the overall mean square error is minimized. The +proposed technique is shown to outperform most available classifier combination +methods when tested on three different classification problems. +","A New Technique for Combining Multiple Classifiers using The + Dempster-Shafer Theory of Evidence" +" Although many algorithms have been designed to construct Bayesian network +structures using different approaches and principles, they all employ only two +methods: those based on independence criteria, and those based on a scoring +function and a search procedure (although some methods combine the two). Within +the score+search paradigm, the dominant approach uses local search methods in +the space of directed acyclic graphs (DAGs), where the usual choices for +defining the elementary modifications (local changes) that can be applied are +arc addition, arc deletion, and arc reversal. In this paper, we propose a new +local search method that uses a different search space, and which takes account +of the concept of equivalence between network structures: restricted acyclic +partially directed graphs (RPDAGs). In this way, the number of different +configurations of the search space is reduced, thus improving efficiency. +Moreover, although the final result must necessarily be a local optimum given +the nature of the search method, the topology of the new search space, which +avoids making early decisions about the directions of the arcs, may help to +find better local optima than those obtained by searching in the DAG space. +Detailed results of the evaluation of the proposed search method on several +test problems, including the well-known Alarm Monitoring System, are also +presented. +","Searching for Bayesian Network Structures in the Space of Restricted + Acyclic Partially Directed Graphs" +" The size and complexity of software and hardware systems have significantly +increased in the past years. As a result, it is harder to guarantee their +correct behavior. One of the most successful methods for automated verification +of finite-state systems is model checking. Most of the current model-checking +systems use binary decision diagrams (BDDs) for the representation of the +tested model and in the verification process of its properties. Generally, BDDs +allow a canonical compact representation of a boolean function (given an order +of its variables). The more compact the BDD is, the better performance one gets +from the verifier. However, finding an optimal order for a BDD is an +NP-complete problem. Therefore, several heuristic methods based on expert +knowledge have been developed for variable ordering. We propose an alternative +approach in which the variable ordering algorithm gains 'ordering experience' +from training models and uses the learned knowledge for finding good orders. +Our methodology is based on offline learning of pair precedence classifiers +from training models, that is, learning which variable pair permutation is more +likely to lead to a good order. For each training model, a number of training +sequences are evaluated. Every training model variable pair permutation is then +tagged based on its performance on the evaluated orders. The tagged +permutations are then passed through a feature extractor and are given as +examples to a classifier creation algorithm. Given a model for which an order +is requested, the ordering algorithm consults each precedence classifier and +constructs a pair precedence table which is used to create the order. Our +algorithm was integrated with SMV, which is one of the most widely used +verification systems. Preliminary empirical evaluation of our methodology, +using real benchmark models, shows performance that is better than random +ordering and is competitive with existing algorithms that use expert knowledge. +We believe that in sub-domains of models (alu, caches, etc.) our system will +prove even more valuable. This is because it features the ability to learn +sub-domain knowledge, something that no other ordering algorithm does. +",Learning to Order BDD Variables in Verification +" Supply chain formation is the process of determining the structure and terms +of exchange relationships to enable a multilevel, multiagent production +activity. We present a simple model of supply chains, highlighting two +characteristic features: hierarchical subtask decomposition, and resource +contention. To decentralize the formation process, we introduce a market price +system over the resources produced along the chain. In a competitive +equilibrium for this system, agents choose locally optimal allocations with +respect to prices, and outcomes are optimal overall. To determine prices, we +define a market protocol based on distributed, progressive auctions, and +myopic, non-strategic agent bidding policies. In the presence of resource +contention, this protocol produces better solutions than the greedy protocols +common in the artificial intelligence and multiagent systems literature. The +protocol often converges to high-value supply chains, and when competitive +equilibria exist, typically to approximate competitive equilibria. However, +complementarities in agent production technologies can cause the protocol to +wastefully allocate inputs to agents that do not produce their outputs. A +subsequent decommitment phase recovers a significant fraction of the lost +surplus. +","Decentralized Supply Chain Formation: A Market Protocol and Competitive + Equilibrium Analysis" +" Information about user preferences plays a key role in automated decision +making. In many domains it is desirable to assess such preferences in a +qualitative rather than quantitative way. In this paper, we propose a +qualitative graphical representation of preferences that reflects conditional +dependence and independence of preference statements under a ceteris paribus +(all else being equal) interpretation. Such a representation is often compact +and arguably quite natural in many circumstances. We provide a formal semantics +for this model, and describe how the structure of the network can be exploited +in several inference tasks, such as determining whether one outcome dominates +(is preferred to) another, ordering a set outcomes according to the preference +relation, and constructing the best outcome subject to available evidence. +","CP-nets: A Tool for Representing and Reasoning withConditional Ceteris + Paribus Preference Statements" +" MAP is the problem of finding a most probable instantiation of a set of +variables given evidence. MAP has always been perceived to be significantly +harder than the related problems of computing the probability of a variable +instantiation Pr, or the problem of computing the most probable explanation +(MPE). This paper investigates the complexity of MAP in Bayesian networks. +Specifically, we show that MAP is complete for NP^PP and provide further +negative complexity results for algorithms based on variable elimination. We +also show that MAP remains hard even when MPE and Pr become easy. For example, +we show that MAP is NP-complete when the networks are restricted to polytrees, +and even then can not be effectively approximated. Given the difficulty of +computing MAP exactly, and the difficulty of approximating MAP while providing +useful guarantees on the resulting approximation, we investigate best effort +approximations. We introduce a generic MAP approximation framework. We provide +two instantiations of the framework; one for networks which are amenable to +exact inference Pr, and one for networks for which even exact inference is too +hard. This allows MAP approximation on networks that are too complex to even +exactly solve the easier problems, Pr and MPE. Experimental results indicate +that using these approximation algorithms provides much better solutions than +standard techniques, and provide accurate MAP estimates in many cases. +",Complexity Results and Approximation Strategies for MAP Explanations +" The Model Checking Integrated Planning System (MIPS) is a temporal least +commitment heuristic search planner based on a flexible object-oriented +workbench architecture. Its design clearly separates explicit and symbolic +directed exploration algorithms from the set of on-line and off-line computed +estimates and associated data structures. MIPS has shown distinguished +performance in the last two international planning competitions. In the last +event the description language was extended from pure propositional planning to +include numerical state variables, action durations, and plan quality objective +functions. Plans were no longer sequences of actions but time-stamped +schedules. As a participant of the fully automated track of the competition, +MIPS has proven to be a general system; in each track and every benchmark +domain it efficiently computed plans of remarkable quality. This article +introduces and analyzes the most important algorithmic novelties that were +necessary to tackle the new layers of expressiveness in the benchmark problems +and to achieve a high level of performance. The extensions include critical +path analysis of sequentially generated plans to generate corresponding optimal +parallel plans. The linear time algorithm to compute the parallel plan bypasses +known NP hardness results for partial ordering by scheduling plans with respect +to the set of actions and the imposed precedence relations. The efficiency of +this algorithm also allows us to improve the exploration guidance: for each +encountered planning state the corresponding approximate sequential plan is +scheduled. One major strength of MIPS is its static analysis phase that grounds +and simplifies parameterized predicates, functions and operators, that infers +knowledge to minimize the state description length, and that detects domain +object symmetries. The latter aspect is analyzed in detail. MIPS has been +developed to serve as a complete and optimal state space planner, with +admissible estimates, exploration engines and branching cuts. In the +competition version, however, certain performance compromises had to be made, +including floating point arithmetic, weighted heuristic search exploration +according to an inadmissible estimate and parameterized optimization. +","Taming Numbers and Durations in the Model Checking Integrated Planning + System" +" We propose a formalism for representation of finite languages, referred to as +the class of IDL-expressions, which combines concepts that were only considered +in isolation in existing formalisms. The suggested applications are in natural +language processing, more specifically in surface natural language generation +and in machine translation, where a sentence is obtained by first generating a +large set of candidate sentences, represented in a compact way, and then by +filtering such a set through a parser. We study several formal properties of +IDL-expressions and compare this new formalism with more standard ones. We also +present a novel parsing algorithm for IDL-expressions and prove a non-trivial +upper bound on its time complexity. +","IDL-Expressions: A Formalism for Representing and Parsing Finite + Languages in Natural Language Processing" +" Hierarchical latent class (HLC) models are tree-structured Bayesian networks +where leaf nodes are observed while internal nodes are latent. There are no +theoretically well justified model selection criteria for HLC models in +particular and Bayesian networks with latent nodes in general. Nonetheless, +empirical studies suggest that the BIC score is a reasonable criterion to use +in practice for learning HLC models. Empirical studies also suggest that +sometimes model selection can be improved if standard model dimension is +replaced with effective model dimension in the penalty term of the BIC score. +Effective dimensions are difficult to compute. In this paper, we prove a +theorem that relates the effective dimension of an HLC model to the effective +dimensions of a number of latent class models. The theorem makes it +computationally feasible to compute the effective dimensions of large HLC +models. The theorem can also be used to compute the effective dimensions of +general tree models. +",Effective Dimensions of Hierarchical Latent Class Models +" We introduce an abductive method for a coherent integration of independent +data-sources. The idea is to compute a list of data-facts that should be +inserted to the amalgamated database or retracted from it in order to restore +its consistency. This method is implemented by an abductive solver, called +Asystem, that applies SLDNFA-resolution on a meta-theory that relates +different, possibly contradicting, input databases. We also give a pure +model-theoretic analysis of the possible ways to `recover' consistent data from +an inconsistent database in terms of those models of the database that exhibit +as minimal inconsistent information as reasonably possible. This allows us to +characterize the `recovered databases' in terms of the `preferred' (i.e., most +consistent) models of the theory. The outcome is an abductive-based application +that is sound and complete with respect to a corresponding model-based, +preferential semantics, and -- to the best of our knowledge -- is more +expressive (thus more general) than any other implementation of coherent +integration of databases. +",Coherent Integration of Databases by Abductive Logic Programming +" We present a visually-grounded language understanding model based on a study +of how people verbally describe objects in scenes. The emphasis of the model is +on the combination of individual word meanings to produce meanings for complex +referring expressions. The model has been implemented, and it is able to +understand a broad range of spatial referring expressions. We describe our +implementation of word level visually-grounded semantics and their embedding in +a compositional parsing framework. The implemented system selects the correct +referents in response to natural language expressions for a large percentage of +test cases. In an analysis of the system's successes and failures we reveal how +visual context influences the semantics of utterances and propose future +extensions to the model that take such context into account. +",Grounded Semantic Composition for Visual Scenes +" The 2002 Trading Agent Competition (TAC) presented a challenging market game +in the domain of travel shopping. One of the pivotal issues in this domain is +uncertainty about hotel prices, which have a significant influence on the +relative cost of alternative trip schedules. Thus, virtually all participants +employ some method for predicting hotel prices. We survey approaches employed +in the tournament, finding that agents apply an interesting diversity of +techniques, taking into account differing sources of evidence bearing on +prices. Based on data provided by entrants on their agents' actual predictions +in the TAC-02 finals and semifinals, we analyze the relative efficacy of these +approaches. The results show that taking into account game-specific information +about flight prices is a major distinguishing factor. Machine learning methods +effectively induce the relationship between flight and hotel prices from game +data, and a purely analytical approach based on competitive equilibrium +analysis achieves equal accuracy with no historical data. Employing a new +measure of prediction quality, we relate absolute accuracy to bottom-line +performance in the game. +",Price Prediction in a Trading Agent Competition +" The predominant knowledge-based approach to automated model construction, +compositional modelling, employs a set of models of particular functional +components. Its inference mechanism takes a scenario describing the constituent +interacting components of a system and translates it into a useful mathematical +model. This paper presents a novel compositional modelling approach aimed at +building model repositories. It furthers the field in two respects. Firstly, it +expands the application domain of compositional modelling to systems that can +not be easily described in terms of interacting functional components, such as +ecological systems. Secondly, it enables the incorporation of user preferences +into the model selection process. These features are achieved by casting the +compositional modelling problem as an activity-based dynamic preference +constraint satisfaction problem, where the dynamic constraints describe the +restrictions imposed over the composition of partial models and the preferences +correspond to those of the user of the automated modeller. In addition, the +preference levels are represented through the use of symbolic values that +differ in orders of magnitude. +","Compositional Model Repositories via Dynamic Constraint Satisfaction + with Order-of-Magnitude Preferences" +" Two major goals in machine learning are the discovery and improvement of +solutions to complex problems. In this paper, we argue that complexification, +i.e. the incremental elaboration of solutions through adding new structure, +achieves both these goals. We demonstrate the power of complexification through +the NeuroEvolution of Augmenting Topologies (NEAT) method, which evolves +increasingly complex neural network architectures. NEAT is applied to an +open-ended coevolutionary robot duel domain where robot controllers compete +head to head. Because the robot duel domain supports a wide range of +strategies, and because coevolution benefits from an escalating arms race, it +serves as a suitable testbed for studying complexification. When compared to +the evolution of networks with fixed structure, complexifying evolution +discovers significantly more sophisticated strategies. The results suggest that +in order to discover and improve complex solutions, evolution, and search in +general, should be allowed to complexify as well as optimize. +",Competitive Coevolution through Evolutionary Complexification +" When writing a constraint program, we have to choose which variables should +be the decision variables, and how to represent the constraints on these +variables. In many cases, there is considerable choice for the decision +variables. Consider, for example, permutation problems in which we have as many +values as variables, and each variable takes an unique value. In such problems, +we can choose between a primal and a dual viewpoint. In the dual viewpoint, +each dual variable represents one of the primal values, whilst each dual value +represents one of the primal variables. Alternatively, by means of channelling +constraints to link the primal and dual variables, we can have a combined model +with both sets of variables. In this paper, we perform an extensive theoretical +and empirical study of such primal, dual and combined models for two classes of +problems: permutation problems and injection problems. Our results show that it +often be advantageous to use multiple viewpoints, and to have constraints which +channel between them to maintain consistency. They also illustrate a general +methodology for comparing different constraint models. +",Dual Modelling of Permutation and Injection Problems +" This is the first of three planned papers describing ZAP, a satisfiability +engine that substantially generalizes existing tools while retaining the +performance characteristics of modern high-performance solvers. The fundamental +idea underlying ZAP is that many problems passed to such engines contain rich +internal structure that is obscured by the Boolean representation used; our +goal is to define a representation in which this structure is apparent and can +easily be exploited to improve computational performance. This paper is a +survey of the work underlying ZAP, and discusses previous attempts to improve +the performance of the Davis-Putnam-Logemann-Loveland algorithm by exploiting +the structure of the problem being solved. We examine existing ideas including +extensions of the Boolean language to allow cardinality constraints, +pseudo-Boolean representations, symmetry, and a limited form of quantification. +While this paper is intended as a survey, our research results are contained in +the two subsequent articles, with the theoretical structure of ZAP described in +the second paper in this series, and ZAP's implementation described in the +third. +","Generalizing Boolean Satisfiability I: Background and Survey of Existing + Work" +" We address the problem of finding the shortest path between two points in an +unknown real physical environment, where a traveling agent must move around in +the environment to explore unknown territory. We introduce the Physical-A* +algorithm (PHA*) for solving this problem. PHA* expands all the mandatory nodes +that A* would expand and returns the shortest path between the two points. +However, due to the physical nature of the problem, the complexity of the +algorithm is measured by the traveling effort of the moving agent and not by +the number of generated nodes, as in standard A*. PHA* is presented as a +two-level algorithm, such that its high level, A*, chooses the next node to be +expanded and its low level directs the agent to that node in order to explore +it. We present a number of variations for both the high-level and low-level +procedures and evaluate their performance theoretically and experimentally. We +show that the travel cost of our best variation is fairly close to the optimal +travel cost, assuming that the mandatory nodes of A* are known in advance. We +then generalize our algorithm to the multi-agent case, where a number of +cooperative agents are designed to solve the problem. Specifically, we provide +an experimental implementation for such a system. It should be noted that the +problem addressed here is not a navigation problem, but rather a problem of +finding the shortest path between two points for future usage. +","PHA*: Finding the Shortest Path with A* in An Unknown Physical + Environment" +" Value iteration is a popular algorithm for finding near optimal policies for +POMDPs. It is inefficient due to the need to account for the entire belief +space, which necessitates the solution of large numbers of linear programs. In +this paper, we study value iteration restricted to belief subsets. We show +that, together with properly chosen belief subsets, restricted value iteration +yields near-optimal policies and we give a condition for determining whether a +given belief subset would bring about savings in space and time. We also apply +restricted value iteration to two interesting classes of POMDPs, namely +informative POMDPs and near-discernible POMDPs. +",Restricted Value Iteration: Theory and Algorithms +" Many researchers in artificial intelligence are beginning to explore the use +of soft constraints to express a set of (possibly conflicting) problem +requirements. A soft constraint is a function defined on a collection of +variables which associates some measure of desirability with each possible +combination of values for those variables. However, the crucial question of the +computational complexity of finding the optimal solution to a collection of +soft constraints has so far received very little attention. In this paper we +identify a class of soft binary constraints for which the problem of finding +the optimal solution is tractable. In other words, we show that for any given +set of such constraints, there exists a polynomial time algorithm to determine +the assignment having the best overall combined measure of desirability. This +tractable class includes many commonly-occurring soft constraints, such as 'as +near as possible' or 'as soon as possible after', as well as crisp constraints +such as 'greater than'. Finally, we show that this tractable class is maximal, +in the sense that adding any other form of soft binary constraint which is not +in the class gives rise to a class of problems which is NP-hard. +",A Maximal Tractable Class of Soft Constraints +" Efficient implementations of DPLL with the addition of clause learning are +the fastest complete Boolean satisfiability solvers and can handle many +significant real-world problems, such as verification, planning and design. +Despite its importance, little is known of the ultimate strengths and +limitations of the technique. This paper presents the first precise +characterization of clause learning as a proof system (CL), and begins the task +of understanding its power by relating it to the well-studied resolution proof +system. In particular, we show that with a new learning scheme, CL can provide +exponentially shorter proofs than many proper refinements of general resolution +(RES) satisfying a natural property. These include regular and Davis-Putnam +resolution, which are already known to be much stronger than ordinary DPLL. We +also show that a slight variant of CL with unlimited restarts is as powerful as +RES itself. Translating these analytical results to practice, however, presents +a challenge because of the nondeterministic nature of clause learning +algorithms. We propose a novel way of exploiting the underlying problem +structure, in the form of a high level problem description such as a graph or +PDDL specification, to guide clause learning algorithms toward faster +solutions. We show that this leads to exponential speed-ups on grid and +randomized pebbling problems, as well as substantial improvements on certain +ordering formulas. +",Towards Understanding and Harnessing the Potential of Clause Learning +" Argumentation is based on the exchange and valuation of interacting +arguments, followed by the selection of the most acceptable of them (for +example, in order to take a decision, to make a choice). Starting from the +framework proposed by Dung in 1995, our purpose is to introduce 'graduality' in +the selection of the best arguments, i.e., to be able to partition the set of +the arguments in more than the two usual subsets of 'selected' and +'non-selected' arguments in order to represent different levels of selection. +Our basic idea is that an argument is all the more acceptable if it can be +preferred to its attackers. First, we discuss general principles underlying a +'gradual' valuation of arguments based on their interactions. Following these +principles, we define several valuation models for an abstract argumentation +system. Then, we introduce 'graduality' in the concept of acceptability of +arguments. We propose new acceptability classes and a refinement of existing +classes taking advantage of an available 'gradual' valuation. +",Graduality in Argumentation +" Inductive learning is based on inferring a general rule from a finite data +set and using it to label new data. In transduction one attempts to solve the +problem of using a labeled training set to label a set of unlabeled points, +which are given to the learner prior to learning. Although transduction seems +at the outset to be an easier task than induction, there have not been many +provably useful algorithms for transduction. Moreover, the precise relation +between induction and transduction has not yet been determined. The main +theoretical developments related to transduction were presented by Vapnik more +than twenty years ago. One of Vapnik's basic results is a rather tight error +bound for transductive classification based on an exact computation of the +hypergeometric tail. While tight, this bound is given implicitly via a +computational routine. Our first contribution is a somewhat looser but explicit +characterization of a slightly extended PAC-Bayesian version of Vapnik's +transductive bound. This characterization is obtained using concentration +inequalities for the tail of sums of random variables obtained by sampling +without replacement. We then derive error bounds for compression schemes such +as (transductive) support vector machines and for transduction algorithms based +on clustering. The main observation used for deriving these new error bounds +and algorithms is that the unlabeled test points, which in the transductive +setting are known in advance, can be used in order to construct useful data +dependent prior distributions over the hypothesis space. +","Explicit Learning Curves for Transduction and Application to Clustering + and Compression Algorithms" +" Decentralized control of cooperative systems captures the operation of a +group of decision makers that share a single global objective. The difficulty +in solving optimally such problems arises when the agents lack full +observability of the global state of the system when they operate. The general +problem has been shown to be NEXP-complete. In this paper, we identify classes +of decentralized control problems whose complexity ranges between NEXP and P. +In particular, we study problems characterized by independent transitions, +independent observations, and goal-oriented objective functions. Two algorithms +are shown to solve optimally useful classes of goal-oriented decentralized +processes in polynomial time. This paper also studies information sharing among +the decision-makers, which can improve their performance. We distinguish +between three ways in which agents can exchange information: indirect +communication, direct communication and sharing state features that are not +controlled by the agents. Our analysis shows that for every class of problems +we consider, introducing direct or indirect communication does not change the +worst-case complexity. The results provide a better understanding of the +complexity of decentralized control problems that arise in practice and +facilitate the development of planning algorithms for these problems. +","Decentralized Control of Cooperative Systems: Categorization and + Complexity Analysis" +" In this paper, we confront the problem of applying reinforcement learning to +agents that perceive the environment through many sensors and that can perform +parallel actions using many actuators as is the case in complex autonomous +robots. We argue that reinforcement learning can only be successfully applied +to this case if strong assumptions are made on the characteristics of the +environment in which the learning is performed, so that the relevant sensor +readings and motor commands can be readily identified. The introduction of such +assumptions leads to strongly-biased learning systems that can eventually lose +the generality of traditional reinforcement-learning algorithms. In this line, +we observe that, in realistic situations, the reward received by the robot +depends only on a reduced subset of all the executed actions and that only a +reduced subset of the sensor inputs (possibly different in each situation and +for each action) are relevant to predict the reward. We formalize this property +in the so called 'categorizability assumption' and we present an algorithm that +takes advantage of the categorizability of the environment, allowing a decrease +in the learning time with respect to existing reinforcement-learning +algorithms. Results of the application of the algorithm to a couple of +simulated realistic-robotic problems (landmark-based navigation and the +six-legged robot gait generation) are reported to validate our approach and to +compare it to existing flat and generalization-based reinforcement-learning +approaches. +","Reinforcement Learning for Agents with Many Sensors and Actuators Acting + in Categorizable Environments" +" We explore a method for computing admissible heuristic evaluation functions +for search problems. It utilizes pattern databases, which are precomputed +tables of the exact cost of solving various subproblems of an existing problem. +Unlike standard pattern database heuristics, however, we partition our problems +into disjoint subproblems, so that the costs of solving the different +subproblems can be added together without overestimating the cost of solving +the original problem. Previously, we showed how to statically partition the +sliding-tile puzzles into disjoint groups of tiles to compute an admissible +heuristic, using the same partition for each state and problem instance. Here +we extend the method and show that it applies to other domains as well. We also +present another method for additive heuristics which we call dynamically +partitioned pattern databases. Here we partition the problem into disjoint +subproblems for each state of the search dynamically. We discuss the pros and +cons of each of these methods and apply both methods to three different problem +domains: the sliding-tile puzzles, the 4-peg Towers of Hanoi problem, and +finding an optimal vertex cover of a graph. We find that in some problem +domains, static partitioning is most effective, while in others dynamic +partitioning is a better choice. In each of these problem domains, either +statically partitioned or dynamically partitioned pattern database heuristics +are the best known heuristics for the problem. +",Additive Pattern Database Heuristics +" This paper is concerned with algorithms for prediction of discrete sequences +over a finite alphabet, using variable order Markov models. The class of such +algorithms is large and in principle includes any lossless compression +algorithm. We focus on six prominent prediction algorithms, including Context +Tree Weighting (CTW), Prediction by Partial Match (PPM) and Probabilistic +Suffix Trees (PSTs). We discuss the properties of these algorithms and compare +their performance using real life sequences from three domains: proteins, +English text and music pieces. The comparison is made with respect to +prediction quality as measured by the average log-loss. We also compare +classification algorithms based on these predictors with respect to a number of +large protein classification tasks. Our results indicate that a ""decomposed"" +CTW (a variant of the CTW algorithm) and PPM outperform all other algorithms in +sequence prediction tasks. Somewhat surprisingly, a different algorithm, which +is a modification of the Lempel-Ziv compression algorithm, significantly +outperforms all algorithms on the protein classification problems. +",On Prediction Using Variable Order Markov Models +" Many known planning tasks have inherent constraints concerning the best order +in which to achieve the goals. A number of research efforts have been made to +detect such constraints and to use them for guiding search, in the hope of +speeding up the planning process. We go beyond the previous approaches by +considering ordering constraints not only over the (top-level) goals, but also +over the sub-goals that will necessarily arise during planning. Landmarks are +facts that must be true at some point in every valid solution plan. We extend +Koehler and Hoffmann's definition of reasonable orders between top level goals +to the more general case of landmarks. We show how landmarks can be found, how +their reasonable orders can be approximated, and how this information can be +used to decompose a given planning task into several smaller sub-tasks. Our +methodology is completely domain- and planner-independent. The implementation +demonstrates that the approach can yield significant runtime performance +improvements when used as a control loop around state-of-the-art sub-optimal +planning systems, as exemplified by FF and LPG. +",Ordered Landmarks in Planning +" Standard value function approaches to finding policies for Partially +Observable Markov Decision Processes (POMDPs) are generally considered to be +intractable for large models. The intractability of these algorithms is to a +large extent a consequence of computing an exact, optimal policy over the +entire belief space. However, in real-world POMDP problems, computing the +optimal policy for the full belief space is often unnecessary for good control +even for problems with complicated policy classes. The beliefs experienced by +the controller often lie near a structured, low-dimensional subspace embedded +in the high-dimensional belief space. Finding a good approximation to the +optimal value function for only this subspace can be much easier than computing +the full value function. We introduce a new method for solving large-scale +POMDPs by reducing the dimensionality of the belief space. We use Exponential +family Principal Components Analysis (Collins, Dasgupta and Schapire, 2002) to +represent sparse, high-dimensional belief spaces using small sets of learned +features of the belief state. We then plan only in terms of the low-dimensional +belief features. By planning in this low-dimensional space, we can find +policies for POMDP models that are orders of magnitude larger than models that +can be handled by conventional techniques. We demonstrate the use of this +algorithm on a synthetic problem and on mobile robot navigation tasks. +",Finding Approximate POMDP solutions Through Belief Compression +" We propose a model for errors in sung queries, a variant of the hidden Markov +model (HMM). This is a solution to the problem of identifying the degree of +similarity between a (typically error-laden) sung query and a potential target +in a database of musical works, an important problem in the field of music +information retrieval. Similarity metrics are a critical component of +query-by-humming (QBH) applications which search audio and multimedia databases +for strong matches to oral queries. Our model comprehensively expresses the +types of error or variation between target and query: cumulative and +non-cumulative local errors, transposition, tempo and tempo changes, +insertions, deletions and modulation. The model is not only expressive, but +automatically trainable, or able to learn and generalize from query examples. +We present results of simulations, designed to assess the discriminatory +potential of the model, and tests with real sung queries, to demonstrate +relevance to real-world applications. +",A Comprehensive Trainable Error Model for Sung Music Queries +" In recent years, there has been much interest in phase transitions of +combinatorial problems. Phase transitions have been successfully used to +analyze combinatorial optimization problems, characterize their typical-case +features and locate the hardest problem instances. In this paper, we study +phase transitions of the asymmetric Traveling Salesman Problem (ATSP), an +NP-hard combinatorial optimization problem that has many real-world +applications. Using random instances of up to 1,500 cities in which intercity +distances are uniformly distributed, we empirically show that many properties +of the problem, including the optimal tour cost and backbone size, experience +sharp transitions as the precision of intercity distances increases across a +critical value. Our experimental results on the costs of the ATSP tours and +assignment problem agree with the theoretical result that the asymptotic cost +of assignment problem is pi ^2 /6 the number of cities goes to infinity. In +addition, we show that the average computational cost of the well-known +branch-and-bound subtour elimination algorithm for the problem also exhibits a +thrashing behavior, transitioning from easy to difficult as the distance +precision increases. These results answer positively an open question regarding +the existence of phase transitions in the ATSP, and provide guidance on how +difficult ATSP problem instances should be generated. +","Phase Transitions and Backbones of the Asymmetric Traveling Salesman + Problem" +" A time series consists of a series of values or events obtained over repeated +measurements in time. Analysis of time series represents and important tool in +many application areas, such as stock market analysis, process and quality +control, observation of natural phenomena, medical treatments, etc. A vital +component in many types of time-series analysis is the choice of an appropriate +distance/similarity measure. Numerous measures have been proposed to date, with +the most successful ones based on dynamic programming. Being of quadratic time +complexity, however, global constraints are often employed to limit the search +space in the matrix during the dynamic programming procedure, in order to speed +up computation. Furthermore, it has been reported that such constrained +measures can also achieve better accuracy. In this paper, we investigate two +representative time-series distance/similarity measures based on dynamic +programming, Dynamic Time Warping (DTW) and Longest Common Subsequence (LCS), +and the effects of global constraints on them. Through extensive experiments on +a large number of time-series data sets, we demonstrate how global constrains +can significantly reduce the computation time of DTW and LCS. We also show +that, if the constraint parameter is tight enough (less than 10-15% of +time-series length), the constrained measure becomes significantly different +from its unconstrained counterpart, in the sense of producing qualitatively +different 1-nearest neighbor graphs. This observation explains the potential +for accuracy gains when using constrained measures, highlighting the need for +careful tuning of constraint parameters in order to achieve a good trade-off +between speed and accuracy. +","The Influence of Global Constraints on Similarity Measures for + Time-Series Databases" +" We present in this paper our law that there is always a connection present +between two entities, with a selfconnection being present at least in each +node. An entity is an object, physical or imaginary, that is connected by a +path (or connection) and which is important for achieving the desired result of +the scenario. In machine learning, we state that for any scenario, a subject +entity is always, directly or indirectly, connected and affected by single or +multiple independent / dependent entities, and their impact on the subject +entity is dependent on various factors falling into the categories such as the +existenc +",Law of Connectivity in Machine Learning +" The importance of algorithm portfolio techniques for SAT has long been noted, +and a number of very successful systems have been devised, including the most +successful one --- SATzilla. However, all these systems are quite complex (to +understand, reimplement, or modify). In this paper we propose a new algorithm +portfolio for SAT that is extremely simple, but in the same time so efficient +that it outperforms SATzilla. For a new SAT instance to be solved, our +portfolio finds its k-nearest neighbors from the training set and invokes a +solver that performs the best at those instances. The main distinguishing +feature of our algorithm portfolio is the locality of the selection procedure +--- the selection of a SAT solver is based only on few instances similar to the +input one. +",Simple Algorithm Portfolio for SAT +" The superiority and inferiority ranking (SIR) method is a generation of the +well-known PROMETHEE method, which can be more efficient to deal with +multi-criterion decision making (MCDM) problem. Intuitionistic fuzzy sets +(IFSs), as an important extension of fuzzy sets (IFs), include both membership +functions and non-membership functions and can be used to, more precisely +describe uncertain information. In real world, decision situations are usually +under uncertain environment and involve multiple individuals who have their own +points of view on handing of decision problems. In order to solve uncertainty +group MCDM problem, we propose a novel intuitionistic fuzzy SIR method in this +paper. This approach uses intuitionistic fuzzy aggregation operators and SIR +ranking methods to handle uncertain information; integrate individual opinions +into group opinions; make decisions on multiple-criterion; and finally +structure a specific decision map. The proposed approach is illustrated in a +simulation of group decision making problem related to supply chain management. +","A Novel Multicriteria Group Decision Making Approach With Intuitionistic + Fuzzy SIR Method" +" This volume contains the papers presented at the first edition of the +Doctoral Consortium of the 5th International Symposium on Rules (RuleML +2011@IJCAI) held on July 19th, 2011 in Barcelona, as well as the poster session +papers of the RuleML 2011@IJCAI main conference. +","Proceedings of the Doctoral Consortium and Poster Session of the 5th + International Symposium on Rules (RuleML 2011@IJCAI)" +" Informledge System (ILS) is a knowledge network with autonomous nodes and +intelligent links that integrate and structure the pieces of knowledge. In this +paper, we put forward the strategies for knowledge embedding and retrieval in +an ILS. ILS is a powerful knowledge network system dealing with logical storage +and connectivity of information units to form knowledge using autonomous nodes +and multi-lateral links. In ILS, the autonomous nodes known as Knowledge +Network Nodes (KNN)s play vital roles which are not only used in storage, +parsing and in forming the multi-lateral linkages between knowledge points but +also in helping the realization of intelligent retrieval of linked information +units in the form of knowledge. Knowledge built in to the ILS forms the shape +of sphere. The intelligence incorporated into the links of a KNN helps in +retrieving various knowledge threads from a specific set of KNNs. A developed +entity of information realized through KNN forms in to the shape of a knowledge +cone +",Knowledge Embedding and Retrieval Strategies in an Informledge System +" The proposal of Elisa Marengo's thesis is to extend commitment protocols to +explicitly account for temporal regulations. This extension will satisfy two +needs: (1) it will allow representing, in a flexible and modular way, temporal +regulations with a normative force, posed on the interaction, so as to +represent conventions, laws and suchlike; (2) it will allow committing to +complex conditions, which describe not only what will be achieved but to some +extent also how. These two aspects will be deeply investigated in the proposal +of a unified framework, which is part of the ongoing work and will be included +in the thesis. +",Extend Commitment Protocols with Temporal Regulations: Why and How +" Rule-Based Systems have been in use for decades to solve a variety of +problems but not in the sensor informatics domain. Rules aid the aggregation of +low-level sensor readings to form a more complete picture of the real world and +help to address 10 identified challenges for sensor network middleware. This +paper presents the reader with an overview of a system architecture and a pilot +application to demonstrate the usefulness of a system integrating rules with +sensor middleware. +",Rule-Based Semantic Sensing +" Multi-Context Systems are an expressive formalism to model (possibly) +non-monotonic information exchange between heterogeneous knowledge bases. Such +information exchange, however, often comes with unforseen side-effects leading +to violation of constraints, making the system inconsistent, and thus unusable. +Although there are many approaches to assess and repair a single inconsistent +knowledge base, the heterogeneous nature of Multi-Context Systems poses +problems which have not yet been addressed in a satisfying way: How to identify +and explain a inconsistency that spreads over multiple knowledge bases with +different logical formalisms (e.g., logic programs and ontologies)? What are +the causes of inconsistency if inference/information exchange is non-monotonic +(e.g., absent information as cause)? How to deal with inconsistency if access +to knowledge bases is restricted (e.g., companies exchange information, but do +not allow arbitrary modifications to their knowledge bases)? Many traditional +approaches solely aim for a consistent system, but automatic removal of +inconsistency is not always desireable. Therefore a human operator has to be +supported in finding the erroneous parts contributing to the inconsistency. In +my thesis those issues will be adressed mainly from a foundational perspective, +while our research project also provides algorithms and prototype +implementations. +",Advancing Multi-Context Systems by Inconsistency Management +" We present a description of the PhD thesis which aims to propose a rule-based +query answering method for relational data. In this approach we use an +additional knowledge which is represented as a set of rules and describes the +source data at concept (ontological) level. Queries are posed in the terms of +abstract level. We present two methods. The first one uses hybrid reasoning and +the second one exploits only forward chaining. These two methods are +demonstrated by the prototypical implementation of the system coupled with the +Jess engine. Tests are performed on the knowledge base of the selected economic +crimes: fraudulent disbursement and money laundering. +","Rule-based query answering method for a knowledge base of economic + crimes" +" IT Service Management deals with managing a broad range of items related to +complex system environments. As there is both, a close connection to business +interests and IT infrastructure, the application of semantic expressions which +are seamlessly integrated within applications for managing ITSM environments, +can help to improve transparency and profitability. This paper focuses on the +challenges regarding the integration of semantics and ontologies within ITSM +environments. It will describe the paradigm of relationships and inheritance +within complex service trees and will present an approach of ontologically +expressing them. Furthermore, the application of SBVR-based rules as executable +SQL triggers will be discussed. Finally, the broad range of topics for further +research, derived from the findings, will be presented. +","Semantic-ontological combination of Business Rules and Business + Processes in IT Service Management" +" The study proposes a framework of ONTOlogy-based Group Decision Support +System (ONTOGDSS) for decision process which exhibits the complex structure of +decision-problem and decision-group. It is capable of reducing the complexity +of problem structure and group relations. The system allows decision makers to +participate in group decision-making through the web environment, via the +ontology relation. It facilitates the management of decision process as a +whole, from criteria generation, alternative evaluation, and opinion +interaction to decision aggregation. The embedded ontology structure in +ONTOGDSS provides the important formal description features to facilitate +decision analysis and verification. It examines the software architecture, the +selection methods, the decision path, etc. Finally, the ontology application of +this system is illustrated with specific real case to demonstrate its +potentials towards decision-making development. +",An Ontology-driven Framework for Supporting Complex Decision Process +" Fault diagnosis and failure prognosis are essential techniques in improving +the safety of many manufacturing systems. Therefore, on-line fault detection +and isolation is one of the most important tasks in safety-critical and +intelligent control systems. Computational intelligence techniques are being +investigated as extension of the traditional fault diagnosis methods. This +paper discusses the Temporal Neuro-Fuzzy Systems (TNFS) fault diagnosis within +an application study of a manufacturing system. The key issues of finding a +suitable structure for detecting and isolating ten realistic actuator faults +are described. Within this framework, data-processing interactive software of +simulation baptized NEFDIAG (NEuro Fuzzy DIAGnosis) version 1.0 is developed. + This software devoted primarily to creation, training and test of a +classification Neuro-Fuzzy system of industrial process failures. NEFDIAG can +be represented like a special type of fuzzy perceptron, with three layers used +to classify patterns and failures. The system selected is the workshop of +SCIMAT clinker, cement factory in Algeria. +",A Temporal Neuro-Fuzzy Monitoring System to Manufacturing Systems +" Open-text (or open-domain) semantic parsers are designed to interpret any +statement in natural language by inferring a corresponding meaning +representation (MR). Unfortunately, large scale systems cannot be easily +machine-learned due to lack of directly supervised data. We propose here a +method that learns to assign MRs to a wide range of text (using a dictionary of +more than 70,000 words, which are mapped to more than 40,000 entities) thanks +to a training scheme that combines learning from WordNet and ConceptNet with +learning from raw text. The model learns structured embeddings of words, +entities and MRs via a multi-task training process operating on these diverse +sources of data that integrates all the learnt knowledge into a single system. +This work ends up combining methods for knowledge acquisition, semantic +parsing, and word-sense disambiguation. Experiments on various tasks indicate +that our approach is indeed successful and can form a basis for future more +sophisticated systems. +","Towards Open-Text Semantic Parsing via Multi-Task Learning of Structured + Embeddings" +" Commute Time Distance (CTD) is a random walk based metric on graphs. CTD has +found widespread applications in many domains including personalized search, +collaborative filtering and making search engines robust against manipulation. +Our interest is inspired by the use of CTD as a metric for anomaly detection. +It has been shown that CTD can be used to simultaneously identify both global +and local anomalies. Here we propose an accurate and efficient approximation +for computing the CTD in an incremental fashion in order to facilitate +real-time applications. An online anomaly detection algorithm is designed where +the CTD of each new arriving data point to any point in the current graph can +be estimated in constant time ensuring a real-time response. Moreover, the +proposed approach can also be applied in many other applications that utilize +commute time distance. +",Online Anomaly Detection Systems Using Incremental Commute Time +" The promise of lifted probabilistic inference is to carry out probabilistic +inference in a relational probabilistic model without needing to reason about +each individual separately (grounding out the representation) by treating the +undistinguished individuals as a block. Current exact methods still need to +ground out in some cases, typically because the representation of the +intermediate results is not closed under the lifted operations. We set out to +answer the question as to whether there is some fundamental reason why lifted +algorithms would need to ground out undifferentiated individuals. We have two +main results: (1) We completely characterize the cases where grounding is +polynomial in a population size, and show how we can do lifted inference in +time polynomial in the logarithm of the population size for these cases. (2) +For the case of no-argument and single-argument parametrized random variables +where the grounding is not polynomial in a population size, we present lifted +inference which is polynomial in the population size whereas grounding is +exponential. Neither of these cases requires reasoning separately about the +individuals that are not explicitly mentioned. +",Towards Completely Lifted Search-based Probabilistic Inference +" Using a recently proposed model for combinatorial landscapes, Local Optima +Networks (LON), we conduct a thorough analysis of two types of instances of the +Quadratic Assignment Problem (QAP). This network model is a reduction of the +landscape in which the nodes correspond to the local optima, and the edges +account for the notion of adjacency between their basins of attraction. The +model was inspired by the notion of 'inherent network' of potential energy +surfaces proposed in physical-chemistry. The local optima networks extracted +from the so called uniform and real-like QAP instances, show features clearly +distinguishing these two types of instances. Apart from a clear confirmation +that the search difficulty increases with the problem dimension, the analysis +provides new confirming evidence explaining why the real-like instances are +easier to solve exactly using heuristic search, while the uniform instances are +easier to solve approximately. Although the local optima network model is still +under development, we argue that it provides a novel view of combinatorial +landscapes, opening up the possibilities for new analytical tools and +understanding of problem difficulty in combinatorial optimization. +",Local Optima Networks of the Quadratic Assignment Problem +" In previous work we have introduced a network-based model that abstracts many +details of the underlying landscape and compresses the landscape information +into a weighted, oriented graph which we call the local optima network. The +vertices of this graph are the local optima of the given fitness landscape, +while the arcs are transition probabilities between local optima basins. Here +we extend this formalism to neutral fitness landscapes, which are common in +difficult combinatorial search spaces. By using two known neutral variants of +the NK family (i.e. NKp and NKq) in which the amount of neutrality can be tuned +by a parameter, we show that our new definitions of the optima networks and the +associated basins are consistent with the previous definitions for the +non-neutral case. Moreover, our empirical study and statistical analysis show +that the features of neutral landscapes interpolate smoothly between landscapes +with maximum neutrality and non-neutral ones. We found some unknown structural +differences between the two studied families of neutral landscapes. But +overall, the network features studied confirmed that neutrality, in landscapes +with percolating neutral networks, may enhance heuristic search. Our current +methodology requires the exhaustive enumeration of the underlying search space. +Therefore, sampling techniques should be developed before this analysis can +have practical implications. We argue, however, that the proposed model offers +a new perspective into the problem difficulty of combinatorial optimization +problems and may inspire the design of more effective search heuristics. +",Local Optima Networks of NK Landscapes with Neutrality +" In this paper, we study the exploration / exploitation trade-off in cellular +genetic algorithms. We define a new selection scheme, the centric selection, +which is tunable and allows controlling the selective pressure with a single +parameter. The equilibrium model is used to study the influence of the centric +selection on the selective pressure and a new model which takes into account +problem dependent statistics and selective pressure in order to deal with the +exploration / exploitation trade-off is proposed: the punctuated equilibria +model. Performances on the quadratic assignment problem and NK-Landscapes put +in evidence an optimal exploration / exploitation trade-off on both of the +classes of problems. The punctuated equilibria model is used to explain these +results. +",Centric selection: a way to tune the exploration/exploitation trade-off +" Negative Slope Coefficient is an indicator of problem hardness that has been +introduced in 2004 and that has returned promising results on a large set of +problems. It is based on the concept of fitness cloud and works by partitioning +the cloud into a number of bins representing as many different regions of the +fitness landscape. The measure is calculated by joining the bins centroids by +segments and summing all their negative slopes. In this paper, for the first +time, we point out a potential problem of the Negative Slope Coefficient: we +study its value for different instances of the well known NK-landscapes and we +show how this indicator is dramatically influenced by the minimum number of +points contained into a bin. Successively, we formally justify this behavior of +the Negative Slope Coefficient and we discuss pros and cons of this measure. +","NK landscapes difficulty and Negative Slope Coefficient: How Sampling + Influences the Results" +" Effective debugging of ontologies is an important prerequisite for their +broad application, especially in areas that rely on everyday users to create +and maintain knowledge bases, such as the Semantic Web. In such systems +ontologies capture formalized vocabularies of terms shared by its users. +However in many cases users have different local views of the domain, i.e. of +the context in which a given term is used. Inappropriate usage of terms +together with natural complications when formulating and understanding logical +descriptions may result in faulty ontologies. Recent ontology debugging +approaches use diagnosis methods to identify causes of the faults. In most +debugging scenarios these methods return many alternative diagnoses, thus +placing the burden of fault localization on the user. This paper demonstrates +how the target diagnosis can be identified by performing a sequence of +observations, that is, by querying an oracle about entailments of the target +ontology. To identify the best query we propose two query selection strategies: +a simple ""split-in-half"" strategy and an entropy-based strategy. The latter +allows knowledge about typical user errors to be exploited to minimize the +number of queries. Our evaluation showed that the entropy-based method +significantly reduces the number of required queries compared to the +""split-in-half"" approach. We experimented with different probability +distributions of user errors and different qualities of the a-priori +probabilities. Our measurements demonstrated the superiority of entropy-based +query selection even in cases where all fault probabilities are equal, i.e. +where no information about typical user errors is available. +","Interactive ontology debugging: two query strategies for efficient fault + localization" +" The web of data consists of data published on the web in such a way that they +can be interpreted and connected together. It is thus critical to establish +links between these data, both for the web of data and for the semantic web +that it contributes to feed. We consider here the various techniques developed +for that purpose and analyze their commonalities and differences. We propose a +general framework and show how the diverse techniques fit in the framework. +From this framework we consider the relation between data interlinking and +ontology matching. Although, they can be considered similar at a certain level +(they both relate formal entities), they serve different purposes, but would +find a mutual benefit at collaborating. We thus present a scheme under which it +is possible for data linking tools to take advantage of ontology alignments. +",MeLinDa: an interlinking framework for the web of data +" Symmetries occur naturally in CSP or SAT problems and are not very difficult +to discover, but using them to prune the search space tends to be very +challenging. Indeed, this usually requires finding specific elements in a group +of symmetries that can be huge, and the problem of their very existence is +NP-hard. We formulate such an existence problem as a constraint problem on one +variable (the symmetry to be used) ranging over a group, and try to find +restrictions that may be solved in polynomial time. By considering a simple +form of constraints (restricted by a cardinality k) and the class of groups +that have the structure of Fp-vector spaces, we propose a partial algorithm +based on linear algebra. This polynomial algorithm always applies when k=p=2, +but may fail otherwise as we prove the problem to be NP-hard for all other +values of k and p. Experiments show that this approach though restricted should +allow for an efficient use of at least some groups of symmetries. We conclude +with a few directions to be explored to efficiently solve this problem on the +general case. +",Solving Linear Constraints in Elementary Abelian p-Groups of Symmetries +" Given a causal model of some domain and a particular story that has taken +place in this domain, the problem of actual causation is deciding which of the +possible causes for some effect actually caused it. One of the most influential +approaches to this problem has been developed by Halpern and Pearl in the +context of structural models. In this paper, I argue that this is actually not +the best setting for studying this problem. As an alternative, I offer the +probabilistic logic programming language of CP-logic. Unlike structural models, +CP-logic incorporates the deviant/default distinction that is generally +considered an important aspect of actual causation, and it has an explicitly +dynamic semantics, which helps to formalize the stories that serve as input to +an actual causation problem. +",Actual Causation in CP-logic +" This paper investigates under which conditions instantiation-based proof +procedures can be combined in a nested way, in order to mechanically construct +new instantiation procedures for richer theories. Interesting applications in +the field of verification are emphasized, particularly for handling extensions +of the theory of arrays. +",Instantiation Schemes for Nested Theories +" Automating the design of heuristic search methods is an active research field +within computer science, artificial intelligence and operational research. In +order to make these methods more generally applicable, it is important to +eliminate or reduce the role of the human expert in the process of designing an +effective methodology to solve a given computational search problem. +Researchers developing such methodologies are often constrained on the number +of problem domains on which to test their adaptive, self-configuring +algorithms; which can be explained by the inherent difficulty of implementing +their corresponding domain specific software components. + This paper presents HyFlex, a software framework for the development of +cross-domain search methodologies. The framework features a common software +interface for dealing with different combinatorial optimisation problems, and +provides the algorithm components that are problem specific. In this way, the +algorithm designer does not require a detailed knowledge the problem domains, +and thus can concentrate his/her efforts in designing adaptive general-purpose +heuristic search algorithms. Four hard combinatorial problems are fully +implemented (maximum satisfiability, one dimensional bin packing, permutation +flow shop and personnel scheduling), each containing a varied set of instance +data (including real-world industrial applications) and an extensive set of +problem specific heuristics and search operators. The framework forms the basis +for the first International Cross-domain Heuristic Search Challenge (CHeSC), +and it is currently in use by the international research community. In summary, +HyFlex represents a valuable new benchmark of heuristic search generality, with +which adaptive cross-domain algorithms are being easily developed, and reliably +compared. +",HyFlex: A Benchmark Framework for Cross-domain Heuristic Search +" In order to address complex systems, apply pattern recongnition on their +evolution could play an key role to understand their dynamics. Global patterns +are required to detect emergent concepts and trends, some of them with +qualitative nature. Formal Concept Analysis (FCA) is a theory whose goal is to +discover and to extract Knowledge from qualitative data. It provides tools for +reasoning with implication basis (and association rules). Implications and +association rules are usefull to reasoning on previously selected attributes, +providing a formal foundation for logical reasoning. In this paper we analyse +how to apply FCA reasoning to increase confidence in sports betting, by means +of detecting temporal regularities from data. It is applied to build a +Knowledge-Based system for confidence reasoning. +",Selecting Attributes for Sport Forecasting using Formal Concept Analysis +" Perfectly rational decision-makers maximize expected utility, but crucially +ignore the resource costs incurred when determining optimal actions. Here we +employ an axiomatic framework for bounded rational decision-making based on a +thermodynamic interpretation of resource costs as information costs. This leads +to a variational ""free utility"" principle akin to thermodynamical free energy +that trades off utility and information costs. We show that bounded optimal +control solutions can be derived from this variational principle, which leads +in general to stochastic policies. Furthermore, we show that risk-sensitive and +robust (minimax) control schemes fall out naturally from this framework if the +environment is considered as a bounded rational and perfectly rational +opponent, respectively. When resource costs are ignored, the maximum expected +utility principle is recovered. +","Information, Utility & Bounded Rationality" +" ROC curves and cost curves are two popular ways of visualising classifier +performance, finding appropriate thresholds according to the operating +condition, and deriving useful aggregated measures such as the area under the +ROC curve (AUC) or the area under the optimal cost curve. In this note we +present some new findings and connections between ROC space and cost space, by +using the expected loss over a range of operating conditions. In particular, we +show that ROC curves can be transferred to cost space by means of a very +natural way of understanding how thresholds should be chosen, by selecting the +threshold such that the proportion of positive predictions equals the operating +condition (either in the form of cost proportion or skew). We call these new +curves {ROC Cost Curves}, and we demonstrate that the expected loss as measured +by the area under these curves is linearly related to AUC. This opens up a +series of new possibilities and clarifies the notion of cost curve and its +relation to ROC analysis. In addition, we show that for a classifier that +assigns the scores in an evenly-spaced way, these curves are equal to the Brier +Curves. As a result, this establishes the first clear connection between AUC +and the Brier score. +",Technical Note: Towards ROC Curves in Cost Space +" OWL 2 has been standardized by the World Wide Web Consortium (W3C) as a +family of ontology languages for the Semantic Web. The most expressive of these +languages is OWL 2 Full, but to date no reasoner has been implemented for this +language. Consistency and entailment checking are known to be undecidable for +OWL 2 Full. We have translated a large fragment of the OWL 2 Full semantics +into first-order logic, and used automated theorem proving systems to do +reasoning based on this theory. The results are promising, and indicate that +this approach can be applied in practice for effective OWL reasoning, beyond +the capabilities of current Semantic Web reasoners. + This is an extended version of a paper with the same title that has been +published at CADE 2011, LNAI 6803, pp. 446-460. The extended version provides +appendices with additional resources that were used in the reported evaluation. +","Reasoning in the OWL 2 Full Ontology Language using First-Order + Automated Theorem Proving" +" This paper introduces 'just enough' principles and 'systems engineering' +approach to the practice of ontology development to provide a minimal yet +complete, lightweight, agile and integrated development process, supportive of +stakeholder management and implementation independence. +",'Just Enough' Ontology Engineering +" In this paper, we investigate the following question: how could you write +such computer programs that can work like conscious beings? The motivation +behind this question is that we want to create such applications that can see +the future. The aim of this paper is to provide an overall conceptual framework +for this new approach to machine consciousness. So we introduce a new +programming paradigm called Consciousness Oriented Programming (COP). +",Conscious Machines and Consciousness Oriented Programming +" There has been a noticeable shift in the relative composition of the industry +in the developed countries in recent years; manufacturing is decreasing while +the service sector is becoming more important. However, currently most +simulation models for investigating service systems are still built in the same +way as manufacturing simulation models, using a process-oriented world view, +i.e. they model the flow of passive entities through a system. These kinds of +models allow studying aspects of operational management but are not well suited +for studying the dynamics that appear in service systems due to human +behaviour. For these kinds of studies we require tools that allow modelling the +system and entities using an object-oriented world view, where intelligent +objects serve as abstract ""actors"" that are goal directed and can behave +proactively. In our work we combine process-oriented discrete event simulation +modelling and object-oriented agent based simulation modelling to investigate +the impact of people management practices on retail productivity. In this +paper, we reveal in a series of experiments what impact considering proactivity +can have on the output accuracy of simulation models of human centric systems. +The model and data we use for this investigation are based on a case study in a +UK department store. We show that considering proactivity positively influences +the validity of these kinds of models and therefore allows analysts to make +better recommendations regarding strategies to apply people management +practises. +","A First Approach on Modelling Staff Proactiveness in Retail Simulation + Models" +" A fact apparently not observed earlier in the literature of nonmonotonic +reasoning is that Reiter, in his default logic paper, did not directly +formalize informal defaults. Instead, he translated a default into a certain +natural language proposition and provided a formalization of the latter. A few +years later, Moore noted that propositions like the one used by Reiter are +fundamentally different than defaults and exhibit a certain autoepistemic +nature. Thus, Reiter had developed his default logic as a formalization of +autoepistemic propositions rather than of defaults. + The first goal of this paper is to show that some problems of Reiter's +default logic as a formal way to reason about informal defaults are directly +attributable to the autoepistemic nature of default logic and to the mismatch +between informal defaults and the Reiter's formal defaults, the latter being a +formal expression of the autoepistemic propositions Reiter used as a +representation of informal defaults. + The second goal of our paper is to compare the work of Reiter and Moore. +While each of them attempted to formalize autoepistemic propositions, the modes +of reasoning in their respective logics were different. We revisit Moore's and +Reiter's intuitions and present them from the perspective of autotheoremhood, +where theories can include propositions referring to the theory's own theorems. +We then discuss the formalization of this perspective in the logics of Moore +and Reiter, respectively, using the unifying semantic framework for default and +autoepistemic logics that we developed earlier. We argue that Reiter's default +logic is a better formalization of Moore's intuitions about autoepistemic +propositions than Moore's own autoepistemic logic. +","Reiter's Default Logic Is a Logic of Autoepistemic Reasoning And a Good + One, Too" +" In 1991, Michael Gelfond introduced the language of epistemic specifications. +The goal was to develop tools for modeling problems that require some form of +meta-reasoning, that is, reasoning over multiple possible worlds. Despite their +relevance to knowledge representation, epistemic specifications have received +relatively little attention so far. In this paper, we revisit the formalism of +epistemic specification. We offer a new definition of the formalism, propose +several semantics (one of which, under syntactic restrictions we assume, turns +out to be equivalent to the original semantics by Gelfond), derive some +complexity results and, finally, show the effectiveness of the formalism for +modeling problems requiring meta-reasoning considered recently by Faber and +Woltran. All these results show that epistemic specifications deserve much more +attention that has been afforded to them so far. +",Revisiting Epistemic Specifications +" We discuss the evolution of aspects of nonmonotonic reasoning towards the +computational paradigm of answer-set programming (ASP). We give a general +overview of the roots of ASP and follow up with the personal perspective on +research developments that helped verbalize the main principles of ASP and +differentiated it from the classical logic programming. +","Origins of Answer-Set Programming - Some Background And Two Personal + Accounts" +" UCT, a state-of-the art algorithm for Monte Carlo tree sampling (MCTS), is +based on UCB, a sampling policy for the Multi-armed Bandit Problem (MAB) that +minimizes the accumulated regret. However, MCTS differs from MAB in that only +the final choice, rather than all arm pulls, brings a reward, that is, the +simple regret, as opposite to the cumulative regret, must be minimized. This +ongoing work aims at applying meta-reasoning techniques to MCTS, which is +non-trivial. We begin by introducing policies for multi-armed bandits with +lower simple regret than UCB, and an algorithm for MCTS which combines +cumulative and simple regret minimization and outperforms UCT. We also develop +a sampling scheme loosely based on a myopic version of perfect value of +information. Finite-time and asymptotic analysis of the policies is provided, +and the algorithms are compared empirically. +",Doing Better Than UCT: Rational Monte Carlo Sampling in Trees +" Self-Organizing Maps are commonly used for unsupervised learning purposes. +This paper is dedicated to the certain modification of SOM called SOMN +(Self-Organizing Mixture Networks) used as a mechanism for representing +grayscale digital images. Any grayscale digital image regarded as a +distribution function can be approximated by the corresponding Gaussian +mixture. In this paper, the use of SOMN is proposed in order to obtain such +approximations for input grayscale images in unsupervised manner. +","Self-Organizing Mixture Networks for Representation of Grayscale Digital + Images" +" Two different conceptions of emergence are reconciled as two instances of the +phenomenon of detection. In the process of comparing these two conceptions, we +find that the notions of complexity and detection allow us to form a unified +definition of emergence that clearly delineates the role of the observer. +",Detection and emergence +" The aim of this paper is to announce the release of a novel system for +abstract argumentation which is based on decomposition and dynamic programming. +We provide first experimental evaluations to show the feasibility of this +approach. +",dynPARTIX - A Dynamic Programming Reasoner for Abstract Argumentation +" Dung's famous abstract argumentation frameworks represent the core formalism +for many problems and applications in the field of argumentation which +significantly evolved within the last decade. Recent work in the field has thus +focused on implementations for these frameworks, whereby one of the main +approaches is to use Answer-Set Programming (ASP). While some of the +argumentation semantics can be nicely expressed within the ASP language, others +required rather cumbersome encoding techniques. Recent advances in ASP systems, +in particular, the metasp optimization frontend for the ASP-package +gringo/claspD provides direct commands to filter answer sets satisfying certain +subset-minimality (or -maximality) constraints. This allows for much simpler +encodings compared to the ones in standard ASP language. In this paper, we +experimentally compare the original encodings (for the argumentation semantics +based on preferred, semi-stable, and respectively, stage extensions) with new +metasp encodings. Moreover, we provide novel encodings for the recently +introduced resolution-based grounded semantics. Our experimental results +indicate that the metasp approach works well in those cases where the +complexity of the encoded problem is adequately mirrored within the metasp +approach. +","Making Use of Advances in Answer-Set Programming for Abstract + Argumentation Systems" +" In a knowledge discovery process, interpretation and evaluation of the mined +results are indispensable in practice. In the case of data clustering, however, +it is often difficult to see in what aspect each cluster has been formed. This +paper proposes a method for automatic and objective characterization or +""verbalization"" of the clusters obtained by mixture models, in which we collect +conjunctions of propositions (attribute-value pairs) that help us interpret or +evaluate the clusters. The proposed method provides us with a new, in-depth and +consistent tool for cluster interpretation/evaluation, and works for various +types of datasets including continuous attributes and missing values. +Experimental results with a couple of standard datasets exhibit the utility of +the proposed method, and the importance of the feedbacks from the +interpretation/evaluation step. +","Verbal Characterization of Probabilistic Clusters using Minimal + Discriminative Propositions" +" A brain-computer interface (BCI) may be used to control a prosthetic or +orthotic hand using neural activity from the brain. The core of this +sensorimotor BCI lies in the interpretation of the neural information extracted +from electroencephalogram (EEG). It is desired to improve on the interpretation +of EEG to allow people with neuromuscular disorders to perform daily +activities. This paper investigates the possibility of discriminating between +the EEG associated with wrist and finger movements. The EEG was recorded from +test subjects as they executed and imagined five essential hand movements using +both hands. Independent component analysis (ICA) and time-frequency techniques +were used to extract spectral features based on event-related +(de)synchronisation (ERD/ERS), while the Bhattacharyya distance (BD) was used +for feature reduction. Mahalanobis distance (MD) clustering and artificial +neural networks (ANN) were used as classifiers and obtained average accuracies +of 65 % and 71 % respectively. This shows that EEG discrimination between wrist +and finger movements is possible. The research introduces a new combination of +motor tasks to BCI research. +","Single-trial EEG Discrimination between Wrist and Finger Movement + Imagery and Execution in a Sensorimotor BCI" +" We present a constraint-based approach to interactive product configuration. +Our configurator tool FdConfig is based on feature models for the +representation of the product domain. Such models can be directly mapped into +constraint satisfaction problems and dealt with by appropriate constraint +solvers. During the interactive configuration process the user generates new +constraints as a result of his configuration decisions and even may retract +constraints posted earlier. We discuss the configuration process, explain the +underlying techniques and show optimizations. +",FdConfig: A Constraint-Based Interactive Product Configurator +" Answer-Set Programming (ASP) is an established declarative programming +paradigm. However, classical ASP lacks subprogram calls as in procedural +programming, and access to external computations (like remote procedure calls) +in general. The feature is desired for increasing modularity and---assuming +proper access in place---(meta-)reasoning over subprogram results. While +HEX-programs extend classical ASP with external source access, they do not +support calls of (sub-)programs upfront. We present nested HEX-programs, which +extend HEX-programs to serve the desired feature, in a user-friendly manner. +Notably, the answer sets of called sub-programs can be individually accessed. +This is particularly useful for applications that need to reason over answer +sets like belief set merging, user-defined aggregate functions, or preferences +of answer sets. +",Nested HEX-Programs +" Statistical relational learning techniques have been successfully applied in +a wide range of relational domains. In most of these applications, the human +designers capitalized on their background knowledge by following a +trial-and-error trajectory, where relational features are manually defined by a +human engineer, parameters are learned for those features on the training data, +the resulting model is validated, and the cycle repeats as the engineer adjusts +the set of features. This paper seeks to streamline application development in +large relational domains by introducing a light-weight approach that +efficiently evaluates relational features on pieces of the relational graph +that are streamed to it one at a time. We evaluate our approach on two social +media tasks and demonstrate that it leads to more accurate models that are +learned faster. +",Structure Selection from Streaming Relational Data +" In order to give appropriate semantics to qualitative conditionals of the +form ""if A then normally B"", ordinal conditional functions (OCFs) ranking the +possible worlds according to their degree of plausibility can be used. An OCF +accepting all conditionals of a knowledge base R can be characterized as the +solution of a constraint satisfaction problem. We present a high-level, +declarative approach using constraint logic programming techniques for solving +this constraint satisfaction problem. In particular, the approach developed +here supports the generation of all minimal solutions; these minimal solutions +are of special interest as they provide a basis for model-based inference from +R. +","A Constraint Logic Programming Approach for Computing Ordinal + Conditional Functions" +" Publishing private data on external servers incurs the problem of how to +avoid unwanted disclosure of confidential data. We study a problem of +confidentiality in extended disjunctive logic programs and show how it can be +solved by extended abduction. In particular, we analyze how credulous +non-monotonic reasoning affects confidentiality. +","Confidentiality-Preserving Data Publishing for Credulous Users by + Extended Abduction" +" In this paper, we present domain-specific languages (DSLs) that we devised +for their use in the implementation of a finite domain constraint programming +system, available as library(clpfd) in SWI-Prolog and YAP-Prolog. These DSLs +are used in propagator selection and constraint reification. In these areas, +they lead to concise specifications that are easy to read and reason about. At +compilation time, these specifications are translated to Prolog code, reducing +interpretative run-time overheads. The devised languages can be used in the +implementation of other finite domain constraint solvers as well and may +contribute to their correctness, conciseness and efficiency. +","Domain-specific Languages in a Finite Domain Constraint Programming + System" +" In this work a stand-alone preprocessor for SAT is presented that is able to +perform most of the known preprocessing techniques. Preprocessing a formula in +SAT is important for performance since redundancy can be removed. The +preprocessor is part of the SAT solver riss and is called Coprocessor. Not only +riss, but also MiniSat 2.2 benefit from it, because the SatELite preprocessor +of MiniSat does not implement recent techniques. By using more advanced +techniques, Coprocessor is able to reduce the redundancy in a formula further +and improves the overall solving performance. +",Coprocessor - a Standalone SAT Preprocessor +" We present an application focused on the design of resilient long-reach +passive optical networks. We specifically consider dual-parented networks +whereby each customer must be connected to two metro sites via local exchange +sites. An important property of such a placement is resilience to single metro +node failure. The objective of the application is to determine the optimal +position of a set of metro nodes such that the total optical fibre length is +minimized. We prove that this problem is NP-Complete. We present two +alternative combinatorial optimisation approaches to finding an optimal metro +node placement using: a mixed integer linear programming (MIP) formulation of +the problem; and, a hybrid approach that uses clustering as a preprocessing +step. We consider a detailed case-study based on a network for Ireland. The +hybrid approach scales well and finds solutions that are close to optimal, with +a runtime that is two orders-of-magnitude better than the MIP model. +","A Combinatorial Optimisation Approach to Designing Dual-Parented + Long-Reach Passive Optical Networks" +" Artificial general intelligence (AGI) refers to research aimed at tackling +the full problem of artificial intelligence, that is, create truly intelligent +agents. This sets it apart from most AI research which aims at solving +relatively narrow domains, such as character recognition, motion planning, or +increasing player satisfaction in games. But how do we know when an agent is +truly intelligent? A common point of reference in the AGI community is Legg and +Hutter's formal definition of universal intelligence, which has the appeal of +simplicity and generality but is unfortunately incomputable. Games of various +kinds are commonly used as benchmarks for ""narrow"" AI research, as they are +considered to have many important properties. We argue that many of these +properties carry over to the testing of general intelligence as well. We then +sketch how such testing could practically be carried out. The central part of +this sketch is an extension of universal intelligence to deal with finite time, +and the use of sampling of the space of games expressed in a suitably biased +game description language. +",Measuring Intelligence through Games +" We propose a structured approach to the problem of retrieval of images by +content and present a description logic that has been devised for the semantic +indexing and retrieval of images containing complex objects. As other +approaches do, we start from low-level features extracted with image analysis +to detect and characterize regions in an image. However, in contrast with +feature-based approaches, we provide a syntax to describe segmented regions as +basic objects and complex objects as compositions of basic ones. Then we +introduce a companion extensional semantics for defining reasoning services, +such as retrieval, classification, and subsumption. These services can be used +for both exact and approximate matching, using similarity measures. Using our +logical approach as a formal specification, we implemented a complete +client-server image retrieval system, which allows a user to pose both queries +by sketch and queries by example. A set of experiments has been carried out on +a testbed of images to assess the retrieval capabilities of the system in +comparison with expert users ranking. Results are presented adopting a +well-established measure of quality borrowed from textual information +retrieval. +",Structured Knowledge Representation for Image Retrieval +" Wind energy plays an increasing role in the supply of energy world-wide. The +energy output of a wind farm is highly dependent on the weather condition +present at the wind farm. If the output can be predicted more accurately, +energy suppliers can coordinate the collaborative production of different +energy sources more efficiently to avoid costly overproductions. + With this paper, we take a computer science perspective on energy prediction +based on weather data and analyze the important parameters as well as their +correlation on the energy output. To deal with the interaction of the different +parameters we use symbolic regression based on the genetic programming tool +DataModeler. + Our studies are carried out on publicly available weather and energy data for +a wind farm in Australia. We reveal the correlation of the different variables +for the energy output. The model obtained for energy prediction gives a very +reliable prediction of the energy output for newly given weather data. +","Predicting the Energy Output of Wind Farms Based on Weather Data: + Important Variables and their Correlation" +" We consider the problem of reconstructing vehicle trajectories from sparse +sequences of GPS points, for which the sampling interval is between 10 seconds +and 2 minutes. We introduce a new class of algorithms, called altogether path +inference filter (PIF), that maps GPS data in real time, for a variety of +trade-offs and scenarios, and with a high throughput. Numerous prior approaches +in map-matching can be shown to be special cases of the path inference filter +presented in this article. We present an efficient procedure for automatically +training the filter on new data, with or without ground truth observations. The +framework is evaluated on a large San Francisco taxi dataset and is shown to +improve upon the current state of the art. This filter also provides insights +about driving patterns of drivers. The path inference filter has been deployed +at an industrial scale inside the Mobile Millennium traffic information system, +and is used to map fleets of data in San Francisco, Sacramento, Stockholm and +Porto. +","The path inference filter: model-based low-latency map matching of probe + vehicle data" +" Software agents can be used to automate many of the tedious, time-consuming +information processing tasks that humans currently have to complete manually. +However, to do so, agent plans must be capable of representing the myriad of +actions and control flows required to perform those tasks. In addition, since +these tasks can require integrating multiple sources of remote information ? +typically, a slow, I/O-bound process ? it is desirable to make execution as +efficient as possible. To address both of these needs, we present a flexible +software agent plan language and a highly parallel execution system that enable +the efficient execution of expressive agent plans. The plan language allows +complex tasks to be more easily expressed by providing a variety of operators +for flexibly processing the data as well as supporting subplans (for +modularity) and recursion (for indeterminate looping). The executor is based on +a streaming dataflow model of execution to maximize the amount of operator and +data parallelism possible at runtime. We have implemented both the language and +executor in a system called THESEUS. Our results from testing THESEUS show that +streaming dataflow execution can yield significant speedups over both +traditional serial (von Neumann) as well as non-streaming dataflow-style +execution that existing software and robot agent execution systems currently +support. In addition, we show how plans written in the language we present can +represent certain types of subtasks that cannot be accomplished using the +languages supported by network query engines. Finally, we demonstrate that the +increased expressivity of our plan language does not hamper performance; +specifically, we show how data can be integrated from multiple remote sources +just as efficiently using our architecture as is possible with a +state-of-the-art streaming-dataflow network query engine. +","An Expressive Language and Efficient Execution System for Software + Agents" +" This work focuses on improving state-of-the-art in stochastic local search +(SLS) for solving Boolean satisfiability (SAT) instances arising from +real-world industrial SAT application domains. The recently introduced SLS +method CRSat has been shown to noticeably improve on previously suggested SLS +techniques in solving such real-world instances by combining +justification-based local search with limited Boolean constraint propagation on +the non-clausal formula representation form of Boolean circuits. In this work, +we study possibilities of further improving the performance of CRSat by +exploiting circuit-level structural knowledge for developing new search +heuristics for CRSat. To this end, we introduce and experimentally evaluate a +variety of search heuristics, many of which are motivated by circuit-level +heuristics originally developed in completely different contexts, e.g., for +electronic design automation applications. To the best of our knowledge, most +of the heuristics are novel in the context of SLS for SAT and, more generally, +SLS for constraint satisfaction problems. +","Structure-Based Local Search Heuristics for Circuit-Level Boolean + Satisfiability" +" This paper studies the problem of learning diagnostic policies from training +examples. A diagnostic policy is a complete description of the decision-making +actions of a diagnostician (i.e., tests followed by a diagnostic decision) for +all possible combinations of test results. An optimal diagnostic policy is one +that minimizes the expected total cost, which is the sum of measurement costs +and misdiagnosis costs. In most diagnostic settings, there is a tradeoff +between these two kinds of costs. This paper formalizes diagnostic decision +making as a Markov Decision Process (MDP). The paper introduces a new family of +systematic search algorithms based on the AO* algorithm to solve this MDP. To +make AO* efficient, the paper describes an admissible heuristic that enables +AO* to prune large parts of the search space. The paper also introduces several +greedy algorithms including some improvements over previously-published +methods. The paper then addresses the question of learning diagnostic policies +from examples. When the probabilities of diseases and test results are computed +from training data, there is a great danger of overfitting. To reduce +overfitting, regularizers are integrated into the search algorithms. Finally, +the paper compares the proposed methods on five benchmark diagnostic data sets. +The studies show that in most cases the systematic search methods produce +better diagnostic policies than the greedy methods. In addition, the studies +show that for training sets of realistic size, the systematic search algorithms +are practical on todays desktop computers. +","Integrating Learning from Examples into the Search for Diagnostic + Policies" +" Variable elimination is a general technique for constraint processing. It is +often discarded because of its high space complexity. However, it can be +extremely useful when combined with other techniques. In this paper we study +the applicability of variable elimination to the challenging problem of finding +still-lifes. We illustrate several alternatives: variable elimination as a +stand-alone algorithm, interleaved with search, and as a source of good quality +lower bounds. We show that these techniques are the best known option both +theoretically and empirically. In our experiments we have been able to solve +the n=20 instance, which is far beyond reach with alternative approaches. +","On the Practical use of Variable Elimination in Constraint Optimization + Problems: 'Still-life' as a Case Study" +" This is the second of three planned papers describing ZAP, a satisfiability +engine that substantially generalizes existing tools while retaining the +performance characteristics of modern high performance solvers. The fundamental +idea underlying ZAP is that many problems passed to such engines contain rich +internal structure that is obscured by the Boolean representation used; our +goal is to define a representation in which this structure is apparent and can +easily be exploited to improve computational performance. This paper presents +the theoretical basis for the ideas underlying ZAP, arguing that existing ideas +in this area exploit a single, recurring structure in that multiple database +axioms can be obtained by operating on a single axiom using a subgroup of the +group of permutations on the literals in the problem. We argue that the group +structure precisely captures the general structure at which earlier approaches +hinted, and give numerous examples of its use. We go on to extend the +Davis-Putnam-Logemann-Loveland inference procedure to this broader setting, and +show that earlier computational improvements are either subsumed or left intact +by the new method. The third paper in this series discusses ZAPs implementation +and presents experimental performance results. +",Generalizing Boolean Satisfiability II: Theory +" Stochastic processes that involve the creation of objects and relations over +time are widespread, but relatively poorly studied. For example, accurate fault +diagnosis in factory assembly processes requires inferring the probabilities of +erroneous assembly operations, but doing this efficiently and accurately is +difficult. Modeled as dynamic Bayesian networks, these processes have discrete +variables with very large domains and extremely high dimensionality. In this +paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an +extension of dynamic Bayesian networks (DBNs) to first-order logic. RDBNs are a +generalization of dynamic probabilistic relational models (DPRMs), which we had +proposed in our previous work to model dynamic uncertain domains. We first +extend the Rao-Blackwellised particle filtering described in our earlier work +to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in +RDBNs and propose two new forms of particle filtering. The first one uses +abstraction hierarchies over the predicates to smooth the particle filters +estimates. The second employs kernel density estimation with a kernel function +specifically designed for relational domains. Experiments show these two +methods greatly outperform standard particle filtering on the task of assembly +plan execution monitoring. +",Relational Dynamic Bayesian Networks +" We present a uniform non-monotonic solution to the problems of reasoning +about action on the basis of an argumentation-theoretic approach. Our theory is +provably correct relative to a sensible minimisation policy introduced on top +of a temporal propositional logic. Sophisticated problem domains can be +formalised in our framework. As much attention of researchers in the field has +been paid to the traditional and basic problems in reasoning about actions such +as the frame, the qualification and the ramification problems, approaches to +these problems within our formalisation lie at heart of the expositions +presented in this paper. +",Reasoning about Action: An Argumentation - Theoretic Approach +" In this paper we present a new approach to modeling finite set domain +constraint problems using Reduced Ordered Binary Decision Diagrams (ROBDDs). We +show that it is possible to construct an efficient set domain propagator which +compactly represents many set domains and set constraints using ROBDDs. We +demonstrate that the ROBDD-based approach provides unprecedented flexibility in +modeling constraint satisfaction problems, leading to performance improvements. +We also show that the ROBDD-based modeling approach can be extended to the +modeling of integer and multiset constraint problems in a straightforward +manner. Since domain propagation is not always practical, we also show how to +incorporate less strict consistency notions into the ROBDD framework, such as +set bounds, cardinality bounds and lexicographic bounds consistency. Finally, +we present experimental results that demonstrate the ROBDD-based solver +performs better than various more conventional constraint solvers on several +standard set constraint problems. +",Solving Set Constraint Satisfaction Problems using ROBDDs +" We present a novel approach to the automatic acquisition of taxonomies or +concept hierarchies from a text corpus. The approach is based on Formal Concept +Analysis (FCA), a method mainly used for the analysis of data, i.e. for +investigating and processing explicitly given information. We follow Harris +distributional hypothesis and model the context of a certain term as a vector +representing syntactic dependencies which are automatically acquired from the +text corpus with a linguistic parser. On the basis of this context information, +FCA produces a lattice that we convert into a special kind of partial order +constituting a concept hierarchy. The approach is evaluated by comparing the +resulting concept hierarchies with hand-crafted taxonomies for two domains: +tourism and finance. We also directly compare our approach with hierarchical +agglomerative clustering as well as with Bi-Section-KMeans as an instance of a +divisive clustering algorithm. Furthermore, we investigate the impact of using +different measures weighting the contribution of each attribute as well as of +applying a particular smoothing technique to cope with data sparseness. +","Learning Concept Hierarchies from Text Corpora using Formal Concept + Analysis" +" This is the third of three papers describing ZAP, a satisfiability engine +that substantially generalizes existing tools while retaining the performance +characteristics of modern high-performance solvers. The fundamental idea +underlying ZAP is that many problems passed to such engines contain rich +internal structure that is obscured by the Boolean representation used; our +goal has been to define a representation in which this structure is apparent +and can be exploited to improve computational performance. The first paper +surveyed existing work that (knowingly or not) exploited problem structure to +improve the performance of satisfiability engines, and the second paper showed +that this structure could be understood in terms of groups of permutations +acting on individual clauses in any particular Boolean theory. We conclude the +series by discussing the techniques needed to implement our ideas, and by +reporting on their performance on a variety of problem instances. +",Generalizing Boolean Satisfiability III: Implementation +" When dealing with incomplete data in statistical learning, or incomplete +observations in probabilistic inference, one needs to distinguish the fact that +a certain event is observed from the fact that the observed event has happened. +Since the modeling and computational complexities entailed by maintaining this +proper distinction are often prohibitive, one asks for conditions under which +it can be safely ignored. Such conditions are given by the missing at random +(mar) and coarsened at random (car) assumptions. In this paper we provide an +in-depth analysis of several questions relating to mar/car assumptions. Main +purpose of our study is to provide criteria by which one may evaluate whether a +car assumption is reasonable for a particular data collecting or observational +process. This question is complicated by the fact that several distinct +versions of mar/car assumptions exist. We therefore first provide an overview +over these different versions, in which we highlight the distinction between +distributional and coarsening variable induced versions. We show that +distributional versions are less restrictive and sufficient for most +applications. We then address from two different perspectives the question of +when the mar/car assumption is warranted. First we provide a static analysis +that characterizes the admissibility of the car assumption in terms of the +support structure of the joint probability distribution of complete data and +incomplete observations. Here we obtain an equivalence characterization that +improves and extends a recent result by Grunwald and Halpern. We then turn to a +procedural analysis that characterizes the admissibility of the car assumption +in terms of procedural models for the actual data (or observation) generating +process. The main result of this analysis is that the stronger coarsened +completely at random (ccar) condition is arguably the most reasonable +assumption, as it alone corresponds to data coarsening procedures that satisfy +a natural robustness property. +",Ignorability in Statistical and Probabilistic Inference +" Partially observable Markov decision processes (POMDPs) form an attractive +and principled framework for agent planning under uncertainty. Point-based +approximate techniques for POMDPs compute a policy based on a finite set of +points collected in advance from the agents belief space. We present a +randomized point-based value iteration algorithm called Perseus. The algorithm +performs approximate value backup stages, ensuring that in each backup stage +the value of each point in the belief set is improved; the key observation is +that a single backup may improve the value of many belief points. Contrary to +other point-based methods, Perseus backs up only a (randomly selected) subset +of points in the belief set, sufficient for improving the value of each belief +point in the set. We show how the same idea can be extended to dealing with +continuous action spaces. Experimental results show the potential of Perseus in +large scale POMDP problems. +",Perseus: Randomized Point-based Value Iteration for POMDPs +" Logical hidden Markov models (LOHMMs) upgrade traditional hidden Markov +models to deal with sequences of structured symbols in the form of logical +atoms, rather than flat characters. + This note formally introduces LOHMMs and presents solutions to the three +central inference problems for LOHMMs: evaluation, most likely hidden state +sequence and parameter estimation. The resulting representation and algorithms +are experimentally evaluated on problems from the domain of bioinformatics. +",Logical Hidden Markov Models +" We describe the version of the GPT planner used in the probabilistic track of +the 4th International Planning Competition (IPC-4). This version, called mGPT, +solves Markov Decision Processes specified in the PPDDL language by extracting +and using different classes of lower bounds along with various heuristic-search +algorithms. The lower bounds are extracted from deterministic relaxations where +the alternative probabilistic effects of an action are mapped into different, +independent, deterministic actions. The heuristic-search algorithms use these +lower bounds for focusing the updates and delivering a consistent value +function over all states reachable from the initial state and the greedy +policy. +",mGPT: A Probabilistic Planner Based on Heuristic Search +" Despite recent progress in AI planning, many benchmarks remain challenging +for current planners. In many domains, the performance of a planner can greatly +be improved by discovering and exploiting information about the domain +structure that is not explicitly encoded in the initial PDDL formulation. In +this paper we present and compare two automated methods that learn relevant +information from previous experience in a domain and use it to solve new +problem instances. Our methods share a common four-step strategy. First, a +domain is analyzed and structural information is extracted, then +macro-operators are generated based on the previously discovered structure. A +filtering and ranking procedure selects the most useful macro-operators. +Finally, the selected macros are used to speed up future searches. We have +successfully used such an approach in the fourth international planning +competition IPC-4. Our system, Macro-FF, extends Hoffmanns state-of-the-art +planner FF 2.3 with support for two kinds of macro-operators, and with +engineering enhancements. We demonstrate the effectiveness of our ideas on +benchmarks from international planning competitions. Our results indicate a +large reduction in search effort in those complex domains where structural +information can be inferred. +","Macro-FF: Improving AI Planning with Automatically Learned + Macro-Operators" +" The Optiplan planning system is the first integer programming-based planner +that successfully participated in the international planning competition. This +engineering note describes the architecture of Optiplan and provides the +integer programming formulation that enabled it to perform reasonably well in +the competition. We also touch upon some recent developments that make integer +programming encodings significantly more competitive. +",Optiplan: Unifying IP-based and Graph-based Planning +" We study an approach to policy selection for large relational Markov Decision +Processes (MDPs). We consider a variant of approximate policy iteration (API) +that replaces the usual value-function learning step with a learning step in +policy space. This is advantageous in domains where good policies are easier to +represent and learn than the corresponding value functions, which is often the +case for the relational MDPs we are interested in. In order to apply API to +such problems, we introduce a relational policy language and corresponding +learner. In addition, we introduce a new bootstrapping routine for goal-based +planning domains, based on random walks. Such bootstrapping is necessary for +many large relational MDPs, where reward is extremely sparse, as API is +ineffective in such domains when initialized with an uninformed policy. Our +experiments show that the resulting system is able to find good policies for a +number of classical planning domains and their stochastic variants by solving +them as extremely large relational MDPs. The experiments also point to some +limitations of our approach, suggesting future work. +","Approximate Policy Iteration with a Policy Language Bias: Solving + Relational Markov Decision Processes" +" Tabu search is one of the most effective heuristics for locating high-quality +solutions to a diverse array of NP-hard combinatorial optimization problems. +Despite the widespread success of tabu search, researchers have a poor +understanding of many key theoretical aspects of this algorithm, including +models of the high-level run-time dynamics and identification of those search +space features that influence problem difficulty. We consider these questions +in the context of the job-shop scheduling problem (JSP), a domain where tabu +search algorithms have been shown to be remarkably effective. Previously, we +demonstrated that the mean distance between random local optima and the nearest +optimal solution is highly correlated with problem difficulty for a well-known +tabu search algorithm for the JSP introduced by Taillard. In this paper, we +discuss various shortcomings of this measure and develop a new model of problem +difficulty that corrects these deficiencies. We show that Taillards algorithm +can be modeled with high fidelity as a simple variant of a straightforward +random walk. The random walk model accounts for nearly all of the variability +in the cost required to locate both optimal and sub-optimal solutions to random +JSPs, and provides an explanation for differences in the difficulty of random +versus structured JSPs. Finally, we discuss and empirically substantiate two +novel predictions regarding tabu search algorithm behavior. First, the method +for constructing the initial solution is highly unlikely to impact the +performance of tabu search. Second, tabu tenure should be selected to be as +small as possible while simultaneously avoiding search stagnation; values +larger than necessary lead to significant degradations in performance. +","Linking Search Space Structure, Run-Time Dynamics, and Problem + Difficulty: A Step Toward Demystifying Tabu Search" +" Code optimization and high level synthesis can be posed as constraint +satisfaction and optimization problems, such as graph coloring used in register +allocation. Graph coloring is also used to model more traditional CSPs relevant +to AI, such as planning, time-tabling and scheduling. Provably optimal +solutions may be desirable for commercial and defense applications. +Additionally, for applications such as register allocation and code +optimization, naturally-occurring instances of graph coloring are often small +and can be solved optimally. A recent wave of improvements in algorithms for +Boolean satisfiability (SAT) and 0-1 Integer Linear Programming (ILP) suggests +generic problem-reduction methods, rather than problem-specific heuristics, +because (1) heuristics may be upset by new constraints, (2) heuristics tend to +ignore structure, and (3) many relevant problems are provably inapproximable. + Problem reductions often lead to highly symmetric SAT instances, and +symmetries are known to slow down SAT solvers. In this work, we compare several +avenues for symmetry breaking, in particular when certain kinds of symmetry are +present in all generated instances. Our focus on reducing CSPs to SAT allows us +to leverage recent dramatic improvement in SAT solvers and automatically +benefit from future progress. We can use a variety of black-box SAT solvers +without modifying their source code because our symmetry-breaking techniques +are static, i.e., we detect symmetries and add symmetry breaking predicates +(SBPs) during pre-processing. + An important result of our work is that among the types of +instance-independent SBPs we studied and their combinations, the simplest and +least complete constructions are the most effective. Our experiments also +clearly indicate that instance-independent symmetries should mostly be +processed together with instance-specific symmetries rather than at the +specification level, contrary to what has been suggested in the literature. +",Breaking Instance-Independent Symmetries In Exact Graph Coloring +" A decision process in which rewards depend on history rather than merely on +the current state is called a decision process with non-Markovian rewards +(NMRDP). In decision-theoretic planning, where many desirable behaviours are +more naturally expressed as properties of execution sequences rather than as +properties of states, NMRDPs form a more natural model than the commonly +adopted fully Markovian decision process (MDP) model. While the more tractable +solution methods developed for MDPs do not directly apply in the presence of +non-Markovian rewards, a number of solution methods for NMRDPs have been +proposed in the literature. These all exploit a compact specification of the +non-Markovian reward function in temporal logic, to automatically translate the +NMRDP into an equivalent MDP which is solved using efficient MDP solution +methods. This paper presents NMRDPP (Non-Markovian Reward Decision Process +Planner), a software platform for the development and experimentation of +methods for decision-theoretic planning with non-Markovian rewards. The current +version of NMRDPP implements, under a single interface, a family of methods +based on existing as well as new approaches which we describe in detail. These +include dynamic programming, heuristic search, and structured methods. Using +NMRDPP, we compare the methods and identify certain problem features that +affect their performance. NMRDPPs treatment of non-Markovian rewards is +inspired by the treatment of domain-specific search control knowledge in the +TLPlan planner, which it incorporates as a special case. In the First +International Probabilistic Planning Competition, NMRDPP was able to compete +and perform well in both the domain-independent and hand-coded tracks, using +search control knowledge in the latter. +",Decision-Theoretic Planning with non-Markovian Rewards +" Boolean optimization finds a wide range of application domains, that +motivated a number of different organizations of Boolean optimizers since the +mid 90s. Some of the most successful approaches are based on iterative calls to +an NP oracle, using either linear search, binary search or the identification +of unsatisfiable sub-formulas. The increasing use of Boolean optimizers in +practical settings raises the question of confidence in computed results. For +example, the issue of confidence is paramount in safety critical settings. One +way of increasing the confidence of the results computed by Boolean optimizers +is to develop techniques for validating the results. Recent work studied the +validation of Boolean optimizers based on branch-and-bound search. This paper +complements existing work, and develops methods for validating Boolean +optimizers that are based on iterative calls to an NP oracle. This entails +implementing solutions for validating both satisfiable and unsatisfiable +answers from the NP oracle. The work described in this paper can be applied to +a wide range of Boolean optimizers, that find application in Pseudo-Boolean +Optimization and in Maximum Satisfiability. Preliminary experimental results +indicate that the impact of the proposed method in overall performance is +negligible. +",On Validating Boolean Optimizers +" The article presents a study on the biobjective inventory routing problem. +Contrary to most previous research, the problem is treated as a true +multi-objective optimization problem, with the goal of identifying +Pareto-optimal solutions. Due to the hardness of the problem at hand, a +reference point based optimization approach is presented and implemented into +an optimization and decision support system, which allows for the computation +of a true subset of the optimal outcomes. Experimental investigation involving +local search metaheuristics are conducted on benchmark data, and numerical +results are reported and analyzed. +","On the use of reference points for the biobjective Inventory Routing + Problem" +" Variable neighborhood search (VNS) is a metaheuristic for solving +optimization problems based on a simple principle: systematic changes of +neighborhoods within the search, both in the descent to local minima and in the +escape from the valleys which contain them. Designing these neighborhoods and +applying them in a meaningful fashion is not an easy task. Moreover, an +appropriate order in which they are applied must be determined. In this paper +we attempt to investigate this issue. Assume that we are given an optimization +problem that is intended to be solved by applying the VNS scheme, how many and +which types of neighborhoods should be investigated and what could be +appropriate selection criteria to apply these neighborhoods. More specifically, +does it pay to ""look ahead"" (see, e.g., in the context of VNS and GRASP) when +attempting to switch from one neighborhood to another? +",Neigborhood Selection in Variable Neighborhood Search +" In this paper we demonstrate that two common problems in Machine +Learning---imbalanced and overlapping data distributions---do not have +independent effects on the performance of SVM classifiers. This result is +notable since it shows that a model of either of these factors must account for +the presence of the other. Our study of the relationship between these problems +has lead to the discovery of a previously unreported form of ""covert"" +overfitting which is resilient to commonly used empirical regularization +techniques. We demonstrate the existance of this covert phenomenon through +several methods based around the parametric regularization of trained SVMs. Our +findings in this area suggest a possible approach to quantifying overlap in +real world data sets. +","A Characterization of the Combined Effects of Overlap and Imbalance on + the SVM Classifier" +" In the theory of belief functions, many measures of uncertainty have been +introduced. However, it is not always easy to understand what these measures +really try to represent. In this paper, we re-interpret some measures of +uncertainty in the theory of belief functions. We present some interests and +drawbacks of the existing measures. On these observations, we introduce a +measure of contradiction. Therefore, we present some degrees of non-specificity +and Bayesianity of a mass. We propose a degree of specificity based on the +distance between a mass and its most specific associated mass. We also show how +to use the degree of specificity to measure the specificity of a fusion rule. +Illustrations on simple examples are given. +","Contradiction measures and specificity degrees of basic belief + assignments" +" We discuss an attentional model for simultaneous object tracking and +recognition that is driven by gaze data. Motivated by theories of perception, +the model consists of two interacting pathways: identity and control, intended +to mirror the what and where pathways in neuroscience models. The identity +pathway models object appearance and performs classification using deep +(factored)-Restricted Boltzmann Machines. At each point in time the +observations consist of foveated images, with decaying resolution toward the +periphery of the gaze. The control pathway models the location, orientation, +scale and speed of the attended object. The posterior distribution of these +states is estimated with particle filtering. Deeper in the control pathway, we +encounter an attentional mechanism that learns to select gazes so as to +minimize tracking uncertainty. Unlike in our previous work, we introduce gaze +selection strategies which operate in the presence of partial information and +on a continuous action space. We show that a straightforward extension of the +existing approach to the partial information setting results in poor +performance, and we propose an alternative method based on modeling the reward +surface as a Gaussian Process. This approach gives good performance in the +presence of partial information and allows us to expand the action space from a +small, discrete set of fixation points to a continuous domain. +",Learning where to Attend with Deep Architectures for Image Tracking +" Several rules for social choice are examined from a unifying point of view +that looks at them as procedures for revising a system of degrees of belief in +accordance with certain specified logical constraints. Belief is here a social +attribute, its degrees being measured by the fraction of people who share a +given opinion. Different known rules and some new ones are obtained depending +on which particular constraints are assumed. These constraints allow to model +different notions of choiceness. In particular, we give a new method to deal +with approval-disapproval-preferential voting. +",Social choice rules driven by propositional logic +" We investigate training and using Gaussian kernel SVMs by approximating the +kernel with an explicit finite- dimensional polynomial feature representation +based on the Taylor expansion of the exponential. Although not as efficient as +the recently-proposed random Fourier features [Rahimi and Recht, 2007] in terms +of the number of features, we show how this polynomial representation can +provide a better approximation in terms of the computational cost involved. +This makes our ""Taylor features"" especially attractive for use on very large +data sets, in conjunction with online or stochastic training. +",Explicit Approximations of the Gaussian Kernel +" Today, available methods that assess AI systems are focused on using +empirical techniques to measure the performance of algorithms in some specific +tasks (e.g., playing chess, solving mazes or land a helicopter). However, these +methods are not appropriate if we want to evaluate the general intelligence of +AI and, even less, if we compare it with human intelligence. The ANYNT project +has designed a new method of evaluation that tries to assess AI systems using +well known computational notions and problems which are as general as possible. +This new method serves to assess general intelligence (which allows us to learn +how to solve any new kind of problem we face) and not only to evaluate +performance on a set of specific tasks. This method not only focuses on +measuring the intelligence of algorithms, but also to assess any intelligent +system (human beings, animals, AI, aliens?,...), and letting us to place their +results on the same scale and, therefore, to be able to compare them. This new +approach will allow us (in the future) to evaluate and compare any kind of +intelligent system known or even to build/find, be it artificial or biological. +This master thesis aims at ensuring that this new method provides consistent +results when evaluating AI algorithms, this is done through the design and +implementation of prototypes of universal intelligence tests and their +application to different intelligent systems (AI algorithms and humans beings). +From the study we analyze whether the results obtained by two different +intelligent systems are properly located on the same scale and we propose +changes and refinements to these prototypes in order to, in the future, being +able to achieve a truly universal intelligence test. +","Analysis of first prototype universal intelligence tests: evaluating and + comparing AI algorithms and humans" +" We provide an overview of the organization and results of the deterministic +part of the 4th International Planning Competition, i.e., of the part concerned +with evaluating systems doing deterministic planning. IPC-4 attracted even more +competing systems than its already large predecessors, and the competition +event was revised in several important respects. After giving an introduction +to the IPC, we briefly explain the main differences between the deterministic +part of IPC-4 and its predecessors. We then introduce formally the language +used, called PDDL2.2 that extends PDDL2.1 by derived predicates and timed +initial literals. We list the competing systems and overview the results of the +competition. The entire set of data is far too large to be presented in full. +We provide a detailed summary; the complete data is available in an online +appendix. We explain how we awarded the competition prizes. +",The Deterministic Part of IPC-4: An Overview +" PDDL2.1 was designed to push the envelope of what planning algorithms can do, +and it has succeeded. It adds two important features: durative actions,which +take time (and may have continuous effects); and objective functions for +measuring the quality of plans. The concept of durative actions is flawed; and +the treatment of their semantics reveals too strong an attachment to the way +many contemporary planners work. Future PDDL innovators should focus on +producing a clean semantics for additions to the language, and let planner +implementers worry about coupling their algorithms to problems expressed in the +latest version of the language. +",PDDL2.1 - The Art of the Possible? Commentary on Fox and Long +" The addition of durative actions to PDDL2.1 sparked some controversy. Fox and +Long argued that actions should be considered as instantaneous, but can start +and stop processes. Ultimately, a limited notion of durative actions was +incorporated into the language. I argue that this notion is still impoverished, +and that the underlying philosophical position of regarding durative actions as +being a shorthand for a start action, process, and stop action ignores the +realities of modelling and execution for complex systems. +",The Case for Durative Actions: A Commentary on PDDL2.1 +" We present a partial-order, conformant, probabilistic planner, Probapop which +competed in the blind track of the Probabilistic Planning Competition in IPC-4. +We explain how we adapt distance based heuristics for use with probabilistic +domains. Probapop also incorporates heuristics based on probability of success. +We explain the successes and difficulties encountered during the design and +implementation of Probapop. +",Engineering a Conformant Probabilistic Planner +" Between 1998 and 2004, the planning community has seen vast progress in terms +of the sizes of benchmark examples that domain-independent planners can tackle +successfully. The key technique behind this progress is the use of heuristic +functions based on relaxing the planning task at hand, where the relaxation is +to assume that all delete lists are empty. The unprecedented success of such +methods, in many commonly used benchmark examples, calls for an understanding +of what classes of domains these methods are well suited for. In the +investigation at hand, we derive a formal background to such an understanding. +We perform a case study covering a range of 30 commonly used STRIPS and ADL +benchmark domains, including all examples used in the first four international +planning competitions. We *prove* connections between domain structure and +local search topology -- heuristic cost surface properties -- under an +idealized version of the heuristic functions used in modern planners. The +idealized heuristic function is called h^+, and differs from the practically +used functions in that it returns the length of an *optimal* relaxed plan, +which is NP-hard to compute. We identify several key characteristics of the +topology under h^+, concerning the existence/non-existence of unrecognized dead +ends, as well as the existence/non-existence of constant upper bounds on the +difficulty of escaping local minima and benches. These distinctions divide the +(set of all) planning domains into a taxonomy of classes of varying h^+ +topology. As it turns out, many of the 30 investigated domains lie in classes +with a relatively easy topology. Most particularly, 12 of the domains lie in +classes where FFs search algorithm, provided with h^+, is a polynomial solving +mechanism. We also present results relating h^+ to its approximation as +implemented in FF. The behavior regarding dead ends is provably the same. We +summarize the results of an empirical investigation showing that, in many +domains, the topological qualities of h^+ are largely inherited by the +approximation. The overall investigation gives a rare example of a successful +analysis of the connections between typical-case problem structure, and search +performance. The theoretical investigation also gives hints on how the +topological phenomena might be automatically recognizable by domain analysis +techniques. We outline some preliminary steps we made into that direction. +","Where 'Ignoring Delete Lists' Works: Local Search Topology in Planning + Benchmarks" +" A non-binary Constraint Satisfaction Problem (CSP) can be solved directly +using extended versions of binary techniques. Alternatively, the non-binary +problem can be translated into an equivalent binary one. In this case, it is +generally accepted that the translated problem can be solved by applying +well-established techniques for binary CSPs. In this paper we evaluate the +applicability of the latter approach. We demonstrate that the use of standard +techniques for binary CSPs in the encodings of non-binary problems is +problematic and results in models that are very rarely competitive with the +non-binary representation. To overcome this, we propose specialized arc +consistency and search algorithms for binary encodings, and we evaluate them +theoretically and empirically. We consider three binary representations; the +hidden variable encoding, the dual encoding, and the double encoding. +Theoretical and empirical results show that, for certain classes of non-binary +constraints, binary encodings are a competitive option, and in many cases, a +better one than the non-binary representation. +","Binary Encodings of Non-binary Constraint Satisfaction Problems: + Algorithms and Experimental Results" +" In a peer-to-peer inference system, each peer can reason locally but can also +solicit some of its acquaintances, which are peers sharing part of its +vocabulary. In this paper, we consider peer-to-peer inference systems in which +the local theory of each peer is a set of propositional clauses defined upon a +local vocabulary. An important characteristic of peer-to-peer inference systems +is that the global theory (the union of all peer theories) is not known (as +opposed to partition-based reasoning systems). The main contribution of this +paper is to provide the first consequence finding algorithm in a peer-to-peer +setting: DeCA. It is anytime and computes consequences gradually from the +solicited peer to peers that are more and more distant. We exhibit a sufficient +condition on the acquaintance graph of the peer-to-peer inference system for +guaranteeing the completeness of this algorithm. Another important contribution +is to apply this general distributed reasoning setting to the setting of the +Semantic Web through the Somewhere semantic peer-to-peer data management +system. The last contribution of this paper is to provide an experimental +analysis of the scalability of the peer-to-peer infrastructure that we propose, +on large networks of 1000 peers. +","Distributed Reasoning in a Peer-to-Peer Setting: Application to the + Semantic Web" +" In this paper, we introduce DLS-MC, a new stochastic local search algorithm +for the maximum clique problem. DLS-MC alternates between phases of iterative +improvement, during which suitable vertices are added to the current clique, +and plateau search, during which vertices of the current clique are swapped +with vertices not contained in the current clique. The selection of vertices is +solely based on vertex penalties that are dynamically adjusted during the +search, and a perturbation mechanism is used to overcome search stagnation. The +behaviour of DLS-MC is controlled by a single parameter, penalty delay, which +controls the frequency at which vertex penalties are reduced. We show +empirically that DLS-MC achieves substantial performance improvements over +state-of-the-art algorithms for the maximum clique problem over a large range +of the commonly used DIMACS benchmark instances. +",Dynamic Local Search for the Maximum Clique Problem +" Open distributed multi-agent systems are gaining interest in the academic +community and in industry. In such open settings, agents are often coordinated +using standardized agent conversation protocols. The representation of such +protocols (for analysis, validation, monitoring, etc) is an important aspect of +multi-agent applications. Recently, Petri nets have been shown to be an +interesting approach to such representation, and radically different approaches +using Petri nets have been proposed. However, their relative strengths and +weaknesses have not been examined. Moreover, their scalability and suitability +for different tasks have not been addressed. This paper addresses both these +challenges. First, we analyze existing Petri net representations in terms of +their scalability and appropriateness for overhearing, an important task in +monitoring open multi-agent systems. Then, building on the insights gained, we +introduce a novel representation using Colored Petri nets that explicitly +represent legal joint conversation states and messages. This representation +approach offers significant improvements in scalability and is particularly +suitable for overhearing. Furthermore, we show that this new representation +offers a comprehensive coverage of all conversation features of FIPA +conversation standards. We also present a procedure for transforming AUML +conversation protocol diagrams (a standard human-readable representation), to +our Colored Petri net representation. +",Representing Conversations for Scalable Overhearing +" The hm admissible heuristics for (sequential and temporal) regression +planning are defined by a parameterized relaxation of the optimal cost function +in the regression search space, where the parameter m offers a trade-off +between the accuracy and computational cost of theheuristic. Existing methods +for computing the hm heuristic require time exponential in m, limiting them to +small values (m andlt= 2). The hm heuristic can also be viewed as the optimal +cost function in a relaxation of the search space: this paper presents relaxed +search, a method for computing this function partially by searching in the +relaxed space. The relaxed search method, because it computes hm only +partially, is computationally cheaper and therefore usable for higher values of +m. The (complete) hm heuristic is combined with partial hm heuristics, for m = +3,..., computed by relaxed search, resulting in a more accurate heuristic. + This use of the relaxed search method to improve on the hm heuristic is +evaluated by comparing two optimal temporal planners: TP4, which does not use +it, and HSP*a, which uses it but is otherwise identical to TP4. The comparison +is made on the domains used in the 2004 International Planning Competition, in +which both planners participated. Relaxed search is found to be cost effective +in some of these domains, but not all. Analysis reveals a characterization of +the domains in which relaxed search can be expected to be cost effective, in +terms of two measures on the original and relaxed search spaces. In the domains +where relaxed search is cost effective, expanding small states is +computationally cheaper than expanding large states and small states tend to +have small successor states. +","Improving Heuristics Through Relaxed Search - An Analysis of TP4 and + HSP*a in the 2004 Planning Competition" +" Recently, a variety of constraint programming and Boolean satisfiability +approaches to scheduling problems have been introduced. They have in common the +use of relatively simple propagation mechanisms and an adaptive way to focus on +the most constrained part of the problem. In some cases, these methods compare +favorably to more classical constraint programming methods relying on +propagation algorithms for global unary or cumulative resource constraints and +dedicated search heuristics. In particular, we described an approach that +combines restarting, with a generic adaptive heuristic and solution guided +branching on a simple model based on a decomposition of disjunctive +constraints. In this paper, we introduce an adaptation of this technique for an +important subclass of job shop scheduling problems (JSPs), where the objective +function involves minimization of earliness/tardiness costs. We further show +that our technique can be improved by adding domain specific information for +one variant of the JSP (involving time lag constraints). In particular we +introduce a dedicated greedy heuristic, and an improved model for the case +where the maximal time lag is 0 (also referred to as no-wait JSPs). +",Models and Strategies for Variants of the Job Shop Scheduling Problem +" The Universal Intelligence Measure is a recently proposed formal definition +of intelligence. It is mathematically specified, extremely general, and +captures the essence of many informal definitions of intelligence. It is based +on Hutter's Universal Artificial Intelligence theory, an extension of Ray +Solomonoff's pioneering work on universal induction. Since the Universal +Intelligence Measure is only asymptotically computable, building a practical +intelligence test from it is not straightforward. This paper studies the +practical issues involved in developing a real-world UIM-based performance +metric. Based on our investigation, we develop a prototype implementation which +we use to evaluate a number of different artificial agents. +",An Approximation of the Universal Intelligence Measure +" Multiple sequence alignment (MSA) is a ubiquitous problem in computational +biology. Although it is NP-hard to find an optimal solution for an arbitrary +number of sequences, due to the importance of this problem researchers are +trying to push the limits of exact algorithms further. Since MSA can be cast as +a classical path finding problem, it is attracting a growing number of AI +researchers interested in heuristic search algorithms as a challenge with +actual practical relevance. In this paper, we first review two previous, +complementary lines of research. Based on Hirschbergs algorithm, Dynamic +Programming needs O(kN^(k-1)) space to store both the search frontier and the +nodes needed to reconstruct the solution path, for k sequences of length N. +Best first search, on the other hand, has the advantage of bounding the search +space that has to be explored using a heuristic. However, it is necessary to +maintain all explored nodes up to the final solution in order to prevent the +search from re-expanding them at higher cost. Earlier approaches to reduce the +Closed list are either incompatible with pruning methods for the Open list, or +must retain at least the boundary of the Closed list. In this article, we +present an algorithm that attempts at combining the respective advantages; like +A* it uses a heuristic for pruning the search space, but reduces both the +maximum Open and Closed size to O(kN^(k-1)), as in Dynamic Programming. The +underlying idea is to conduct a series of searches with successively increasing +upper bounds, but using the DP ordering as the key for the Open priority queue. +With a suitable choice of thresholds, in practice, a running time below four +times that of A* can be expected. In our experiments we show that our algorithm +outperforms one of the currently most successful algorithms for optimal +multiple sequence alignments, Partial Expansion A*, both in time and memory. +Moreover, we apply a refined heuristic based on optimal alignments not only of +pairs of sequences, but of larger subsets. This idea is not new; however, to +make it practically relevant we show that it is equally important to bound the +heuristic computation appropriately, or the overhead can obliterate any +possible gain. Furthermore, we discuss a number of improvements in time and +space efficiency with regard to practical implementations. Our algorithm, used +in conjunction with higher-dimensional heuristics, is able to calculate for the +first time the optimal alignment for almost all of the problems in Reference 1 +of the benchmark database BAliBASE. +",An Improved Search Algorithm for Optimal Multiple-Sequence Alignment +" This article develops Probabilistic Hybrid Action Models (PHAMs), a realistic +causal model for predicting the behavior generated by modern percept-driven +robot plans. PHAMs represent aspects of robot behavior that cannot be +represented by most action models used in AI planning: the temporal structure +of continuous control processes, their non-deterministic effects, several modes +of their interferences, and the achievement of triggering conditions in +closed-loop robot plans. + The main contributions of this article are: (1) PHAMs, a model of concurrent +percept-driven behavior, its formalization, and proofs that the model generates +probably, qualitatively accurate predictions; and (2) a resource-efficient +inference method for PHAMs based on sampling projections from probabilistic +action models and state descriptions. We show how PHAMs can be applied to +planning the course of action of an autonomous robot office courier based on +analytical and experimental results. +","Probabilistic Hybrid Action Models for Predicting Concurrent + Percept-driven Robot Behavior" +" We present a novel framework for integrating prior knowledge into +discriminative classifiers. Our framework allows discriminative classifiers +such as Support Vector Machines (SVMs) to utilize prior knowledge specified in +the generative setting. The dual objective of fitting the data and respecting +prior knowledge is formulated as a bilevel program, which is solved +(approximately) via iterative application of second-order cone programming. To +test our approach, we consider the problem of using WordNet (a semantic +database of English language) to improve low-sample classification accuracy of +newsgroup categorization. WordNet is viewed as an approximate, but readily +available source of background knowledge, and our framework is capable of +utilizing it in a flexible way. +",Generative Prior Knowledge for Discriminative Classification +" Fast Downward is a classical planning system based on heuristic search. It +can deal with general deterministic planning problems encoded in the +propositional fragment of PDDL2.2, including advanced features like ADL +conditions and effects and derived predicates (axioms). Like other well-known +planners such as HSP and FF, Fast Downward is a progression planner, searching +the space of world states of a planning task in the forward direction. However, +unlike other PDDL planning systems, Fast Downward does not use the +propositional PDDL representation of a planning task directly. Instead, the +input is first translated into an alternative representation called +multi-valued planning tasks, which makes many of the implicit constraints of a +propositional planning task explicit. Exploiting this alternative +representation, Fast Downward uses hierarchical decompositions of planning +tasks for computing its heuristic function, called the causal graph heuristic, +which is very different from traditional HSP-like heuristics based on ignoring +negative interactions of operators. + In this article, we give a full account of Fast Downwards approach to solving +multi-valued planning tasks. We extend our earlier discussion of the causal +graph heuristic to tasks involving axioms and conditional effects and present +some novel techniques for search control that are used within Fast Downwards +best-first search algorithm: preferred operators transfer the idea of helpful +actions from local search to global best-first search, deferred evaluation of +heuristic functions mitigates the negative effect of large branching factors on +search performance, and multi-heuristic best-first search combines several +heuristic evaluation functions within a single search algorithm in an +orthogonal way. We also describe efficient data structures for fast state +expansion (successor generators and axiom evaluators) and present a new +non-heuristic search algorithm called focused iterative-broadening search, +which utilizes the information encoded in causal graphs in a novel way. + Fast Downward has proven remarkably successful: It won the ""classical (i.e., +propositional, non-optimising) track of the 4th International Planning +Competition at ICAPS 2004, following in the footsteps of planners such as FF +and LPG. Our experiments show that it also performs very well on the benchmarks +of the earlier planning competitions and provide some insights about the +usefulness of the new search enhancements. +",The Fast Downward Planning System +" Distributed Constraint Satisfaction (DCSP) has long been considered an +important problem in multi-agent systems research. This is because many +real-world problems can be represented as constraint satisfaction and these +problems often present themselves in a distributed form. In this article, we +present a new complete, distributed algorithm called Asynchronous Partial +Overlay (APO) for solving DCSPs that is based on a cooperative mediation +process. The primary ideas behind this algorithm are that agents, when acting +as a mediator, centralize small, relevant portions of the DCSP, that these +centralized subproblems overlap, and that agents increase the size of their +subproblems along critical paths within the DCSP as the problem solving +unfolds. We present empirical evidence that shows that APO outperforms other +known, complete DCSP techniques. +","Asynchronous Partial Overlay: A New Algorithm for Solving Distributed + Constraint Satisfaction Problems" +" As partial justification of their framework for iterated belief revision +Darwiche and Pearl convincingly argued against Boutiliers natural revision and +provided a prototypical revision operator that fits into their scheme. We show +that the Darwiche-Pearl arguments lead naturally to the acceptance of a smaller +class of operators which we refer to as admissible. Admissible revision ensures +that the penultimate input is not ignored completely, thereby eliminating +natural revision, but includes the Darwiche-Pearl operator, Nayaks +lexicographic revision operator, and a newly introduced operator called +restrained revision. We demonstrate that restrained revision is the most +conservative of admissible revision operators, effecting as few changes as +possible, while lexicographic revision is the least conservative, and point out +that restrained revision can also be viewed as a composite operator, consisting +of natural revision preceded by an application of a ""backwards revision"" +operator previously studied by Papini. Finally, we propose the establishment of +a principled approach for choosing an appropriate revision operator in +different contexts and discuss future work. +",Admissible and Restrained Revision +" In recent years, CP-nets have emerged as a useful tool for supporting +preference elicitation, reasoning, and representation. CP-nets capture and +support reasoning with qualitative conditional preference statements, +statements that are relatively natural for users to express. In this paper, we +extend the CP-nets formalism to handle another class of very natural +qualitative statements one often uses in expressing preferences in daily life - +statements of relative importance of attributes. The resulting formalism, +TCP-nets, maintains the spirit of CP-nets, in that it remains focused on using +only simple and natural preference statements, uses the ceteris paribus +semantics, and utilizes a graphical representation of this information to +reason about its consistency and to perform, possibly constrained, optimization +using it. The extra expressiveness it provides allows us to better model +tradeoffs users would like to make, more faithfully representing their +preferences. +",On Graphical Modeling of Preference and Importance +" Linear Temporal Logic (LTL) is widely used for defining conditions on the +execution paths of dynamic systems. In the case of dynamic systems that allow +for nondeterministic evolutions, one has to specify, along with an LTL formula +f, which are the paths that are required to satisfy the formula. Two extreme +cases are the universal interpretation A.f, which requires that the formula be +satisfied for all execution paths, and the existential interpretation E.f, +which requires that the formula be satisfied for some execution path. + When LTL is applied to the definition of goals in planning problems on +nondeterministic domains, these two extreme cases are too restrictive. It is +often impossible to develop plans that achieve the goal in all the +nondeterministic evolutions of a system, and it is too weak to require that the +goal is satisfied by some execution. + In this paper we explore alternative interpretations of an LTL formula that +are between these extreme cases. We define a new language that permits an +arbitrary combination of the A and E quantifiers, thus allowing, for instance, +to require that each finite execution can be extended to an execution +satisfying an LTL formula (AE.f), or that there is some finite execution whose +extensions all satisfy an LTL formula (EA.f). We show that only eight of these +combinations of path quantifiers are relevant, corresponding to an alternation +of the quantifiers of length one (A and E), two (AE and EA), three (AEA and +EAE), and infinity ((AE)* and (EA)*). We also present a planning algorithm for +the new language that is based on an automata-theoretic approach, and study its +complexity. +","The Planning Spectrum - One, Two, Three, Infinity" +" A delta-model is a satisfying assignment of a Boolean formula for which any +small alteration, such as a single bit flip, can be repaired by flips to some +small number of other bits, yielding a new satisfying assignment. These +satisfying assignments represent robust solutions to optimization problems +(e.g., scheduling) where it is possible to recover from unforeseen events +(e.g., a resource becoming unavailable). The concept of delta-models was +introduced by Ginsberg, Parkes and Roy (AAAI 1998), where it was proved that +finding delta-models for general Boolean formulas is NP-complete. In this +paper, we extend that result by studying the complexity of finding delta-models +for classes of Boolean formulas which are known to have polynomial time +satisfiability solvers. In particular, we examine 2-SAT, Horn-SAT, Affine-SAT, +dual-Horn-SAT, 0-valid and 1-valid SAT. We see a wide variation in the +complexity of finding delta-models, e.g., while 2-SAT and Affine-SAT have +polynomial time tests for delta-models, testing whether a Horn-SAT formula has +one is NP-complete. +",Fault Tolerant Boolean Satisfiability +" Multimodal conversational interfaces provide a natural means for users to +communicate with computer systems through multiple modalities such as speech +and gesture. To build effective multimodal interfaces, automated interpretation +of user multimodal inputs is important. Inspired by the previous investigation +on cognitive status in multimodal human machine interaction, we have developed +a greedy algorithm for interpreting user referring expressions (i.e., +multimodal reference resolution). This algorithm incorporates the cognitive +principles of Conversational Implicature and Givenness Hierarchy and applies +constraints from various sources (e.g., temporal, semantic, and contextual) to +resolve references. Our empirical results have shown the advantage of this +algorithm in efficiently resolving a variety of user references. Because of its +simplicity and generality, this approach has the potential to improve the +robustness of multimodal input interpretation. +",Cognitive Principles in Robust Multimodal Interpretation +" This paper presents a new framework for anytime heuristic search where the +task is to achieve as many goals as possible within the allocated resources. We +show the inadequacy of traditional distance-estimation heuristics for tasks of +this type and present alternative heuristics that are more appropriate for +multiple-goal search. In particular, we introduce the marginal-utility +heuristic, which estimates the cost and the benefit of exploring a subtree +below a search node. We developed two methods for online learning of the +marginal-utility heuristic. One is based on local similarity of the partial +marginal utility of sibling nodes, and the other generalizes marginal-utility +over the state feature space. We apply our adaptive and non-adaptive +multiple-goal search algorithms to several problems, including focused +crawling, and show their superiority over existing methods. +",Multiple-Goal Heuristic Search +" We present a heuristic search algorithm for solving first-order Markov +Decision Processes (FOMDPs). Our approach combines first-order state +abstraction that avoids evaluating states individually, and heuristic search +that avoids evaluating all states. Firstly, in contrast to existing systems, +which start with propositionalizing the FOMDP and then perform state +abstraction on its propositionalized version we apply state abstraction +directly on the FOMDP avoiding propositionalization. This kind of abstraction +is referred to as first-order state abstraction. Secondly, guided by an +admissible heuristic, the search is restricted to those states that are +reachable from the initial state. We demonstrate the usefulness of the above +techniques for solving FOMDPs with a system, referred to as FluCaP (formerly, +FCPlanner), that entered the probabilistic track of the 2004 International +Planning Competition (IPC2004) and demonstrated an advantage over other +planners on the problems represented in first-order terms. +",FluCaP: A Heuristic Search Planner for First-Order MDPs +" Suppose we want to build a system that answers a natural language question by +representing its semantics as a logical form and computing the answer given a +structured database of facts. The core part of such a system is the semantic +parser that maps questions to logical forms. Semantic parsers are typically +trained from examples of questions annotated with their target logical forms, +but this type of annotation is expensive. + Our goal is to learn a semantic parser from question-answer pairs instead, +where the logical form is modeled as a latent variable. Motivated by this +challenging learning problem, we develop a new semantic formalism, +dependency-based compositional semantics (DCS), which has favorable linguistic, +statistical, and computational properties. We define a log-linear distribution +over DCS logical forms and estimate the parameters using a simple procedure +that alternates between beam search and numerical optimization. On two standard +semantic parsing benchmarks, our system outperforms all existing +state-of-the-art systems, despite using no annotated logical forms. +",Learning Dependency-Based Compositional Semantics +" It was recently proved that a sound and complete qualitative simulator does +not exist, that is, as long as the input-output vocabulary of the +state-of-the-art QSIM algorithm is used, there will always be input models +which cause any simulator with a coverage guarantee to make spurious +predictions in its output. In this paper, we examine whether a meaningfully +expressive restriction of this vocabulary is possible so that one can build a +simulator with both the soundness and completeness properties. We prove several +negative results: All sound qualitative simulators, employing subsets of the +QSIM representation which retain the operating region transition feature, and +support at least the addition and constancy constraints, are shown to be +inherently incomplete. Even when the simulations are restricted to run in a +single operating region, a constraint vocabulary containing just the addition, +constancy, derivative, and multiplication relations makes the construction of +sound and complete qualitative simulators impossible. +",Causes of Ineradicable Spurious Predictions in Qualitative Simulation +" We study properties of programs with monotone and convex constraints. We +extend to these formalisms concepts and results from normal logic programming. +They include the notions of strong and uniform equivalence with their +characterizations, tight programs and Fages Lemma, program completion and loop +formulas. Our results provide an abstract account of properties of some recent +extensions of logic programming with aggregates, especially the formalism of +lparse programs. They imply a method to compute stable models of lparse +programs by means of off-the-shelf solvers of pseudo-boolean constraints, which +is often much faster than the smodels system. +","Properties and Applications of Programs with Monotone and Convex + Constraints" +" We characterize the search landscape of random instances of the job shop +scheduling problem (JSP). Specifically, we investigate how the expected values +of (1) backbone size, (2) distance between near-optimal schedules, and (3) +makespan of random schedules vary as a function of the job to machine ratio +(N/M). For the limiting cases N/M approaches 0 and N/M approaches infinity we +provide analytical results, while for intermediate values of N/M we perform +experiments. We prove that as N/M approaches 0, backbone size approaches 100%, +while as N/M approaches infinity the backbone vanishes. In the process we show +that as N/M approaches 0 (resp. N/M approaches infinity), simple priority rules +almost surely generate an optimal schedule, providing theoretical evidence of +an ""easy-hard-easy"" pattern of typical-case instance difficulty in job shop +scheduling. We also draw connections between our theoretical results and the +""big valley"" picture of JSP landscapes. +","How the Landscape of Random Job Shop Scheduling Instances Depends on the + Ratio of Jobs to Machines" +" We consider interactive tools that help users search for their most preferred +item in a large collection of options. In particular, we examine +example-critiquing, a technique for enabling users to incrementally construct +preference models by critiquing example options that are presented to them. We +present novel techniques for improving the example-critiquing technology by +adding suggestions to its displayed options. Such suggestions are calculated +based on an analysis of users current preference model and their potential +hidden preferences. We evaluate the performance of our model-based suggestion +techniques with both synthetic and real users. Results show that such +suggestions are highly attractive to users and can stimulate them to express +more preferences to improve the chance of identifying their most preferred item +by up to 78%. +",Preference-based Search using Example-Critiquing with Suggestions +" The Partially Observable Markov Decision Process has long been recognized as +a rich framework for real-world planning and control problems, especially in +robotics. However exact solutions in this framework are typically +computationally intractable for all but the smallest problems. A well-known +technique for speeding up POMDP solving involves performing value backups at +specific belief points, rather than over the entire belief simplex. The +efficiency of this approach, however, depends greatly on the selection of +points. This paper presents a set of novel techniques for selecting informative +belief points which work well in practice. The point selection procedure is +combined with point-based value backups to form an effective anytime POMDP +algorithm called Point-Based Value Iteration (PBVI). The first aim of this +paper is to introduce this algorithm and present a theoretical analysis +justifying the choice of belief selection technique. The second aim of this +paper is to provide a thorough empirical comparison between PBVI and other +state-of-the-art POMDP methods, in particular the Perseus algorithm, in an +effort to highlight their similarities and differences. Evaluation is performed +using both standard POMDP domains and realistic robotic tasks. +",Anytime Point-Based Approximations for Large POMDPs +" Efficient representations and solutions for large decision problems with +continuous and discrete variables are among the most important challenges faced +by the designers of automated decision support systems. In this paper, we +describe a novel hybrid factored Markov decision process (MDP) model that +allows for a compact representation of these problems, and a new hybrid +approximate linear programming (HALP) framework that permits their efficient +solutions. The central idea of HALP is to approximate the optimal value +function by a linear combination of basis functions and optimize its weights by +linear programming. We analyze both theoretical and computational aspects of +this approach, and demonstrate its scale-up potential on several hybrid +optimization problems. +",Solving Factored MDPs with Hybrid State and Action Variables +" This paper introduces and analyzes a battery of inference models for the +problem of semantic role labeling: one based on constraint satisfaction, and +several strategies that model the inference as a meta-learning problem using +discriminative classifiers. These classifiers are developed with a rich set of +novel features that encode proposition and sentence-level information. To our +knowledge, this is the first work that: (a) performs a thorough analysis of +learning-based inference models for semantic role labeling, and (b) compares +several inference strategies in this context. We evaluate the proposed +inference strategies in the framework of the CoNLL-2005 shared task using only +automatically-generated syntactic information. The extensive experimental +evaluation and analysis indicates that all the proposed inference strategies +are successful -they all outperform the current best results reported in the +CoNLL-2005 evaluation exercise- but each of the proposed approaches has its +advantages and disadvantages. Several important traits of a state-of-the-art +SRL combination strategy emerge from this analysis: (i) individual models +should be combined at the granularity of candidate arguments rather than at the +granularity of complete solutions; (ii) the best combination strategy uses an +inference model based in learning; and (iii) the learning-based inference +benefits from max-margin classifiers and global feedback. +",Combination Strategies for Semantic Role Labeling +" In contrast to the existing approaches to bisimulation for fuzzy systems, we +introduce a behavioral distance to measure the behavioral similarity of states +in a nondeterministic fuzzy-transition system. This behavioral distance is +defined as the greatest fixed point of a suitable monotonic function and +provides a quantitative analogue of bisimilarity. The behavioral distance has +the important property that two states are at zero distance if and only if they +are bisimilar. Moreover, for any given threshold, we find that states with +behavioral distances bounded by the threshold are equivalent. In addition, we +show that two system combinators---parallel composition and product---are +non-expansive with respect to our behavioral distance, which makes +compositional verification possible. +",A Behavioral Distance for Fuzzy-Transition Systems +" In a field of research about general reasoning mechanisms, it is essential to +have appropriate benchmarks. Ideally, the benchmarks should reflect possible +applications of the developed technology. In AI Planning, researchers more and +more tend to draw their testing examples from the benchmark collections used in +the International Planning Competition (IPC). In the organization of (the +deterministic part of) the fourth IPC, IPC-4, the authors therefore invested +significant effort to create a useful set of benchmarks. They come from five +different (potential) real-world applications of planning: airport ground +traffic control, oil derivative transportation in pipeline networks, +model-checking safety properties, power supply restoration, and UMTS call +setup. Adapting and preparing such an application for use as a benchmark in the +IPC involves, at the time, inevitable (often drastic) simplifications, as well +as careful choice between, and engineering of, domain encodings. For the first +time in the IPC, we used compilations to formulate complex domain features in +simple languages such as STRIPS, rather than just dropping the more interesting +problem constraints in the simpler language subsets. The article explains and +discusses the five application domains and their adaptation to form the PDDL +test suites used in IPC-4. We summarize known theoretical results on structural +properties of the domains, regarding their computational complexity and +provable properties of their topology under the h+ function (an idealized +version of the relaxed plan heuristic). We present new (empirical) results +illuminating properties such as the quality of the most wide-spread heuristic +functions (planning graph, serial planning graph, and relaxed plan), the growth +of propositional representations over instance size, and the number of actions +available to achieve each fact; we discuss these data in conjunction with the +best results achieved by the different kinds of planners participating in +IPC-4. +","Engineering Benchmarks for Planning: the Domains Used in the + Deterministic Part of IPC-4" +" In this paper we present pddl+, a planning domain description language for +modelling mixed discrete-continuous planning domains. We describe the syntax +and modelling style of pddl+, showing that the language makes convenient the +modelling of complex time-dependent effects. We provide a formal semantics for +pddl+ by mapping planning instances into constructs of hybrid automata. Using +the syntax of HAs as our semantic model we construct a semantic mapping to +labelled transition systems to complete the formal interpretation of pddl+ +planning instances. An advantage of building a mapping from pddl+ to HA theory +is that it forms a bridge between the Planning and Real Time Systems research +communities. One consequence is that we can expect to make use of some of the +theoretical properties of HAs. For example, for a restricted class of HAs the +Reachability problem (which is equivalent to Plan Existence) is decidable. +pddl+ provides an alternative to the continuous durative action model of +pddl2.1, adding a more flexible and robust model of time-dependent behaviour. +",Modelling Mixed Discrete-Continuous Domains for Planning +" In this paper, we show that there is a close relation between consistency in +a constraint network and set intersection. A proof schema is provided as a +generic way to obtain consistency properties from properties on set +intersection. This approach not only simplifies the understanding of and +unifies many existing consistency results, but also directs the study of +consistency to that of set intersection properties in many situations, as +demonstrated by the results on the convexity and tightness of constraints in +this paper. Specifically, we identify a new class of tree convex constraints +where local consistency ensures global consistency. This generalizes row convex +constraints. Various consistency results are also obtained on constraint +networks where only some, in contrast to all in the existing work,constraints +are tight. +",Set Intersection and Consistency in Constraint Networks +" In this paper, we study the possibility of designing non-trivial random CSP +models by exploiting the intrinsic connection between structures and +typical-case hardness. We show that constraint consistency, a notion that has +been developed to improve the efficiency of CSP algorithms, is in fact the key +to the design of random CSP models that have interesting phase transition +behavior and guaranteed exponential resolution complexity without putting much +restriction on the parameter of constraint tightness or the domain size of the +problem. We propose a very flexible framework for constructing problem +instances withinteresting behavior and develop a variety of concrete methods to +construct specific random CSP models that enforce different levels of +constraint consistency. A series of experimental studies with interesting +observations are carried out to illustrate the effectiveness of introducing +structural elements in random instances, to verify the robustness of our +proposal, and to investigate features of some specific models based on our +framework that are highly related to the behavior of backtracking search +algorithms. +",Consistency and Random Constraint Satisfaction Models +" In this paper, we present two alternative approaches to defining answer sets +for logic programs with arbitrary types of abstract constraint atoms (c-atoms). +These approaches generalize the fixpoint-based and the level mapping based +answer set semantics of normal logic programs to the case of logic programs +with arbitrary types of c-atoms. The results are four different answer set +definitions which are equivalent when applied to normal logic programs. The +standard fixpoint-based semantics of logic programs is generalized in two +directions, called answer set by reduct and answer set by complement. These +definitions, which differ from each other in the treatment of +negation-as-failure (naf) atoms, make use of an immediate consequence operator +to perform answer set checking, whose definition relies on the notion of +conditional satisfaction of c-atoms w.r.t. a pair of interpretations. The other +two definitions, called strongly and weakly well-supported models, are +generalizations of the notion of well-supported models of normal logic programs +to the case of programs with c-atoms. As for the case of fixpoint-based +semantics, the difference between these two definitions is rooted in the +treatment of naf atoms. We prove that answer sets by reduct (resp. by +complement) are equivalent to weakly (resp. strongly) well-supported models of +a program, thus generalizing the theorem on the correspondence between stable +models and well-supported models of a normal logic program to the class of +programs with c-atoms. We show that the newly defined semantics coincide with +previously introduced semantics for logic programs with monotone c-atoms, and +they extend the original answer set semantics of normal logic programs. We also +study some properties of answer sets of programs with c-atoms, and relate our +definitions to several semantics for logic programs with aggregates presented +in the literature. +",Answer Sets for Logic Programs with Arbitrary Abstract Constraint Atoms +" Many combinatorial optimization problems such as the bin packing and multiple +knapsack problems involve assigning a set of discrete objects to multiple +containers. These problems can be used to model task and resource allocation +problems in multi-agent systems and distributed systms, and can also be found +as subproblems of scheduling problems. We propose bin completion, a +branch-and-bound strategy for one-dimensional, multicontainer packing problems. +Bin completion combines a bin-oriented search space with a powerful dominance +criterion that enables us to prune much of the space. The performance of the +basic bin completion framework can be enhanced by using a number of extensions, +including nogood-based pruning techniques that allow further exploitation of +the dominance criterion. Bin completion is applied to four problems: multiple +knapsack, bin covering, min-cost covering, and bin packing. We show that our +bin completion algorithms yield new, state-of-the-art results for the multiple +knapsack, bin covering, and min-cost covering problems, outperforming previous +algorithms by several orders of magnitude with respect to runtime on some +classes of hard, random problem instances. For the bin packing problem, we +demonstrate significant improvements compared to most previous results, but +show that bin completion is not competitive with current state-of-the-art +cutting-stock based approaches. +","Bin Completion Algorithms for Multicontainer Packing, Knapsack, and + Covering Problems" +" In real-life temporal scenarios, uncertainty and preferences are often +essential and coexisting aspects. We present a formalism where quantitative +temporal constraints with both preferences and uncertainty can be defined. We +show how three classical notions of controllability (that is, strong, weak, and +dynamic), which have been developed for uncertain temporal problems, can be +generalized to handle preferences as well. After defining this general +framework, we focus on problems where preferences follow the fuzzy approach, +and with properties that assure tractability. For such problems, we propose +algorithms to check the presence of the controllability properties. In +particular, we show that in such a setting dealing simultaneously with +preferences and uncertainty does not increase the complexity of controllability +testing. We also develop a dynamic execution algorithm, of polynomial +complexity, that produces temporal plans under uncertainty that are optimal +with respect to fuzzy preferences. +","Uncertainty in Soft Temporal Constraint Problems:A General Framework and + Controllability Algorithms for the Fuzzy Case" +" In the recent years several research efforts have focused on the concept of +time granularity and its applications. A first stream of research investigated +the mathematical models behind the notion of granularity and the algorithms to +manage temporal data based on those models. A second stream of research +investigated symbolic formalisms providing a set of algebraic operators to +define granularities in a compact and compositional way. However, only very +limited manipulation algorithms have been proposed to operate directly on the +algebraic representation making it unsuitable to use the symbolic formalisms in +applications that need manipulation of granularities. + This paper aims at filling the gap between the results from these two streams +of research, by providing an efficient conversion from the algebraic +representation to the equivalent low-level representation based on the +mathematical models. In addition, the conversion returns a minimal +representation in terms of period length. Our results have a major practical +impact: users can more easily define arbitrary granularities in terms of +algebraic operators, and then access granularity reasoning and other services +operating efficiently on the equivalent, minimal low-level representation. As +an example, we illustrate the application to temporal constraint reasoning with +multiple granularities. + From a technical point of view, we propose an hybrid algorithm that +interleaves the conversion of calendar subexpressions into periodical sets with +the minimization of the period length. The algorithm returns set-based +granularity representations having minimal period length, which is the most +relevant parameter for the performance of the considered reasoning services. +Extensive experimental work supports the techniques used in the algorithm, and +shows the efficiency and effectiveness of the algorithm. +","Supporting Temporal Reasoning by Mapping Calendar Expressions to Minimal + Periodic Sets" +" We consider the problem of computing a lightest derivation of a global +structure using a set of weighted rules. A large variety of inference problems +in AI can be formulated in this framework. We generalize A* search and +heuristics derived from abstractions to a broad class of lightest derivation +problems. We also describe a new algorithm that searches for lightest +derivations using a hierarchy of abstractions. Our generalization of A* gives a +new algorithm for searching AND/OR graphs in a bottom-up fashion. We discuss +how the algorithms described here provide a general architecture for addressing +the pipeline problem --- the problem of passing information back and forth +between various stages of processing in a perceptual system. We consider +examples in computer vision and natural language processing. We apply the +hierarchical search algorithm to the problem of estimating the boundaries of +convex objects in grayscale images and compare it to other search methods. A +second set of experiments demonstrate the use of a new compositional model for +finding salient curves in images. +",The Generalized A* Architecture +" In this paper, we construct and investigate a hierarchy of spatio-temporal +formalisms that result from various combinations of propositional spatial and +temporal logics such as the propositional temporal logic PTL, the spatial +logics RCC-8, BRCC-8, S4u and their fragments. The obtained results give a +clear picture of the trade-off between expressiveness and computational +realisability within the hierarchy. We demonstrate how different combining +principles as well as spatial and temporal primitives can produce NP-, PSPACE-, +EXPSPACE-, 2EXPSPACE-complete, and even undecidable spatio-temporal logics out +of components that are at most NP- or PSPACE-complete. +",Combining Spatial and Temporal Logics: Expressiveness vs. Complexity +" The treatment of exogenous events in planning is practically important in +many real-world domains where the preconditions of certain plan actions are +affected by such events. In this paper we focus on planning in temporal domains +with exogenous events that happen at known times, imposing the constraint that +certain actions in the plan must be executed during some predefined time +windows. When actions have durations, handling such temporal constraints adds +an extra difficulty to planning. We propose an approach to planning in these +domains which integrates constraint-based temporal reasoning into a graph-based +planning framework using local search. Our techniques are implemented in a +planner that took part in the 4th International Planning Competition (IPC-4). A +statistical analysis of the results of IPC-4 demonstrates the effectiveness of +our approach in terms of both CPU-time and plan quality. Additional experiments +show the good performance of the temporal reasoning techniques integrated into +our planner. +","An Approach to Temporal Planning and Scheduling in Domains with + Predictable Exogenous Events" +" In this commentary I argue that although PDDL is a very useful standard for +the planning competition, its design does not properly consider the issue of +domain modeling. Hence, I would not advocate its use in specifying planning +domains outside of the context of the planning competition. Rather, the field +needs to explore different approaches and grapple more directly with the +problem of effectively modeling and utilizing all of the diverse pieces of +knowledge we typically have about planning domains. +",The Power of Modeling - a Response to PDDL2.1 +" PDDL was originally conceived and constructed as a lingua franca for the +International Planning Competition. PDDL2.1 embodies a set of extensions +intended to support the expression of something closer to real planning +problems. This objective has only been partially achieved, due in large part to +a deliberate focus on not moving too far from classical planning models and +solution methods. +",Imperfect Match: PDDL 2.1 and Real Applications +" I comment on the PDDL 2.1 language and its use in the planning competition, +focusing on the choices made for accommodating time and concurrency. I also +discuss some methodological issues that have to do with the move toward more +expressive planning languages and the balance needed in planning research +between semantics and computation. +",PDDL 2.1: Representation vs. Computation +" Most classical scheduling formulations assume a fixed and known duration for +each activity. In this paper, we weaken this assumption, requiring instead that +each duration can be represented by an independent random variable with a known +mean and variance. The best solutions are ones which have a high probability of +achieving a good makespan. We first create a theoretical framework, formally +showing how Monte Carlo simulation can be combined with deterministic +scheduling algorithms to solve this problem. We propose an associated +deterministic scheduling problem whose solution is proved, under certain +conditions, to be a lower bound for the probabilistic problem. We then propose +and investigate a number of techniques for solving such problems based on +combinations of Monte Carlo simulation, solutions to the associated +deterministic problem, and either constraint programming or tabu search. Our +empirical results demonstrate that a combination of the use of the associated +deterministic problem and Monte Carlo simulation results in algorithms that +scale best both in terms of problem size and uncertainty. Further experiments +point to the correlation between the quality of the deterministic solution and +the quality of the probabilistic solution as a major factor responsible for +this success. +","Proactive Algorithms for Job Shop Scheduling with Probabilistic + Durations" +" This paper is concerned with a class of algorithms that perform exhaustive +search on propositional knowledge bases. We show that each of these algorithms +defines and generates a propositional language. Specifically, we show that the +trace of a search can be interpreted as a combinational circuit, and a search +algorithm then defines a propositional language consisting of circuits that are +generated across all possible executions of the algorithm. In particular, we +show that several versions of exhaustive DPLL search correspond to such +well-known languages as FBDD, OBDD, and a precisely-defined subset of d-DNNF. +By thus mapping search algorithms to propositional languages, we provide a +uniform and practical framework in which successful search techniques can be +harnessed for compilation of knowledge into various languages of interest, and +a new methodology whereby the power and limitations of search algorithms can be +understood by looking up the tractability and succinctness of the corresponding +propositional languages. +",The Language of Search +" The best performing algorithms for a particular oversubscribed scheduling +application, Air Force Satellite Control Network (AFSCN) scheduling, appear to +have little in common. Yet, through careful experimentation and modeling of +performance in real problem instances, we can relate characteristics of the +best algorithms to characteristics of the application. In particular, we find +that plateaus dominate the search spaces (thus favoring algorithms that make +larger changes to solutions) and that some randomization in exploration is +critical to good performance (due to the lack of gradient information on the +plateaus). Based on our explanations of algorithm performance, we develop a new +algorithm that combines characteristics of the best performers; the new +algorithms performance is better than the previous best. We show how hypothesis +driven experimentation and search modeling can both explain algorithm +performance and motivate the design of a new algorithm. +","Understanding Algorithm Performance on an Oversubscribed Scheduling + Application" +" This paper describes Marvin, a planner that competed in the Fourth +International Planning Competition (IPC 4). Marvin uses +action-sequence-memoisation techniques to generate macro-actions, which are +then used during search for a solution plan. We provide an overview of its +architecture and search behaviour, detailing the algorithms used. We also +empirically demonstrate the effectiveness of its features in various planning +domains; in particular, the effects on performance due to the use of +macro-actions, the novel features of its search behaviour, and the native +support of ADL and Derived Predicates. +",Marvin: A Heuristic Search Planner with Online Macro-Action Learning +" We describe how to convert the heuristic search algorithm A* into an anytime +algorithm that finds a sequence of improved solutions and eventually converges +to an optimal solution. The approach we adopt uses weighted heuristic search to +find an approximate solution quickly, and then continues the weighted search to +find improved solutions as well as to improve a bound on the suboptimality of +the current solution. When the time available to solve a search problem is +limited or uncertain, this creates an anytime heuristic search algorithm that +allows a flexible tradeoff between search time and solution quality. We analyze +the properties of the resulting Anytime A* algorithm, and consider its +performance in three domains; sliding-tile puzzles, STRIPS planning, and +multiple sequence alignment. To illustrate the generality of this approach, we +also describe how to transform the memory-efficient search algorithm Recursive +Best-First Search (RBFS) into an anytime algorithm. +",Anytime Heuristic Search +" In this paper we apply computer-aided theorem discovery technique to discover +theorems about strongly equivalent logic programs under the answer set +semantics. Our discovered theorems capture new classes of strongly equivalent +logic programs that can lead to new program simplification rules that preserve +strong equivalence. Specifically, with the help of computers, we discovered +exact conditions that capture the strong equivalence between a rule and the +empty set, between two rules, between two rules and one of the two rules, +between two rules and another rule, and between three rules and two of the +three rules. +",Discovering Classes of Strongly Equivalent Logic Programs +" The QXORSAT problem is the quantified version of the satisfiability problem +XORSAT in which the connective exclusive-or is used instead of the usual or. We +study the phase transition associated with random QXORSAT instances. We give a +description of this phase transition in the case of one alternation of +quantifiers, thus performing an advanced practical and theoretical study on the +phase transition of a quantified roblem. +",Phase Transition for Random Quantified XOR-Formulas +" The paper presents a new sampling methodology for Bayesian networks that +samples only a subset of variables and applies exact inference to the rest. +Cutset sampling is a network structure-exploiting application of the +Rao-Blackwellisation principle to sampling in Bayesian networks. It improves +convergence by exploiting memory-based inference algorithms. It can also be +viewed as an anytime approximation of the exact cutset-conditioning algorithm +developed by Pearl. Cutset sampling can be implemented efficiently when the +sampled variables constitute a loop-cutset of the Bayesian network and, more +generally, when the induced width of the networks graph conditioned on the +observed sampled variables is bounded by a constant w. We demonstrate +empirically the benefit of this scheme on a range of benchmarks. +",Cutset Sampling for Bayesian Networks +" Numerous formalisms and dedicated algorithms have been designed in the last +decades to model and solve decision making problems. Some formalisms, such as +constraint networks, can express ""simple"" decision problems, while others are +designed to take into account uncertainties, unfeasible decisions, and +utilities. Even in a single formalism, several variants are often proposed to +model different types of uncertainty (probability, possibility...) or utility +(additive or not). In this article, we introduce an algebraic graphical model +that encompasses a large number of such formalisms: (1) we first adapt previous +structures from Friedman, Chu and Halpern for representing uncertainty, +utility, and expected utility in order to deal with generic forms of sequential +decision making; (2) on these structures, we then introduce composite graphical +models that express information via variables linked by ""local"" functions, +thanks to conditional independence; (3) on these graphical models, we finally +define a simple class of queries which can represent various scenarios in terms +of observabilities and controllabilities. A natural decision-tree semantics for +such queries is completed by an equivalent operational semantics, which induces +generic algorithms. The proposed framework, called the +Plausibility-Feasibility-Utility (PFU) framework, not only provides a better +understanding of the links between existing formalisms, but it also covers yet +unpublished frameworks (such as possibilistic influence diagrams) and unifies +formalisms such as quantified boolean formulas and influence diagrams. Our +backtrack and variable elimination generic algorithms are a first step towards +unified algorithms. +","An Algebraic Graphical Model for Decision with Uncertainties, + Feasibilities, and Utilities" +" Matchmaking arises when supply and demand meet in an electronic marketplace, +or when agents search for a web service to perform some task, or even when +recruiting agencies match curricula and job profiles. In such open +environments, the objective of a matchmaking process is to discover best +available offers to a given request. We address the problem of matchmaking from +a knowledge representation perspective, with a formalization based on +Description Logics. We devise Concept Abduction and Concept Contraction as +non-monotonic inferences in Description Logics suitable for modeling +matchmaking in a logical framework, and prove some related complexity results. +We also present reasonable algorithms for semantic matchmaking based on the +devised inferences, and prove that they obey to some commonsense properties. +Finally, we report on the implementation of the proposed matchmaking framework, +which has been used both as a mediator in e-marketplaces and for semantic web +services discovery. +","Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic + Approach" +" Solution-Guided Multi-Point Constructive Search (SGMPCS) is a novel +constructive search technique that performs a series of resource-limited tree +searches where each search begins either from an empty solution (as in +randomized restart) or from a solution that has been encountered during the +search. A small number of these ""elite solutions is maintained during the +search. We introduce the technique and perform three sets of experiments on the +job shop scheduling problem. First, a systematic, fully crossed study of SGMPCS +is carried out to evaluate the performance impact of various parameter +settings. Second, we inquire into the diversity of the elite solution set, +showing, contrary to expectations, that a less diverse set leads to stronger +performance. Finally, we compare the best parameter setting of SGMPCS from the +first two experiments to chronological backtracking, limited discrepancy +search, randomized restart, and a sophisticated tabu search algorithm on a set +of well-known benchmark problems. Results demonstrate that SGMPCS is +significantly better than the other constructive techniques tested, though lags +behind the tabu search. +",Solution-Guided Multi-Point Constructive Search for Job Shop Scheduling +" This essay explores the limits of Turing machines concerning the modeling of +minds and suggests alternatives to go beyond those limits. +",Are Minds Computable? +" This paper compares various optimization methods for fuzzy inference system +optimization. The optimization methods compared are genetic algorithm, particle +swarm optimization and simulated annealing. When these techniques were +implemented it was observed that the performance of each technique within the +fuzzy inference system classification was context dependent. +",Fuzzy Inference Systems Optimization +" We introduce matrix and its block to the Dung's theory of argumentation +framework. It is showed that each argumentation framework has a matrix +representation, and the indirect attack relation and indirect defence relation +can be characterized by computing the matrix. This provide a powerful +mathematics way to determine the ""controversial arguments"" in an argumentation +framework. Also, we introduce several kinds of blocks based on the matrix, and +various prudent semantics of argumentation frameworks can all be determined by +computing and comparing the matrices and their blocks which we have defined. In +contrast with traditional method of directed graph, the matrix method has an +excellent advantage: computability(even can be realized on computer easily). +So, there is an intensive perspective to import the theory of matrix to the +research of argumentation frameworks and its related areas. +",Handling controversial arguments by matrix +" Real-time search methods are suited for tasks in which the agent is +interacting with an initially unknown environment in real time. In such +simultaneous planning and learning problems, the agent has to select its +actions in a limited amount of time, while sensing only a local part of the +environment centered at the agents current location. Real-time heuristic search +agents select actions using a limited lookahead search and evaluating the +frontier states with a heuristic function. Over repeated experiences, they +refine heuristic values of states to avoid infinite loops and to converge to +better solutions. The wide spread of such settings in autonomous software and +hardware agents has led to an explosion of real-time search algorithms over the +last two decades. Not only is a potential user confronted with a hodgepodge of +algorithms, but he also faces the choice of control parameters they use. In +this paper we address both problems. The first contribution is an introduction +of a simple three-parameter framework (named LRTS) which extracts the core +ideas behind many existing algorithms. We then prove that LRTA*, epsilon-LRTA*, +SLA*, and gamma-Trap algorithms are special cases of our framework. Thus, they +are unified and extended with additional features. Second, we prove +completeness and convergence of any algorithm covered by the LRTS framework. +Third, we prove several upper-bounds relating the control parameters and +solution quality. Finally, we analyze the influence of the three control +parameters empirically in the realistic scalable domains of real-time +navigation on initially unknown maps from a commercial role-playing game as +well as routing in ad hoc sensor networks. +",Learning in Real-Time Search: A Unifying Framework +" This paper introduces the SEQ BIN meta-constraint with a polytime algorithm +achieving general- ized arc-consistency according to some properties. SEQ BIN +can be used for encoding counting con- straints such as CHANGE, SMOOTH or +INCREAS- ING NVALUE. For some of these constraints and some of their variants +GAC can be enforced with a time and space complexity linear in the sum of +domain sizes, which improves or equals the best known results of the +literature. +","A Generalized Arc-Consistency Algorithm for a Class of Counting + Constraints: Revised Edition that Incorporates One Correction" +" The Taaable projet goal is to create a case-based reasoning system for +retrieval and adaptation of cooking recipes. Within this framework, we are +discussing the temporal aspects of recipes and the means of representing those +in order to adapt their text. +","Quels formalismes temporels pour repr\'esenter des connaissances + extraites de textes de recettes de cuisine ?" +" Designing component-based constraint solvers is a complex problem. Some +components are required, some are optional and there are interdependencies +between the components. Because of this, previous approaches to solver design +and modification have been ad-hoc and limited. We present a system that +transforms a description of the components and the characteristics of the +target constraint solver into a constraint problem. Solving this problem yields +the description of a valid solver. Our approach represents a significant step +towards the automated design and synthesis of constraint solvers that are +specialised for individual constraint problem classes or instances. +",Modelling Constraint Solver Architecture Design as a Constraint Problem +" Classification of targets by radar has proved to be notoriously difficult +with the best systems still yet to attain sufficiently high levels of +performance and reliability. In the current contribution we explore a new +design of radar based target recognition, where angular diversity is used in a +cognitive manner to attain better performance. Performance is bench- marked +against conventional classification schemes. The proposed scheme can easily be +extended to cognitive target recognition based on multiple diversity +strategies. +",A cognitive diversity framework for radar target classification +" It is widely recognized today that the management of imprecision and +vagueness will yield more intelligent and realistic knowledge-based +applications. Description Logics (DLs) are a family of knowledge representation +languages that have gained considerable attention the last decade, mainly due +to their decidability and the existence of empirically high performance of +reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to +the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms +(S), inverse roles (I), role hierarchies (H) and number restrictions (N). We +illustrate why transitive role axioms are difficult to handle in the presence +of fuzzy interpretations and how to handle them properly. Then we extend these +results by adding role hierarchies and finally number restrictions. The main +contributions of the paper are the decidability proof of the fuzzy DL languages +fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base +satisfiability problem of the fuzzy-SI and fuzzy-SHIN. +",Reasoning with Very Expressive Fuzzy Description Logics +" Exact Max-SAT solvers, compared with SAT solvers, apply little inference at +each node of the proof tree. Commonly used SAT inference rules like unit +propagation produce a simplified formula that preserves satisfiability but, +unfortunately, solving the Max-SAT problem for the simplified formula is not +equivalent to solving it for the original formula. In this paper, we define a +number of original inference rules that, besides being applied efficiently, +transform Max-SAT instances into equivalent Max-SAT instances which are easier +to solve. The soundness of the rules, that can be seen as refinements of unit +resolution adapted to Max-SAT, are proved in a novel and simple way via an +integer programming transformation. With the aim of finding out how powerful +the inference rules are in practice, we have developed a new Max-SAT solver, +called MaxSatz, which incorporates those rules, and performed an experimental +investigation. The results provide empirical evidence that MaxSatz is very +competitive, at least, on random Max-2SAT, random Max-3SAT, Max-Cut, and Graph +3-coloring instances, as well as on the benchmarks from the Max-SAT Evaluation +2006. +",New Inference Rules for Max-SAT +" Reputation mechanisms offer an effective alternative to verification +authorities for building trust in electronic markets with moral hazard. Future +clients guide their business decisions by considering the feedback from past +transactions; if truthfully exposed, cheating behavior is sanctioned and thus +becomes irrational. + It therefore becomes important to ensure that rational clients have the right +incentives to report honestly. As an alternative to side-payment schemes that +explicitly reward truthful reports, we show that honesty can emerge as a +rational behavior when clients have a repeated presence in the market. To this +end we describe a mechanism that supports an equilibrium where truthful +feedback is obtained. Then we characterize the set of pareto-optimal equilibria +of the mechanism, and derive an upper bound on the percentage of false reports +that can be recorded by the mechanism. An important role in the existence of +this bound is played by the fact that rational clients can establish a +reputation for reporting honestly. +",Obtaining Reliable Feedback for Sanctioning Reputation Mechanisms +" We present a new algorithm for probabilistic planning with no observability. +Our algorithm, called Probabilistic-FF, extends the heuristic forward-search +machinery of Conformant-FF to problems with probabilistic uncertainty about +both the initial state and action effects. Specifically, Probabilistic-FF +combines Conformant-FFs techniques with a powerful machinery for weighted model +counting in (weighted) CNFs, serving to elegantly define both the search space +and the heuristic function. Our evaluation of Probabilistic-FF shows its fine +scalability in a range of probabilistic domains, constituting a several orders +of magnitude improvement over previous results in this area. We use a +problematic case to point out the main open issue to be addressed by further +research. +","Probabilistic Planning via Heuristic Forward Search and Weighted Model + Counting" +" Conjunctive queries play an important role as an expressive query language +for Description Logics (DLs). Although modern DLs usually provide for +transitive roles, conjunctive query answering over DL knowledge bases is only +poorly understood if transitive roles are admitted in the query. In this paper, +we consider unions of conjunctive queries over knowledge bases formulated in +the prominent DL SHIQ and allow transitive roles in both the query and the +knowledge base. We show decidability of query answering in this setting and +establish two tight complexity bounds: regarding combined complexity, we prove +that there is a deterministic algorithm for query answering that needs time +single exponential in the size of the KB and double exponential in the size of +the query, which is optimal. Regarding data complexity, we prove containment in +co-NP. +",Conjunctive Query Answering for the Description Logic SHIQ +" Experience in the physical sciences suggests that the only realistic means of +understanding complex systems is through the use of mathematical models. +Typically, this has come to mean the identification of quantitative models +expressed as differential equations. Quantitative modelling works best when the +structure of the model (i.e., the form of the equations) is known; and the +primary concern is one of estimating the values of the parameters in the model. +For complex biological systems, the model-structure is rarely known and the +modeler has to deal with both model-identification and parameter-estimation. In +this paper we are concerned with providing automated assistance to the first of +these problems. Specifically, we examine the identification by machine of the +structural relationships between experimentally observed variables. These +relationship will be expressed in the form of qualitative abstractions of a +quantitative model. Such qualitative models may not only provide clues to the +precise quantitative model, but also assist in understanding the essence of +that model. Our position in this paper is that background knowledge +incorporating system modelling principles can be used to constrain effectively +the set of good qualitative models. Utilising the model-identification +framework provided by Inductive Logic Programming (ILP) we present empirical +support for this position using a series of increasingly complex artificial +datasets. The results are obtained with qualitative and quantitative data +subject to varying amounts of noise and different degrees of sparsity. The +results also point to the presence of a set of qualitative states, which we +term kernel subsets, that may be necessary for a qualitative model-learner to +learn correct models. We demonstrate scalability of the method to biological +system modelling by identification of the glycolysis metabolic pathway from +data. +",Qualitative System Identification from Imperfect Data +" Multi-robot path planning is difficult due to the combinatorial explosion of +the search space with every new robot added. Complete search of the combined +state-space soon becomes intractable. In this paper we present a novel form of +abstraction that allows us to plan much more efficiently. The key to this +abstraction is the partitioning of the map into subgraphs of known structure +with entry and exit restrictions which we can represent compactly. Planning +then becomes a search in the much smaller space of subgraph configurations. +Once an abstract plan is found, it can be quickly resolved into a correct (but +possibly sub-optimal) concrete plan without the need for further search. We +prove that this technique is sound and complete and demonstrate its practical +effectiveness on a real map. + A contending solution, prioritised planning, is also evaluated and shown to +have similar performance albeit at the cost of completeness. The two approaches +are not necessarily conflicting; we demonstrate how they can be combined into a +single algorithm which outperforms either approach alone. +",Exploiting Subgraph Structure in Multi-Robot Path Planning +" Ontologies and automated reasoning are the building blocks of the Semantic +Web initiative. Derivation rules can be included in an ontology to define +derived concepts, based on base concepts. For example, rules allow to define +the extension of a class or property, based on a complex relation between the +extensions of the same or other classes and properties. On the other hand, the +inclusion of negative information both in the form of negation-as-failure and +explicit negative information is also needed to enable various forms of +reasoning. In this paper, we extend RDF graphs with weak and strong negation, +as well as derivation rules. The ERDF stable model semantics of the extended +framework (Extended RDF) is defined, extending RDF(S) semantics. A distinctive +feature of our theory, which is based on Partial Logic, is that both truth and +falsity extensions of properties and classes are considered, allowing for truth +value gaps. Our framework supports both closed-world and open-world reasoning +through the explicit representation of the particular closed-world assumptions +and the ERDF ontological categories of total properties and total classes. +",Extended RDF as a Semantic Foundation of Rule Markup Languages +" We present three new complexity results for classes of planning problems with +simple causal graphs. First, we describe a polynomial-time algorithm that uses +macros to generate plans for the class 3S of planning problems with binary +state variables and acyclic causal graphs. This implies that plan generation +may be tractable even when a planning problem has an exponentially long minimal +solution. We also prove that the problem of plan existence for planning +problems with multi-valued variables and chain causal graphs is NP-hard. +Finally, we show that plan existence for planning problems with binary state +variables and polytree causal graphs is NP-complete. +",The Complexity of Planning Problems With Simple Causal Graphs +" We represent planning as a set of loosely coupled network flow problems, +where each network corresponds to one of the state variables in the planning +domain. The network nodes correspond to the state variable values and the +network arcs correspond to the value transitions. The planning problem is to +find a path (a sequence of actions) in each network such that, when merged, +they constitute a feasible plan. In this paper we present a number of integer +programming formulations that model these loosely coupled networks with varying +degrees of flexibility. Since merging may introduce exponentially many ordering +constraints we implement a so-called branch-and-cut algorithm, in which these +constraints are dynamically generated and added to the formulation when needed. +Our results are very promising, they improve upon previous planning as integer +programming approaches and lay the foundation for integer programming +approaches for cost optimal planning. +","Loosely Coupled Formulations for Automated Planning: An Integer + Programming Perspective" +" In a facility with front room and back room operations, it is useful to +switch workers between the rooms in order to cope with changing customer +demand. Assuming stochastic customer arrival and service times, we seek a +policy for switching workers such that the expected customer waiting time is +minimized while the expected back room staffing is sufficient to perform all +work. Three novel constraint programming models and several shaving procedures +for these models are presented. Experimental results show that a model based on +closed-form expressions together with a combination of shaving procedures is +the most efficient. This model is able to find and prove optimal solutions for +many problem instances within a reasonable run-time. Previously, the only +available approach was a heuristic algorithm. Furthermore, a hybrid method +combining the heuristic and the best constraint programming method is shown to +perform as well as the heuristic in terms of solution quality over time, while +achieving the same performance in terms of proving optimality as the pure +constraint programming model. This is the first work of which we are aware that +solves such queueing-based problems with constraint programming. +",A Constraint Programming Approach for Solving a Queueing Control Problem +" Decision-theoretic planning is a popular approach to sequential decision +making problems, because it treats uncertainty in sensing and acting in a +principled way. In single-agent frameworks like MDPs and POMDPs, planning can +be carried out by resorting to Q-value functions: an optimal Q-value function +Q* is computed in a recursive manner by dynamic programming, and then an +optimal policy is extracted from Q*. In this paper we study whether similar +Q-value functions can be defined for decentralized POMDP models (Dec-POMDPs), +and how policies can be extracted from such value functions. We define two +forms of the optimal Q-value function for Dec-POMDPs: one that gives a +normative description as the Q-value function of an optimal pure joint policy +and another one that is sequentially rational and thus gives a recipe for +computation. This computation, however, is infeasible for all but the smallest +problems. Therefore, we analyze various approximate Q-value functions that +allow for efficient computation. We describe how they relate, and we prove that +they all provide an upper bound to the optimal Q-value function Q*. Finally, +unifying some previous approaches for solving Dec-POMDPs, we describe a family +of algorithms for extracting policies from such Q-value functions, and perform +an experimental evaluation on existing test problems, including a new +firefighting benchmark problem. +",Optimal and Approximate Q-value Functions for Decentralized POMDPs +" Multi-agent planning in stochastic environments can be framed formally as a +decentralized Markov decision problem. Many real-life distributed problems that +arise in manufacturing, multi-robot coordination and information gathering +scenarios can be formalized using this framework. However, finding the optimal +solution in the general case is hard, limiting the applicability of recently +developed algorithms. This paper provides a practical approach for solving +decentralized control problems when communication among the decision makers is +possible, but costly. We develop the notion of communication-based mechanism +that allows us to decompose a decentralized MDP into multiple single-agent +problems. In this framework, referred to as decentralized semi-Markov decision +process with direct communication (Dec-SMDP-Com), agents operate separately +between communications. We show that finding an optimal mechanism is equivalent +to solving optimally a Dec-SMDP-Com. We also provide a heuristic search +algorithm that converges on the optimal decomposition. Restricting the +decomposition to some specific types of local behaviors reduces significantly +the complexity of planning. In particular, we present a polynomial-time +algorithm for the case in which individual agents perform goal-oriented +behaviors between communications. The paper concludes with an additional +tractable algorithm that enables the introduction of human knowledge, thereby +reducing the overall problem to finding the best time to communicate. Empirical +results show that these approaches provide good approximate solutions. +",Communication-Based Decomposition Mechanisms for Decentralized MDPs +" Informally, a set of abstractions of a state space S is additive if the +distance between any two states in S is always greater than or equal to the sum +of the corresponding distances in the abstract spaces. The first known additive +abstractions, called disjoint pattern databases, were experimentally +demonstrated to produce state of the art performance on certain state spaces. +However, previous applications were restricted to state spaces with special +properties, which precludes disjoint pattern databases from being defined for +several commonly used testbeds, such as Rubiks Cube, TopSpin and the Pancake +puzzle. In this paper we give a general definition of additive abstractions +that can be applied to any state space and prove that heuristics based on +additive abstractions are consistent as well as admissible. We use this new +definition to create additive abstractions for these testbeds and show +experimentally that well chosen additive abstractions can reduce search time +substantially for the (18,4)-TopSpin puzzle and by three orders of magnitude +over state of the art methods for the 17-Pancake puzzle. We also derive a way +of testing if the heuristic value returned by additive abstractions is provably +too low and show that the use of this test can reduce search time for the +15-puzzle and TopSpin by roughly a factor of two. +",A General Theory of Additive State Space Abstractions +" Markov decision processes capture sequential decision making under +uncertainty, where an agent must choose actions so as to optimize long term +reward. The paper studies efficient reasoning mechanisms for Relational Markov +Decision Processes (RMDP) where world states have an internal relational +structure that can be naturally described in terms of objects and relations +among them. Two contributions are presented. First, the paper develops First +Order Decision Diagrams (FODD), a new compact representation for functions over +relational structures, together with a set of operators to combine FODDs, and +novel reduction techniques to keep the representation small. Second, the paper +shows how FODDs can be used to develop solutions for RMDPs, where reasoning is +performed at the abstract level and the resulting optimal policy is independent +of domain size (number of objects) or instantiation. In particular, a variant +of the value iteration algorithm is developed by using special operations over +FODDs, and the algorithm is shown to converge to the optimal policy. +",First Order Decision Diagrams for Relational MDPs +" Resolution is the rule of inference at the basis of most procedures for +automated reasoning. In these procedures, the input formula is first translated +into an equisatisfiable formula in conjunctive normal form (CNF) and then +represented as a set of clauses. Deduction starts by inferring new clauses by +resolution, and goes on until the empty clause is generated or satisfiability +of the set of clauses is proven, e.g., because no new clauses can be generated. + In this paper, we restrict our attention to the problem of evaluating +Quantified Boolean Formulas (QBFs). In this setting, the above outlined +deduction process is known to be sound and complete if given a formula in CNF +and if a form of resolution, called Q-resolution, is used. We introduce +Q-resolution on terms, to be used for formulas in disjunctive normal form. We +show that the computation performed by most of the available procedures for +QBFs --based on the Davis-Logemann-Loveland procedure (DLL) for propositional +satisfiability-- corresponds to a tree in which Q-resolution on terms and +clauses alternate. This poses the theoretical bases for the introduction of +learning, corresponding to recording Q-resolution formulas associated with the +nodes of the tree. We discuss the problems related to the introduction of +learning in DLL based procedures, and present solutions extending +state-of-the-art proposals coming from the literature on propositional +satisfiability. Finally, we show that our DLL based solver extended with +learning, performs significantly better on benchmarks used in the 2003 QBF +solvers comparative evaluation. +","Clause/Term Resolution and Learning in the Evaluation of Quantified + Boolean Formulas" +" The theoretical transition from the graphs of production systems to the +bipartite graphs of the MIVAR nets is shown. Examples of the implementation of +the MIVAR nets in the formalisms of matrixes and graphs are given. The linear +computational complexity of algorithms for automated building of objects and +rules of the MIVAR nets is theoretically proved. On the basis of the MIVAR nets +the UDAV software complex is developed, handling more than 1.17 million objects +and more than 3.5 million rules on ordinary computers. The results of +experiments that confirm a linear computational complexity of the MIVAR method +of information processing are given. + Keywords: MIVAR, MIVAR net, logical inference, computational complexity, +artificial intelligence, intelligent systems, expert systems, General Problem +Solver. +","MIVAR: Transition from Productions to Bipartite Graphs MIVAR Nets and + Practical Realization of Automated Constructor of Algorithms Handling More + than Three Million Production Rules" +" Description logic programs (dl-programs) under the answer set semantics +formulated by Eiter {\em et al.} have been considered as a prominent formalism +for integrating rules and ontology knowledge bases. A question of interest has +been whether dl-programs can be captured in a general formalism of nonmonotonic +logic. In this paper, we study the possibility of embedding dl-programs into +default logic. We show that dl-programs under the strong and weak answer set +semantics can be embedded in default logic by combining two translations, one +of which eliminates the constraint operator from nonmonotonic dl-atoms and the +other translates a dl-program into a default theory. For dl-programs without +nonmonotonic dl-atoms but with the negation-as-failure operator, our embedding +is polynomial, faithful, and modular. In addition, our default logic encoding +can be extended in a simple way to capture recently proposed weakly +well-supported answer set semantics, for arbitrary dl-programs. These results +reinforce the argument that default logic can serve as a fruitful foundation +for query-based approaches to integrating ontology and rules. With its simple +syntax and intuitive semantics, plus available computational results, default +logic can be considered an attractive approach to integration of ontology and +rules. +",Embedding Description Logic Programs into Default Logic +" Electronic government (e-government) has been one of the most active areas of +ontology development during the past six years. In e-government, ontologies are +being used to describe and specify e-government services (e-services) because +they enable easy composition, matching, mapping and merging of various +e-government services. More importantly, they also facilitate the semantic +integration and interoperability of e-government services. However, it is still +unclear in the current literature how an existing ontology building methodology +can be applied to develop semantic ontology models in a government service +domain. In this paper the Uschold and King ontology building methodology is +applied to develop semantic ontology models in a government service domain. +Firstly, the Uschold and King methodology is presented, discussed and applied +to build a government domain ontology. Secondly, the domain ontology is +evaluated for semantic consistency using its semi-formal representation in +Description Logic. Thirdly, an alignment of the domain ontology with the +Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) upper +level ontology is drawn to allow its wider visibility and facilitate its +integration with existing metadata standard. Finally, the domain ontology is +formally written in Web Ontology Language (OWL) to enable its automatic +processing by computers. The study aims to provide direction for the +application of existing ontology building methodologies in the Semantic Web +development processes of e-government domain specific ontology models; which +would enable their repeatability in other e-government projects and strengthen +the adoption of semantic technologies in e-government. +","Semantic-Driven e-Government: Application of Uschold and King Ontology + Building Methodology for Semantic Ontology Models Development" +" It has been widely observed that there is no single ""dominant"" SAT solver; +instead, different solvers perform best on different instances. Rather than +following the traditional approach of choosing the best solver for a given +class of instances, we advocate making this decision online on a per-instance +basis. Building on previous work, we describe SATzilla, an automated approach +for constructing per-instance algorithm portfolios for SAT that use so-called +empirical hardness models to choose among their constituent solvers. This +approach takes as input a distribution of problem instances and a set of +component solvers, and constructs a portfolio optimizing a given objective +function (such as mean runtime, percent of instances solved, or score in a +competition). The excellent performance of SATzilla was independently verified +in the 2007 SAT Competition, where our SATzilla07 solvers won three gold, one +silver and one bronze medal. In this article, we go well beyond SATzilla07 by +making the portfolio construction scalable and completely automated, and +improving it by integrating local search solvers as candidate solvers, by +predicting performance score instead of runtime, and by using hierarchical +hardness models that take into account different types of SAT instances. We +demonstrate the effectiveness of these new techniques in extensive experimental +results on data sets including instances from the most recent SAT competition. +",SATzilla: Portfolio-based Algorithm Selection for SAT +" This paper shows that maintaining logical consistency of an iris recognition +system is a matter of finding a suitable partitioning of the input space in +enrollable and unenrollable pairs by negotiating the user comfort and the +safety of the biometric system. In other words, consistent enrollment is +mandatory in order to preserve system consistency. A fuzzy 3-valued +disambiguated model of iris recognition is proposed and analyzed in terms of +completeness, consistency, user comfort and biometric safety. It is also shown +here that the fuzzy 3-valued model of iris recognition is hosted by an 8-valued +Boolean algebra of modulo 8 integers that represents the computational +formalization in which a biometric system (a software agent) can achieve the +artificial understanding of iris recognition in a logically consistent manner. +",8-Valent Fuzzy Logic for Iris Recognition and Biometry +" In voting contexts, some new candidates may show up in the course of the +process. In this case, we may want to determine which of the initial candidates +are possible winners, given that a fixed number $k$ of new candidates will be +added. We give a computational study of this problem, focusing on scoring +rules, and we provide a formal comparison with related problems such as control +via adding candidates or cloning. +","New Candidates Welcome! Possible Winners with respect to the Addition of + New Candidates" +" Orseau and Ring, as well as Dewey, have recently described problems, +including self-delusion, with the behavior of agents using various definitions +of utility functions. An agent's utility function is defined in terms of the +agent's history of interactions with its environment. This paper argues, via +two examples, that the behavior problems can be avoided by formulating the +utility function in two steps: 1) inferring a model of the environment from +interactions, and 2) computing utility as a function of the environment model. +Basing a utility function on a model that the agent must learn implies that the +utility function must initially be expressed in terms of specifications to be +matched to structures in the learned model. These specifications constitute +prior assumptions about the environment so this approach will not work with +arbitrary environments. But the approach should work for agents designed by +humans to act in the physical world. The paper also addresses the issue of +self-modifying agents and shows that if provided with the possibility to modify +their utility functions agents will not choose to do so, under some usual +assumptions. +",Model-based Utility Functions +" We show that estimating the complexity (mean and distribution) of the +instances of a fixed size Constraint Satisfaction Problem (CSP) can be very +hard. We deal with the main two aspects of the problem: defining a measure of +complexity and generating random unbiased instances. For the first problem, we +rely on a general framework and a measure of complexity we presented at +CISSE08. For the generation problem, we restrict our analysis to the Sudoku +example and we provide a solution that also explains why it is so difficult. +",Unbiased Statistics of a CSP - A Controlled-Bias Generator +" Trying to be effective (no matter who exactly and in what field) a person +face the problem which inevitably destroys all our attempts to easily get to a +desired goal. The problem is the existence of some insuperable barriers for our +mind, anotherwords barriers for principles of thinking. They are our clue and +main reason for research. Here we investigate these barriers and their features +exposing the nature of mental process. We start from special structures which +reflect the ways to define relations between objects. Then we came to realizing +about what is the material our mind uses to build thoughts, to make +conclusions, to understand, to form reasoning, etc. This can be called a mental +dynamics. After this the nature of mental barriers on the required level of +abstraction as well as the ways to pass through them became clear. We begin to +understand why thinking flows in such a way, with such specifics and with such +limitations we can observe in reality. This can help us to be more optimal. At +the final step we start to understand, what ma-thematical models can be applied +to such a picture. We start to express our thoughts in a language of +mathematics, developing an apparatus for our Spatial Theory of Mind, suitable +to represent processes and infrastructure of thinking. We use abstract algebra +and stay invariant in relation to the nature of objects. +",A Model of Spatial Thinking for Computational Intelligence +" In this paper, we investigate the problem of mining numerical data in the +framework of Formal Concept Analysis. The usual way is to use a scaling +procedure --transforming numerical attributes into binary ones-- leading either +to a loss of information or of efficiency, in particular w.r.t. the volume of +extracted patterns. By contrast, we propose to directly work on numerical data +in a more precise and efficient way, and we prove it. For that, the notions of +closed patterns, generators and equivalent classes are revisited in the +numerical context. Moreover, two original algorithms are proposed and used in +an evaluation involving real-world data, showing the predominance of the +present approach. +",Revisiting Numerical Pattern Mining with Formal Concept Analysis +" We identify principles characterizing Solomonoff Induction by demands on an +agent's external behaviour. Key concepts are rationality, computability, +indifference and time consistency. Furthermore, we discuss extensions to the +full AI case to derive AIXI. +",Principles of Solomonoff Induction and AIXI +" The use of patterns in predictive models is a topic that has received a lot +of attention in recent years. Pattern mining can help to obtain models for +structured domains, such as graphs and sequences, and has been proposed as a +means to obtain more accurate and more interpretable models. Despite the large +amount of publications devoted to this topic, we believe however that an +overview of what has been accomplished in this area is missing. This paper +presents our perspective on this evolving area. We identify the principles of +pattern mining that are important when mining patterns for models and provide +an overview of pattern-based classification methods. We categorize these +methods along the following dimensions: (1) whether they post-process a +pre-computed set of patterns or iteratively execute pattern mining algorithms; +(2) whether they select patterns model-independently or whether the pattern +selection is guided by a model. We summarize the results that have been +obtained for each of these methods. +",Pattern-Based Classification: A Unifying Perspective +" Nowadays, e-government has emerged as a government policy to improve the +quality and efficiency of public administrations. By exploiting the potential +of new information and communication technologies, government agencies are +providing a wide spectrum of online services. These services are composed of +several web services that comply with well defined processes. One of the big +challenges is the need to optimize the composition of the elementary web +services. In this paper, we present a solution for optimizing the computation +effort in web service composition. Our method is based on Graph Theory. We +model the semantic relationship between the involved web services through a +directed graph. Then, we compute all shortest paths using for the first time, +an extended version of the Floyd-Warshall algorithm. +",Graph based E-Government web service composition +" With the fast growth of the Internet, more and more information is available +on the Web. The Semantic Web has many features which cannot be handled by using +the traditional search engines. It extracts metadata for each discovered Web +documents in RDF or OWL formats, and computes relations between documents. We +proposed a hybrid indexing and ranking technique for the Semantic Web which +finds relevant documents and computes the similarity among a set of documents. +First, it returns with the most related document from the repository of +Semantic Web Documents (SWDs) by using a modified version of the ObjectRank +technique. Then, it creates a sub-graph for the most related SWDs. Finally, It +returns the hubs and authorities of these document by using the HITS algorithm. +Our technique increases the quality of the results and decreases the execution +time of processing the user's query. +",An Enhanced Indexing And Ranking Technique On The Semantic Web +" This paper presents an algorithm for learning a highly redundant inverse +model in continuous and non-preset environments. Our Socially Guided Intrinsic +Motivation by Demonstrations (SGIM-D) algorithm combines the advantages of both +social learning and intrinsic motivation, to specialise in a wide range of +skills, while lessening its dependence on the teacher. SGIM-D is evaluated on a +fishing skill learning experiment. +","Constraining the Size Growth of the Task Space with Socially Guided + Intrinsic Motivation using Demonstrations" +" We consider an extension of the setting of label ranking, in which the +learner is allowed to make predictions in the form of partial instead of total +orders. Predictions of that kind are interpreted as a partial abstention: If +the learner is not sufficiently certain regarding the relative order of two +alternatives, it may abstain from this decision and instead declare these +alternatives as being incomparable. We propose a new method for learning to +predict partial orders that improves on an existing approach, both +theoretically and empirically. Our method is based on the idea of thresholding +the probabilities of pairwise preferences between labels as induced by a +predicted (parameterized) probability distribution on the set of all rankings. +","Label Ranking with Abstention: Predicting Partial Orders by Thresholding + Probability Distributions (Extended Abstract)" +" Covering model provides a general framework for granular computing in that +overlapping among granules are almost indispensable. For any given covering, +both intersection and union of covering blocks containing an element are +exploited as granules to form granular worlds at different abstraction levels, +respectively, and transformations among these different granular worlds are +also discussed. As an application of the presented multi-granular perspective +on covering, relational interpretation and axiomization of four types of +covering based rough upper approximation operators are investigated, which can +be dually applied to lower ones. +",Multi-granular Perspectives on Covering +" Slow Feature Analysis (SFA) extracts features representing the underlying +causes of changes within a temporally coherent high-dimensional raw sensory +input signal. Our novel incremental version of SFA (IncSFA) combines +incremental Principal Components Analysis and Minor Components Analysis. Unlike +standard batch-based SFA, IncSFA adapts along with non-stationary environments, +is amenable to episodic training, is not corrupted by outliers, and is +covariance-free. These properties make IncSFA a generally useful unsupervised +preprocessor for autonomous learning agents and robots. In IncSFA, the CCIPCA +and MCA updates take the form of Hebbian and anti-Hebbian updating, extending +the biological plausibility of SFA. In both single node and deep network +versions, IncSFA learns to encode its input streams (such as high-dimensional +video) by informative slow features representing meaningful abstract +environmental properties. It can handle cases where batch SFA fails. +","Incremental Slow Feature Analysis: Adaptive and Episodic Learning from + High-Dimensional Input Streams" +" Many performance metrics have been introduced for the evaluation of +classification performance, with different origins and niches of application: +accuracy, macro-accuracy, area under the ROC curve, the ROC convex hull, the +absolute error, and the Brier score (with its decomposition into refinement and +calibration). One way of understanding the relation among some of these metrics +is the use of variable operating conditions (either in the form of +misclassification costs or class proportions). Thus, a metric may correspond to +some expected loss over a range of operating conditions. One dimension for the +analysis has been precisely the distribution we take for this range of +operating conditions, leading to some important connections in the area of +proper scoring rules. However, we show that there is another dimension which +has not received attention in the analysis of performance metrics. This new +dimension is given by the decision rule, which is typically implemented as a +threshold choice method when using scoring models. In this paper, we explore +many old and new threshold choice methods: fixed, score-uniform, score-driven, +rate-driven and optimal, among others. By calculating the loss of these methods +for a uniform range of operating conditions we get the 0-1 loss, the absolute +error, the Brier score (mean squared error), the AUC and the refinement loss +respectively. This provides a comprehensive view of performance metrics as well +as a systematic approach to loss minimisation, namely: take a model, apply +several threshold choice methods consistent with the information which is (and +will be) available about the operating condition, and compare their expected +losses. In order to assist in this procedure we also derive several connections +between the aforementioned performance metrics, and we highlight the role of +calibration in choosing the threshold choice method. +",Threshold Choice Methods: the Missing Link +" Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's +PRISM, Poole's ICL, Raedt et al's ProbLog and Vennekens et al's LPAD, is aimed +at combining statistical and logical knowledge representation and inference. A +key characteristic of PLP frameworks is that they are conservative extensions +to non-probabilistic logic programs which have been widely used for knowledge +representation. PLP frameworks extend traditional logic programming semantics +to a distribution semantics, where the semantics of a probabilistic logic +program is given in terms of a distribution over possible models of the +program. However, the inference techniques used in these works rely on +enumerating sets of explanations for a query answer. Consequently, these +languages permit very limited use of random variables with continuous +distributions. In this paper, we present a symbolic inference procedure that +uses constraints and represents sets of explanations without enumeration. This +permits us to reason over PLPs with Gaussian or Gamma-distributed random +variables (in addition to discrete-valued random variables) and linear equality +constraints over reals. We develop the inference procedure in the context of +PRISM; however the procedure's core ideas can be easily applied to other PLP +languages as well. An interesting aspect of our inference procedure is that +PRISM's query evaluation process becomes a special case in the absence of any +continuous random variables in the program. The symbolic inference procedure +enables us to reason over complex probabilistic models such as Kalman filters +and a large subclass of Hybrid Bayesian networks that were hitherto not +possible in PLP frameworks. (To appear in Theory and Practice of Logic +Programming). +","Inference in Probabilistic Logic Programs with Continuous Random + Variables" +" We consider the task of performing probabilistic inference with probabilistic +logical models. Many algorithms for approximate inference with such models are +based on sampling. From a logic programming perspective, sampling boils down to +repeatedly calling the same queries on a knowledge base composed of a static +part and a dynamic part. The larger the static part, the more redundancy there +is in these repeated calls. This is problematic since inefficient sampling +yields poor approximations. + We show how to apply logic program specialization to make sampling-based +inference more efficient. We develop an algorithm that specializes the +definitions of the query predicates with respect to the static part of the +knowledge base. In experiments on real-world data we obtain speedups of up to +an order of magnitude, and these speedups grow with the data-size. +","Improving the Efficiency of Approximate Inference for Probabilistic + Logical Models by means of Program Specialization" +" In this paper, the continuity and strong continuity in domain-free +information algebras and labeled information algebras are introduced +respectively. A more general concept of continuous function which is defined +between two domain-free continuous information algebras is presented. It is +shown that, with the operations combination and focusing, the set of all +continuous functions between two domain-free s-continuous information algebras +forms a new s-continuous information algebra. By studying the relationship +between domain-free information algebras and labeled information algebras, it +is demonstrated that they do correspond to each other on s-compactness. +",Continuity in Information Algebras +" We study propagation of the RegularGcc global constraint. This ensures that +each row of a matrix of decision variables satisfies a Regular constraint, and +each column satisfies a Gcc constraint. On the negative side, we prove that +propagation is NP-hard even under some strong restrictions (e.g. just 3 values, +just 4 states in the automaton, or just 5 columns to the matrix). On the +positive side, we identify two cases where propagation is fixed parameter +tractable. In addition, we show how to improve propagation over a simple +decomposition into separate Regular and Gcc constraints by identifying some +necessary but insufficient conditions for a solution. We enforce these +conditions with some additional weighted row automata. Experimental results +demonstrate the potential of these methods on some standard benchmark problems. +",The RegularGcc Matrix Constraint +" Fuzzy rule based models have a capability to approximate any continuous +function to any degree of accuracy on a compact domain. The majority of FLC +design process relies on heuristic knowledge of experience operators. In order +to make the design process automatic we present a genetic approach to learn +fuzzy rules as well as membership function parameters. Moreover, several +statistical information criteria such as the Akaike information criterion +(AIC), the Bhansali-Downham information criterion (BDIC), and the +Schwarz-Rissanen information criterion (SRIC) are used to construct optimal +fuzzy models by reducing fuzzy rules. A genetic scheme is used to design +Takagi-Sugeno-Kang (TSK) model for identification of the antecedent rule +parameters and the identification of the consequent parameters. Computer +simulations are presented confirming the performance of the constructed fuzzy +logic controller. +","Optimal Fuzzy Model Construction with Statistical Information using + Genetic Algorithm" +" We mathematically model Ignacio Matte Blanco's principles of symmetric and +asymmetric being through use of an ultrametric topology. We use for this the +highly regarded 1975 book of this Chilean psychiatrist and pyschoanalyst (born +1908, died 1995). Such an ultrametric model corresponds to hierarchical +clustering in the empirical data, e.g. text. We show how an ultrametric +topology can be used as a mathematical model for the structure of the logic +that reflects or expresses Matte Blanco's symmetric being, and hence of the +reasoning and thought processes involved in conscious reasoning or in reasoning +that is lacking, perhaps entirely, in consciousness or awareness of itself. In +a companion paper we study how symmetric (in the sense of Matte Blanco's) +reasoning can be demarcated in a context of symmetric and asymmetric reasoning +provided by narrative text. +","Ultrametric Model of Mind, I: Review" +" This paper is the continuation of our research work about linguistic +truth-valued concept lattice. In order to provide a mathematical tool for +mining tacit knowledge, we establish a concrete model of 6-ary linguistic +truth-valued concept lattice and introduce a mining algorithm through the +structure consistency. Specifically, we utilize the attributes to depict +knowledge, propose the 6-ary linguistic truth-valued attribute extended context +and congener context to characterize tacit knowledge, and research the +necessary and sufficient conditions of forming tacit knowledge. We respectively +give the algorithms of generating the linguistic truth-valued congener context +and constructing the linguistic truth-valued concept lattice. +","Tacit knowledge mining algorithm based on linguistic truth-valued + concept lattice" +" Large-scale, parallel clusters composed of commodity processors are +increasingly available, enabling the use of vast processing capabilities and +distributed RAM to solve hard search problems. We investigate Hash-Distributed +A* (HDA*), a simple approach to parallel best-first search that asynchronously +distributes and schedules work among processors based on a hash function of the +search state. We use this approach to parallelize the A* algorithm in an +optimal sequential version of the Fast Downward planner, as well as a 24-puzzle +solver. The scaling behavior of HDA* is evaluated experimentally on a shared +memory, multicore machine with 8 cores, a cluster of commodity machines using +up to 64 cores, and large-scale high-performance clusters, using up to 2400 +processors. We show that this approach scales well, allowing the effective +utilization of large amounts of distributed memory to optimally solve problems +which require terabytes of RAM. We also compare HDA* to Transposition-table +Driven Scheduling (TDS), a hash-based parallelization of IDA*, and show that, +in planning, HDA* significantly outperforms TDS. A simple hybrid which combines +HDA* and TDS to exploit strengths of both algorithms is proposed and evaluated. +","Evaluation of a Simple, Scalable, Parallel Best-First Search Strategy" +" We review some existing methods for the computation of first order moments on +junction trees using Shafer-Shenoy algorithm. First, we consider the problem of +first order moments computation as vertices problem in junction trees. In this +way, the problem is solved using the memory space of an order of the junction +tree edge-set cardinality. After that, we consider two algorithms, +Lauritzen-Nilsson algorithm, and Mau\'a et al. algorithm, which computes the +first order moments as the normalization problem in junction tree, using the +memory space of an order of the junction tree leaf-set cardinality. +",The computation of first order moments on junction trees +" We present a technique for the animation of a 3D kinematic tongue model, one +component of the talking head of an acoustic-visual (AV) speech synthesizer. +The skeletal animation approach is adapted to make use of a deformable rig +controlled by tongue motion capture data obtained with electromagnetic +articulography (EMA), while the tongue surface is extracted from volumetric +magnetic resonance imaging (MRI) data. Initial results are shown and future +work outlined. +","Progress in animation of an EMA-controlled tongue model for + acoustic-visual speech synthesis" +" This paper draws on diverse areas of computer science to develop a unified +view of computation: + (1) Optimization in operations research, where a numerical objective function +is maximized under constraints, is generalized from the numerical total order +to a non-numerical partial order that can be interpreted in terms of +information. (2) Relations are generalized so that there are relations of which +the constituent tuples have numerical indexes, whereas in other relations these +indexes are variables. The distinction is essential in our definition of +constraint satisfaction problems. (3) Constraint satisfaction problems are +formulated in terms of semantics of conjunctions of atomic formulas of +predicate logic. (4) Approximation structures, which are available for several +important domains, are applied to solutions of constraint satisfaction +problems. + As application we treat constraint satisfaction problems over reals. These +cover a large part of numerical analysis, most significantly nonlinear +equations and inequalities. The chaotic algorithm analyzed in the paper +combines the efficiency of floating-point computation with the correctness +guarantees of arising from our logico-mathematical model of +constraint-satisfaction problems. +",Constraint Propagation as Information Maximization +" We built a multiagent simulation of urban traffic to model both ordinary +traffic and emergency or crisis mode traffic. This simulation first builds a +modeled road network based on detailed geographical information. On this +network, the simulation creates two populations of agents: the Transporters and +the Mobiles. Transporters embody the roads themselves; they are utilitarian and +meant to handle the low level realism of the simulation. Mobile agents embody +the vehicles that circulate on the network. They have one or several +destinations they try to reach using initially their beliefs of the structure +of the network (length of the edges, speed limits, number of lanes etc.). +Nonetheless, when confronted to a dynamic, emergent prone environment (other +vehicles, unexpectedly closed ways or lanes, traffic jams etc.), the rather +reactive agent will activate more cognitive modules to adapt its beliefs, +desires and intentions. It may change its destination(s), change the tactics +used to reach the destination (favoring less used roads, following other +agents, using general headings), etc. We describe our current validation of our +model and the next planned improvements, both in validation and in +functionalities. +",A multiagent urban traffic simulation +" After more than sixty years, Shannon's research [1-3] continues to raise +fundamental questions, such as the one formulated by Luce [4,5], which is still +unanswered: ""Why is information theory not very applicable to psychological +problems, despite apparent similarities of concepts?"" On this topic, Pinker +[6], one of the foremost defenders of the computational theory of mind [6], has +argued that thought is simply a type of computation, and that the gap between +human cognition and computational models may be illusory. In this context, in +his latest book, titled Thinking Fast and Slow [8], Kahneman [7,8] provides +further theoretical interpretation by differentiating the two assumed systems +of the cognitive functioning of the human mind. He calls them intuition (system +1) determined to be an associative (automatic, fast and perceptual) machine, +and reasoning (system 2) required to be voluntary and to operate logical- +deductively. In this paper, we propose an ansatz inspired by Ausubel's learning +theory for investigating, from the constructivist perspective [9-12], +information processing in the working memory of cognizers. Specifically, a +thought experiment is performed utilizing the mind of a dual-natured creature +known as Maxwell's demon: a tiny ""man-machine"" solely equipped with the +characteristics of system 1, which prevents it from reasoning. The calculation +presented here shows that [...]. This result indicates that when the system 2 +is shut down, both an intelligent being, as well as a binary machine, incur the +same energy cost per unit of information processed, which mathematically proves +the computational attribute of the system 1, as Kahneman [7,8] theorized. This +finding links information theory to human psychological features and opens a +new path toward the conception of a multi-bit reasoning machine. +",The thermodynamic cost of fast thought +" The holistic approach to sustainable urban planning implies using different +models in an integrated way that is capable of simulating the urban system. As +the interconnection of such models is not a trivial task, one of the key +elements that may be applied is the description of the urban geometric +properties in an ""interoperable"" way. Focusing on air quality as one of the +most pronounced urban problems, the geometric aspects of a city may be +described by objects such as those defined in CityGML, so that an appropriate +air quality model can be applied for estimating the quality of the urban air on +the basis of atmospheric flow and chemistry equations. + In this paper we first present theoretical background and motivations for the +interconnection of 3D city models and other models related to sustainable +development and urban planning. Then we present a practical experiment based on +the interconnection of CityGML with an air quality model. Our approach is based +on the creation of an ontology of air quality models and on the extension of an +ontology of urban planning process (OUPP) that acts as an ontology mediator. +",Ontologies for the Integration of Air Quality Models and 3D City Models +" In this paper, we will argue that if we want to understand the function of +the brain (or the control in the case of robots), we must understand how the +brain is embedded into the physical system, and how the organism interacts with +the real world. While embodiment has often been used in its trivial meaning, +i.e. 'intelligence requires a body', the concept has deeper and more important +implications, concerned with the relation between physical and information +(neural, control) processes. A number of case studies are presented to +illustrate the concept. These involve animals and robots and are concentrated +around locomotion, grasping, and visual perception. A theoretical scheme that +can be used to embed the diverse case studies will be presented. Finally, we +will establish a link between the low-level sensory-motor processes and +cognition. We will present an embodied view on categorization, and propose the +concepts of 'body schema' and 'forward models' as a natural extension of the +embodied approach toward first representations. +","The implications of embodiment for behavior and cognition: animal and + robotic case studies" +" This paper analyses the influence of including agents of different degrees of +intelligence in a multiagent system. The goal is to better understand how we +can develop intelligence tests that can evaluate social intelligence. We +analyse several reinforcement algorithms in several contexts of cooperation and +competition. Our experimental setting is inspired by the recently developed +Darwin-Wallace distribution. +","On the influence of intelligence in (social) intelligence testing + environments" +" Many methods have been developed to secure the network infrastructure and +communication over the Internet. Intrusion detection is a relatively new +addition to such techniques. Intrusion detection systems (IDS) are used to find +out if someone has intrusion into or is trying to get it the network. One big +problem is amount of Intrusion which is increasing day by day. We need to know +about network attack information using IDS, then analysing the effect. Due to +the nature of IDSs which are solely signature based, every new intrusion cannot +be detected; so it is important to introduce artificial intelligence (AI) +methods / techniques in IDS. Introduction of AI necessitates the importance of +normalization in intrusions. This work is focused on classification of AI based +IDS techniques which will help better design intrusion detection systems in the +future. We have also proposed a support vector machine for IDS to detect Smurf +attack with much reliable accuracy. +",Classification of artificial intelligence ids for smurf attack +" Today, University Timetabling problems are occurred annually and they are +often hard and time consuming to solve. This paper describes Hyper Heuristics +(HH) method based on Great Deluge (GD) and its variants for solving large, +highly constrained timetabling problems from different domains. Generally, in +hyper heuristic framework, there are two main stages: heuristic selection and +move acceptance. This paper emphasizes on the latter stage to develop Hyper +Heuristic (HH) framework. The main contribution of this paper is that Great +Deluge (GD) and its variants: Flex Deluge(FD), Non-linear(NLGD), Extended Great +Deluge(EGD) are used as move acceptance method in HH by combining Reinforcement +learning (RL).These HH methods are tested on exam benchmark timetabling problem +and best results and comparison analysis are reported. +","Hyper heuristic based on great deluge and its variants for exam + timetabling problem" +" Data Mining techniques plays a vital role like extraction of required +knowledge, finding unsuspected information to make strategic decision in a +novel way which in term understandable by domain experts. A generalized frame +work is proposed by considering non - domain experts during mining process for +better understanding, making better decision and better finding new patters in +case of selecting suitable data mining techniques based on the user profile by +means of intelligent agents. KEYWORDS: Data Mining Techniques, Intelligent +Agents, User Profile, Multidimensional Visualization, Knowledge Discovery. +","A framework: Cluster detection and multidimensional visualization of + automated data mining using intelligent agents" +" The First-Order Variable Elimination (FOVE) algorithm allows exact inference +to be applied directly to probabilistic relational models, and has proven to be +vastly superior to the application of standard inference methods on a grounded +propositional model. Still, FOVE operators can be applied under restricted +conditions, often forcing one to resort to propositional inference. This paper +aims to extend the applicability of FOVE by providing two new model conversion +operators: the first and the primary is joint formula conversion and the second +is just-different counting conversion. These new operations allow efficient +inference methods to be applied directly on relational models, where no +existing efficient method could be applied hitherto. In addition, aided by +these capabilities, we show how to adapt FOVE to provide exact solutions to +Maximum Expected Utility (MEU) queries over relational models for decision +under uncertainty. Experimental evaluations show our algorithms to provide +significant speedup over the alternatives. +",Extended Lifted Inference with Joint Formulas +" Bayes-optimal behavior, while well-defined, is often difficult to achieve. +Recent advances in the use of Monte-Carlo tree search (MCTS) have shown that it +is possible to act near-optimally in Markov Decision Processes (MDPs) with very +large or infinite state spaces. Bayes-optimal behavior in an unknown MDP is +equivalent to optimal behavior in the known belief-space MDP, although the size +of this belief-space MDP grows exponentially with the amount of history +retained, and is potentially infinite. We show how an agent can use one +particular MCTS algorithm, Forward Search Sparse Sampling (FSSS), in an +efficient way to act nearly Bayes-optimally for all but a polynomial number of +steps, assuming that FSSS can be used to act efficiently in any possible +underlying MDP. +","Learning is planning: near Bayes-optimal reinforcement learning via + Monte-Carlo tree search" +" Hierarchical problem abstraction, when applicable, may offer exponential +reductions in computational complexity. Previous work on coarse-to-fine dynamic +programming (CFDP) has demonstrated this possibility using state abstraction to +speed up the Viterbi algorithm. In this paper, we show how to apply temporal +abstraction to the Viterbi problem. Our algorithm uses bounds derived from +analysis of coarse timescales to prune large parts of the state trellis at +finer timescales. We demonstrate improvements of several orders of magnitude +over the standard Viterbi algorithm, as well as significant speedups over CFDP, +for problems whose state variables evolve at widely differing rates. +",A temporally abstracted Viterbi algorithm +" We propose a method called EDML for learning MAP parameters in binary +Bayesian networks under incomplete data. The method assumes Beta priors and can +be used to learn maximum likelihood parameters when the priors are +uninformative. EDML exhibits interesting behaviors, especially when compared to +EM. We introduce EDML, explain its origin, and study some of its properties +both analytically and empirically. +",EDML: A Method for Learning Parameters in Bayesian Networks +" We present a novel approach to constraint-based causal discovery, that takes +the form of straightforward logical inference, applied to a list of simple, +logical statements about causal relations that are derived directly from +observed (in)dependencies. It is both sound and complete, in the sense that all +invariant features of the corresponding partial ancestral graph (PAG) are +identified, even in the presence of latent variables and selection bias. The +approach shows that every identifiable causal relation corresponds to one of +just two fundamental forms. More importantly, as the basic building blocks of +the method do not rely on the detailed (graphical) structure of the +corresponding PAG, it opens up a range of new opportunities, including more +robust inference, detailed accountability, and application to large models. +",A Logical Characterization of Constraint-Based Causal Discovery +" The problem of learning the structure of Bayesian networks from complete +discrete data with a limit on parent set size is considered. Learning is cast +explicitly as an optimisation problem where the goal is to find a BN structure +which maximises log marginal likelihood (BDe score). Integer programming, +specifically the SCIP framework, is used to solve this optimisation problem. +Acyclicity constraints are added to the integer program (IP) during solving in +the form of cutting planes. Finding good cutting planes is the key to the +success of the approach -the search for such cutting planes is effected using a +sub-IP. Results show that this is a particularly fast method for exact BN +learning. +",Bayesian network learning with cutting planes +" When the information about uncertainty cannot be quantified in a simple, +probabilistic way, the topic of possibilistic decision theory is often a +natural one to consider. The development of possibilistic decision theory has +lead to a series of possibilistic criteria, e.g pessimistic possibilistic +qualitative utility, possibilistic likely dominance, binary possibilistic +utility and possibilistic Choquet integrals. This paper focuses on sequential +decision making in possibilistic decision trees. It proposes a complexity study +of the problem of finding an optimal strategy depending on the monotonicity +property of the optimization criteria which allows the application of dynamic +programming that offers a polytime reduction of the decision problem. It also +shows that possibilistic Choquet integrals do not satisfy this property, and +that in this case the optimization problem is NP - hard. +",On the Complexity of Decision Making in Possibilistic Decision Trees +" Probabilistic logic programs are logic programs in which some of the facts +are annotated with probabilities. Several classical probabilistic inference +tasks (such as MAP and computing marginals) have not yet received a lot of +attention for this formalism. The contribution of this paper is that we develop +efficient inference algorithms for these tasks. This is based on a conversion +of the probabilistic logic program and the query and evidence to a weighted CNF +formula. This allows us to reduce the inference tasks to well-studied tasks +such as weighted model counting. To solve such tasks, we employ +state-of-the-art methods. We consider multiple methods for the conversion of +the programs as well as for inference on the weighted CNF. The resulting +approach is evaluated experimentally and shown to improve upon the +state-of-the-art in probabilistic logic programming. +",Inference in Probabilistic Logic Programs using Weighted CNF's +" The ideas about decision making under ignorance in economics are combined +with the ideas about uncertainty representation in computer science. The +combination sheds new light on the question of how artificial agents can act in +a dynamically consistent manner. The notion of sequential consistency is +formalized by adapting the law of iterated expectation for plausibility +measures. The necessary and sufficient condition for a certainty equivalence +operator for Nehring-Puppe's preference to be sequentially consistent is given. +This result sheds light on the models of decision making under uncertainty. +",Dynamic consistency and decision making under vacuous belief +" Inference in graphical models consists of repeatedly multiplying and summing +out potentials. It is generally intractable because the derived potentials +obtained in this way can be exponentially large. Approximate inference +techniques such as belief propagation and variational methods combat this by +simplifying the derived potentials, typically by dropping variables from them. +We propose an alternate method for simplifying potentials: quantizing their +values. Quantization causes different states of a potential to have the same +value, and therefore introduces context-specific independencies that can be +exploited to represent the potential more compactly. We use algebraic decision +diagrams (ADDs) to do this efficiently. We apply quantization and ADD reduction +to variable elimination and junction tree propagation, yielding a family of +bounded approximate inference schemes. Our experimental tests show that our new +schemes significantly outperform state-of-the-art approaches on many benchmark +instances. +",Approximation by Quantization +" Many representation schemes combining first-order logic and probability have +been proposed in recent years. Progress in unifying logical and probabilistic +inference has been slower. Existing methods are mainly variants of lifted +variable elimination and belief propagation, neither of which take logical +structure into account. We propose the first method that has the full power of +both graphical model inference and first-order theorem proving (in finite +domains with Herbrand interpretations). We first define probabilistic theorem +proving, their generalization, as the problem of computing the probability of a +logical formula given the probabilities or weights of a set of formulas. We +then show how this can be reduced to the problem of lifted weighted model +counting, and develop an efficient algorithm for the latter. We prove the +correctness of this algorithm, investigate its properties, and show how it +generalizes previous approaches. Experiments show that it greatly outperforms +lifted variable elimination when logical structure is present. Finally, we +propose an algorithm for approximate probabilistic theorem proving, and show +that it can greatly outperform lifted belief propagation. +",Probabilistic Theorem Proving +" This paper presents an approach for learning to translate simple narratives, +i.e., texts (sequences of sentences) describing dynamic systems, into coherent +sequences of events without the need for labeled training data. Our approach +incorporates domain knowledge in the form of preconditions and effects of +events, and we show that it outperforms state-of-the-art supervised learning +systems on the task of reconstructing RoboCup soccer games from their +commentaries. +",Reasoning about RoboCup Soccer Narratives +" We consider how to use the Bellman residual of the dynamic programming +operator to compute suboptimality bounds for solutions to stochastic shortest +path problems. Such bounds have been previously established only in the special +case that ""all policies are proper,"" in which case the dynamic programming +operator is known to be a contraction, and have been shown to be easily +computable only in the more limited special case of discounting. Under the +condition that transition costs are positive, we show that suboptimality bounds +can be easily computed even when not all policies are proper. In the general +case when there are no restrictions on transition costs, the analysis is more +complex. But we present preliminary results that show such bounds are possible. +",Suboptimality Bounds for Stochastic Shortest Path Problems +" We study the problem of agent-based negotiation in combinatorial domains. It +is difficult to reach optimal agreements in bilateral or multi-lateral +negotiations when the agents' preferences for the possible alternatives are not +common knowledge. Self-interested agents often end up negotiating inefficient +agreements in such situations. In this paper, we present a protocol for +negotiation in combinatorial domains which can lead rational agents to reach +optimal agreements under incomplete information setting. Our proposed protocol +enables the negotiating agents to identify efficient solutions using +distributed search that visits only a small subspace of the whole outcome +space. Moreover, the proposed protocol is sufficiently general that it is +applicable to most preference representation models in combinatorial domains. +We also present results of experiments that demonstrate the feasibility and +computational efficiency of our approach. +","An Efficient Protocol for Negotiation over Combinatorial Domains with + Incomplete Information" +" We present theoretical results in terms of lower and upper bounds on the +query complexity of noisy search with comparative feedback. In this search +model, the noise in the feedback depends on the distance between query points +and the search target. Consequently, the error probability in the feedback is +not fixed but varies for the queries posed by the search algorithm. Our results +show that a target out of n items can be found in O(log n) queries. We also +show the surprising result that for k possible answers per query, the speedup +is not log k (as for k-ary search) but only log log k in some cases. +",Noisy Search with Comparative Feedback +" Situation calculus has been applied widely in artificial intelligence to +model and reason about actions and changes in dynamic systems. Since actions +carried out by agents will cause constant changes of the agents' beliefs, how +to manage these changes is a very important issue. Shapiro et al. [22] is one +of the studies that considered this issue. However, in this framework, the +problem of noisy sensing, which often presents in real-world applications, is +not considered. As a consequence, noisy sensing actions in this framework will +lead to an agent facing inconsistent situation and subsequently the agent +cannot proceed further. In this paper, we investigate how noisy sensing actions +can be handled in iterated belief change within the situation calculus +formalism. We extend the framework proposed in [22] with the capability of +managing noisy sensings. We demonstrate that an agent can still detect the +actual situation when the ratio of noisy sensing actions vs. accurate sensing +actions is limited. We prove that our framework subsumes the iterated belief +change strategy in [22] when all sensing actions are accurate. Furthermore, we +prove that our framework can adequately handle belief introspection, mistaken +beliefs, belief revision and belief update even with noisy sensing, as done in +[22] with accurate sensing actions only. +",Belief change with noisy sensing in the situation calculus +" Previous work has shown that the problem of learning the optimal structure of +a Bayesian network can be formulated as a shortest path finding problem in a +graph and solved using A* search. In this paper, we improve the scalability of +this approach by developing a memory-efficient heuristic search algorithm for +learning the structure of a Bayesian network. Instead of using A*, we propose a +frontier breadth-first branch and bound search that leverages the layered +structure of the search graph of this problem so that no more than two layers +of the graph, plus solution reconstruction information, need to be stored in +memory at a time. To further improve scalability, the algorithm stores most of +the graph in external memory, such as hard disk, when it does not fit in RAM. +Experimental results show that the resulting algorithm solves significantly +larger problems than the current state of the art. +","Improving the Scalability of Optimal Bayesian Network Learning with + External-Memory Frontier Breadth-First Branch and Bound Search" +" In this paper, we develop a qualitative theory of influence diagrams that can +be used to model and solve sequential decision making tasks when only +qualitative (or imprecise) information is available. Our approach is based on +an order-of-magnitude approximation of both probabilities and utilities and +allows for specifying partially ordered preferences via sets of utility values. +We also propose a dedicated variable elimination algorithm that can be applied +for solving order-of-magnitude influence diagrams. +",Order-of-Magnitude Influence Diagrams +" To deal with the prohibitive complexity of calculating policies in +Decentralized MDPs, researchers have proposed models that exploit structured +agent interactions. Settings where most agent actions are independent except +for few actions that affect the transitions and/or rewards of other agents can +be modeled using Event-Driven Interactions with Complex Rewards (EDI-CR). +Finding the optimal joint policy can be formulated as an optimization problem. +However, existing formulations are too verbose and/or lack optimality +guarantees. We propose a compact Mixed Integer Linear Program formulation of +EDI-CR instances. The key insight is that most action sequences of a group of +agents have the same effect on a given agent. This allows us to treat these +sequences similarly and use fewer variables. Experiments show that our +formulation is more compact and leads to faster solution times and better +solutions than existing formulations. +","Compact Mathematical Programs For DEC-MDPs With Structured Agent + Interactions" +" Markov decision processes (MDPs) are widely used in modeling decision making +problems in stochastic environments. However, precise specification of the +reward functions in MDPs is often very difficult. Recent approaches have +focused on computing an optimal policy based on the minimax regret criterion +for obtaining a robust policy under uncertainty in the reward function. One of +the core tasks in computing the minimax regret policy is to obtain the set of +all policies that can be optimal for some candidate reward function. In this +paper, we propose an efficient algorithm that exploits the geometric properties +of the reward function associated with the policies. We also present an +approximate version of the method for further speed up. We experimentally +demonstrate that our algorithm improves the performance by orders of magnitude. +",A Geometric Traversal Algorithm for Reward-Uncertain MDPs +" Hidden Markov models (HMMs) and conditional random fields (CRFs) are two +popular techniques for modeling sequential data. Inference algorithms designed +over CRFs and HMMs allow estimation of the state sequence given the +observations. In several applications, estimation of the state sequence is not +the end goal; instead the goal is to compute some function of it. In such +scenarios, estimating the state sequence by conventional inference techniques, +followed by computing the functional mapping from the estimate is not +necessarily optimal. A more formal approach is to directly infer the final +outcome from the observations. In particular, we consider the specific +instantiation of the problem where the goal is to find the state trajectories +without exact transition points and derive a novel polynomial time inference +algorithm that outperforms vanilla inference techniques. We show that this +particular problem arises commonly in many disparate applications and present +experiments on three of them: (1) Toy robot tracking; (2) Single stroke +character recognition; (3) Handwritten word recognition. +",Compressed Inference for Probabilistic Sequential Models +" Many real-world decision-theoretic planning problems can be naturally modeled +with discrete and continuous state Markov decision processes (DC-MDPs). While +previous work has addressed automated decision-theoretic planning for DCMDPs, +optimal solutions have only been defined so far for limited settings, e.g., +DC-MDPs having hyper-rectangular piecewise linear value functions. In this +work, we extend symbolic dynamic programming (SDP) techniques to provide +optimal solutions for a vastly expanded class of DCMDPs. To address the +inherent combinatorial aspects of SDP, we introduce the XADD - a continuous +variable extension of the algebraic decision diagram (ADD) - that maintains +compact representations of the exact value function. Empirically, we +demonstrate an implementation of SDP with XADDs on various DC-MDPs, showing the +first optimal automated solutions to DCMDPs with linear and nonlinear piecewise +partitioned value functions and showing the advantages of constraint-based +pruning for XADDs. +",Symbolic Dynamic Programming for Discrete and Continuous State MDPs +" Identifying and controlling bias is a key problem in empirical sciences. +Causal diagram theory provides graphical criteria for deciding whether and how +causal effects can be identified from observed (nonexperimental) data by +covariate adjustment. Here we prove equivalences between existing as well as +new criteria for adjustment and we provide a new simplified but still +equivalent notion of d-separation. These lead to efficient algorithms for two +important tasks in causal diagram analysis: (1) listing minimal covariate +adjustments (with polynomial delay); and (2) identifying the subdiagram +involved in biasing paths (in linear time). Our results improve upon existing +exponential-time solutions for these problems, enabling users to assess the +effects of covariate adjustment on diagrams with tens to hundreds of variables +interactively in real time. +",Adjustment Criteria in Causal Diagrams: An Algorithmic Perspective +" We present a distributed anytime algorithm for performing MAP inference in +graphical models. The problem is formulated as a linear programming relaxation +over the edges of a graph. The resulting program has a constraint structure +that allows application of the Dantzig-Wolfe decomposition principle. +Subprograms are defined over individual edges and can be computed in a +distributed manner. This accommodates solutions to graphs whose state space +does not fit in memory. The decomposition master program is guaranteed to +compute the optimal solution in a finite number of iterations, while the +solution converges monotonically with each iteration. Formulating the MAP +inference problem as a linear program allows additional (global) constraints to +be defined; something not possible with message passing algorithms. +Experimental results show that our algorithm's solution quality outperforms +most current algorithms and it scales well to large problems. +",Distributed Anytime MAP Inference +" Approximate Bayesian inference is NP-hard. Dagum and Luby defined the Local +Variance Bound (LVB) to measure the approximation hardness of Bayesian +inference on Bayesian networks, assuming the networks model strictly positive +joint probability distributions, i.e. zero probabilities are not permitted. +This paper introduces the k-test to measure the approximation hardness of +inference on Bayesian networks with deterministic causalities in the +probability distribution, i.e. when zero conditional probabilities are +permitted. Approximation by stochastic sampling is a widely-used inference +method that is known to suffer from inefficiencies due to sample rejection. The +k-test predicts when rejection rates of stochastic sampling a Bayesian network +will be low, modest, high, or when sampling is intractable. +","Measuring the Hardness of Stochastic Sampling on Bayesian Networks with + Deterministic Causalities: the k-Test" +" This paper investigates a new method for improving the learning algorithm of +Mixture of Experts (ME) model using a hybrid of Modified Cuckoo Search (MCS) +and Conjugate Gradient (CG) as a second order optimization technique. The CG +technique is combined with Back-Propagation (BP) algorithm to yield a much more +efficient learning algorithm for ME structure. In addition, the experts and +gating networks in enhanced model are replaced by CG based Multi-Layer +Perceptrons (MLPs) to provide faster and more accurate learning. The CG is +considerably depends on initial weights of connections of Artificial Neural +Network (ANN), so, a metaheuristic algorithm, the so-called Modified Cuckoo +Search is applied in order to select the optimal weights. The performance of +proposed method is compared with Gradient Decent Based ME (GDME) and Conjugate +Gradient Based ME (CGME) in classification and regression problems. The +experimental results show that hybrid MSC and CG based ME (MCS-CGME) has faster +convergence and better performance in utilized benchmark data sets. +","Extended Mixture of MLP Experts by Hybrid of Conjugate Gradient Method + and Modified Cuckoo Search" +" Spectrum sensing is a fundamental problem in cognitive radio. We propose a +function of covariance matrix based detection algorithm for spectrum sensing in +cognitive radio network. Monotonically increasing property of function of +matrix involving trace operation is utilized as the cornerstone for this +algorithm. The advantage of proposed algorithm is it works under extremely low +signal-to-noise ratio, like lower than -30 dB with limited sample data. +Theoretical analysis of threshold setting for the algorithm is discussed. A +performance comparison between the proposed algorithm and other +state-of-the-art methods is provided, by the simulation on captured digital +television (DTV) signal. +","Generalized FMD Detection for Spectrum Sensing Under Low Signal-to-Noise + Ratio" +" The purpose of statistical disclosure control (SDC) of microdata, a.k.a. data +anonymization or privacy-preserving data mining, is to publish data sets +containing the answers of individual respondents in such a way that the +respondents corresponding to the released records cannot be re-identified and +the released data are analytically useful. SDC methods are either based on +masking the original data, generating synthetic versions of them or creating +hybrid versions by combining original and synthetic data. The choice of SDC +methods for categorical data, especially nominal data, is much smaller than the +choice of methods for numerical data. We mitigate this problem by introducing a +numerical mapping for hierarchical nominal data which allows computing means, +variances and covariances on them. +","Marginality: a numerical mapping for enhanced treatment of nominal and + hierarchical attributes" +" The first decade of this century has seen the nascency of the first +mathematical theory of general artificial intelligence. This theory of +Universal Artificial Intelligence (UAI) has made significant contributions to +many theoretical, philosophical, and practical AI questions. In a series of +papers culminating in book (Hutter, 2005), an exciting sound and complete +mathematical model for a super intelligent agent (AIXI) has been developed and +rigorously analyzed. While nowadays most AI researchers avoid discussing +intelligence, the award-winning PhD thesis (Legg, 2008) provided the +philosophical embedding and investigated the UAI-based universal measure of +rational intelligence, which is formal, objective and non-anthropocentric. +Recently, effective approximations of AIXI have been derived and experimentally +investigated in JAIR paper (Veness et al. 2011). This practical breakthrough +has resulted in some impressive applications, finally muting earlier critique +that UAI is only a theory. For the first time, without providing any domain +knowledge, the same agent is able to self-adapt to a diverse range of +interactive environments. For instance, AIXI is able to learn from scratch to +play TicTacToe, Pacman, Kuhn Poker, and other games by trial and error, without +even providing the rules of the games. + These achievements give new hope that the grand goal of Artificial General +Intelligence is not elusive. + This article provides an informal overview of UAI in context. It attempts to +gently introduce a very theoretical, formal, and mathematical subject, and +discusses philosophical and technical ingredients, traits of intelligence, some +social questions, and the past and future of UAI. +",One Decade of Universal Artificial Intelligence +" Relational representations in reinforcement learning allow for the use of +structural information like the presence of objects and relationships between +them in the description of value functions. Through this paper, we show that +such representations allow for the inclusion of background knowledge that +qualitatively describes a state and can be used to design agents that +demonstrate learning behavior in domains with large state and actions spaces +such as computer games. +",Relational Reinforcement Learning in Infinite Mario +" In a world where communication and information sharing are at the heart of +our business, the terminology needs are most pressing. It has become imperative +to identify the terms used and defined in a consensual and coherent way while +preserving linguistic diversity. To streamline and strengthen the process of +acquisition, representation and exploitation of scenarii of train accidents, it +is necessary to harmonize and standardize the terminology used by players in +the security field. The research aims to significantly improve analytical +activities and operations of the various safety studies, by tracking the error +in system, hardware, software and human. This paper presents the contribution +of ontology to modeling scenarii for rail accidents through a knowledge model +based on a generic ontology and domain ontology. After a detailed presentation +of the state of the art material, this article presents the first results of +the developed model. +","Development of an Ontology to Assist the Modeling of Accident Scenarii + ""Application on Railroad Transport """ +" The ability to model search in a constraint solver can be an essential asset +for solving combinatorial problems. However, existing infrastructure for +defining search heuristics is often inadequate. Either modeling capabilities +are extremely limited or users are faced with a general-purpose programming +language whose features are not tailored towards writing search heuristics. As +a result, major improvements in performance may remain unexplored. + This article introduces search combinators, a lightweight and +solver-independent method that bridges the gap between a conceptually simple +modeling language for search (high-level, functional and naturally +compositional) and an efficient implementation (low-level, imperative and +highly non-modular). By allowing the user to define application-tailored search +strategies from a small set of primitives, search combinators effectively +provide a rich domain-specific language (DSL) for modeling search to the user. +Remarkably, this DSL comes at a low implementation cost to the developer of a +constraint solver. + The article discusses two modular implementation approaches and shows, by +empirical evaluation, that search combinators can be implemented without +overhead compared to a native, direct implementation in a constraint solver. +",Search Combinators +" In Multi-Source Feedback or 360 Degree Feedback, data on the performance of +an individual are collected systematically from a number of stakeholders and +are used for improving performance. The 360-Degree Feedback approach provides a +consistent management philosophy meeting the criterion outlined previously. The +360-degree feedback appraisal process describes a human resource methodology +that is frequently used for both employee appraisal and employee development. +Used in employee performance appraisals, the 360-degree feedback methodology is +differentiated from traditional, top-down appraisal methods in which the +supervisor responsible for the appraisal provides the majority of the data. +Instead it seeks to use information gained from other sources to provide a +fuller picture of employees' performances. Similarly, when this technique used +in employee development it augments employees' perceptions of training needs +with those of the people with whom they interact. The 360-degree feedback based +appraisal is a comprehensive method where in the feedback about the employee +comes from all the sources that come into contact with the employee on his/her +job. The respondents for an employee can be her/his peers, managers, +subordinates team members, customers, suppliers and vendors. Hence anyone who +comes into contact with the employee, the 360 degree appraisal has four +components that include self-appraisal, superior's appraisal, subordinate's +appraisal student's appraisal and peer's appraisal .The proposed system is an +attempt to implement the 360 degree feedback based appraisal system in +academics especially engineering colleges. +","Multi source feedback based performance appraisal system using Fuzzy + logic decision support system" +" We propose a simple method for combining together voting rules that performs +a run-off between the different winners of each voting rule. We prove that this +combinator has several good properties. For instance, even if just one of the +base voting rules has a desirable property like Condorcet consistency, the +combination inherits this property. In addition, we prove that combining voting +rules together in this way can make finding a manipulation more computationally +difficult. Finally, we study the impact of this combinator on approximation +methods that find close to optimal manipulations. +",Combining Voting Rules Together +" Languages for open-universe probabilistic models (OUPMs) can represent +situations with an unknown number of objects and iden- tity uncertainty. While +such cases arise in a wide range of important real-world appli- cations, +existing general purpose inference methods for OUPMs are far less efficient +than those available for more restricted lan- guages and model classes. This +paper goes some way to remedying this deficit by in- troducing, and proving +correct, a generaliza- tion of Gibbs sampling to partial worlds with possibly +varying model structure. Our ap- proach draws on and extends previous generic +OUPM inference methods, as well as aux- iliary variable samplers for +nonparametric mixture models. It has been implemented for BLOG, a well-known +OUPM language. Combined with compile-time optimizations, the resulting +algorithm yields very substan- tial speedups over existing methods on sev- eral +test cases, and substantially improves the practicality of OUPM languages +generally. +",Gibbs Sampling in Open-Universe Stochastic Languages +" Qualitative possibilistic networks, also known as min-based possibilistic +networks, are important tools for handling uncertain information in the +possibility theory frame- work. Despite their importance, only the junction +tree adaptation has been proposed for exact reasoning with such networks. This +paper explores alternative algorithms using compilation techniques. We first +propose possibilistic adaptations of standard compilation-based probabilistic +methods. Then, we develop a new, purely possibilistic, method based on the +transformation of the initial network into a possibilistic base. A comparative +study shows that this latter performs better than the possibilistic adap- +tations of probabilistic methods. This result is also confirmed by experimental +results. +","Compiling Possibilistic Networks: Alternative Approaches to + Possibilistic Inference" +" Possibilistic answer set programming (PASP) extends answer set programming +(ASP) by attaching to each rule a degree of certainty. While such an extension +is important from an application point of view, existing semantics are not +well-motivated, and do not always yield intuitive results. To develop a more +suitable semantics, we first introduce a characterization of answer sets of +classical ASP programs in terms of possibilistic logic where an ASP program +specifies a set of constraints on possibility distributions. This +characterization is then naturally generalized to define answer sets of PASP +programs. We furthermore provide a syntactic counterpart, leading to a +possibilistic generalization of the well-known Gelfond-Lifschitz reduct, and we +show how our framework can readily be implemented using standard ASP solvers. +",Possibilistic Answer Set Programming Revisited +" Performing sensitivity analysis for influence diagrams using the decision +circuit framework is particularly convenient, since the partial derivatives +with respect to every parameter are readily available [Bhattacharjya and +Shachter, 2007; 2008]. In this paper we present three non-linear sensitivity +analysis methods that utilize this partial derivative information and therefore +do not require re-evaluating the decision situation multiple times. +Specifically, we show how to efficiently compare strategies in decision +situations, perform sensitivity to risk aversion and compute the value of +perfect hedging [Seyller, 2008]. +",Three new sensitivity analysis methods for influence diagrams +" Many machine learning applications require the ability to learn from and +reason about noisy multi-relational data. To address this, several effective +representations have been developed that provide both a language for expressing +the structural regularities of a domain, and principled support for +probabilistic inference. In addition to these two aspects, however, many +applications also involve a third aspect-the need to reason about +similarities-which has not been directly supported in existing frameworks. This +paper introduces probabilistic similarity logic (PSL), a general-purpose +framework for joint reasoning about similarity in relational domains that +incorporates probabilistic reasoning about similarities and relational +structure in a principled way. PSL can integrate any existing domain-specific +similarity measures and also supports reasoning about similarities between sets +of entities. We provide efficient inference and learning techniques for PSL and +demonstrate its effectiveness both in common relational tasks and in settings +that require reasoning about similarity. +",Probabilistic Similarity Logic +" The Next Generation Air Transportation System will introduce new, advanced +sensor technologies into the cockpit. With the introduction of such systems, +the responsibilities of the pilot are expected to dramatically increase. In the +ALARMS (ALerting And Reasoning Management System) project for NASA, we focus on +a key challenge of this environment, the quick and efficient handling of +aircraft sensor alerts. It is infeasible to alert the pilot on the state of all +subsystems at all times. Furthermore, there is uncertainty as to the true +hazard state despite the evidence of the alerts, and there is uncertainty as to +the effect and duration of actions taken to address these alerts. This paper +reports on the first steps in the construction of an application designed to +handle Next Generation alerts. In ALARMS, we have identified 60 different +aircraft subsystems and 20 different underlying hazards. In this paper, we show +how a Bayesian network can be used to derive the state of the underlying +hazards, based on the sensor input. Then, we propose a framework whereby an +automated system can plan to address these hazards in cooperation with the +pilot, using a Time-Dependent Markov Process (TMDP). Different hazards and +pilot states will call for different alerting automation plans. We demonstrate +this emerging application of Bayesian networks and TMDPs to cockpit automation, +for a use case where a small number of hazards are present, and analyze the +resulting alerting automation policies. +","ALARMS: Alerting and Reasoning Management System for Next Generation + Aircraft Hazards" +" Relational Continuous Models (RCMs) represent joint probability densities +over attributes of objects, when the attributes have continuous domains. With +relational representations, they can model joint probability distributions over +large numbers of variables compactly in a natural way. This paper presents a +new exact lifted inference algorithm for RCMs, thus it scales up to large +models of real world applications. The algorithm applies to Relational Pairwise +Models which are (relational) products of potentials of arity 2. Our algorithm +is unique in two ways. First, it substantially improves the efficiency of +lifted inference with variables of continuous domains. When a relational model +has Gaussian potentials, it takes only linear-time compared to cubic time of +previous methods. Second, it is the first exact inference algorithm which +handles RCMs in a lifted way. The algorithm is illustrated over an example from +econometrics. Experimental results show that our algorithm outperforms both a +groundlevel inference algorithm and an algorithm built with previously-known +lifted methods. +",Lifted Inference for Relational Continuous Models +" We propose a new point-based method for approximate planning in Dec-POMDP +which outperforms the state-of-the-art approaches in terms of solution quality. +It uses a heuristic estimation of the prior probability of beliefs to choose a +bounded number of policy trees: this choice is formulated as a combinatorial +optimisation problem minimising the error induced by pruning. +",Distribution over Beliefs for Memory Bounded Dec-POMDP Planning +" Partially-Observable Markov Decision Processes (POMDPs) are typically solved +by finding an approximate global solution to a corresponding belief-MDP. In +this paper, we offer a new planning algorithm for POMDPs with continuous state, +action and observation spaces. Since such domains have an inherent notion of +locality, we can find an approximate solution using local optimization methods. +We parameterize the belief distribution as a Gaussian mixture, and use the +Extended Kalman Filter (EKF) to approximate the belief update. Since the EKF is +a first-order filter, we can marginalize over the observations analytically. By +using feedback control and state estimation during policy execution, we recover +a behavior that is effectively conditioned on incoming observations despite the +unconditioned planning. Local optimization provides no guarantees of global +optimality, but it allows us to tackle domains that are at least an order of +magnitude larger than the current state-of-the-art. We demonstrate the +scalability of our algorithm by considering a simulated hand-eye coordination +domain with 16 continuous state dimensions and 6 continuous action dimensions. +","A Scalable Method for Solving High-Dimensional Continuous POMDPs Using + Local Approximation" +" Computing the probability of a formula given the probabilities or weights +associated with other formulas is a natural extension of logical inference to +the probabilistic setting. Surprisingly, this problem has received little +attention in the literature to date, particularly considering that it includes +many standard inference problems as special cases. In this paper, we propose +two algorithms for this problem: formula decomposition and conditioning, which +is an exact method, and formula importance sampling, which is an approximate +method. The latter is, to our knowledge, the first application of model +counting to approximate probabilistic inference. Unlike conventional +variable-based algorithms, our algorithms work in the dual realm of logical +formulas. Theoretically, we show that our algorithms can greatly improve +efficiency by exploiting the structural information in the formulas. +Empirically, we show that they are indeed quite powerful, often achieving +substantial performance gains over state-of-the-art schemes. +",Formula-Based Probabilistic Inference +" Decentralized POMDPs provide an expressive framework for multi-agent +sequential decision making. While fnite-horizon DECPOMDPs have enjoyed +signifcant success, progress remains slow for the infnite-horizon case mainly +due to the inherent complexity of optimizing stochastic controllers +representing agent policies. We present a promising new class of algorithms for +the infnite-horizon case, which recasts the optimization problem as inference +in a mixture of DBNs. An attractive feature of this approach is the +straightforward adoption of existing inference techniques in DBNs for solving +DEC-POMDPs and supporting richer representations such as factored or continuous +states and actions. We also derive the Expectation Maximization (EM) algorithm +to optimize the joint policy represented as DBNs. Experiments on benchmark +domains show that EM compares favorably against the state-of-the-art solvers. +",Anytime Planning for Decentralized POMDPs using Expectation Maximization +" We describe a framework and an algorithm for solving hybrid influence +diagrams with discrete, continuous, and deterministic chance variables, and +discrete and continuous decision variables. A continuous chance variable in an +influence diagram is said to be deterministic if its conditional distributions +have zero variances. The solution algorithm is an extension of Shenoy's fusion +algorithm for discrete influence diagrams. We describe an extended +Shenoy-Shafer architecture for propagation of discrete, continuous, and utility +potentials in hybrid influence diagrams that include deterministic chance +variables. The algorithm and framework are illustrated by solving two small +examples. +",Solving Hybrid Influence Diagrams with Deterministic Variables +" The paper introduces k-bounded MAP inference, a parameterization of MAP +inference in Markov logic networks. k-Bounded MAP states are MAP states with at +most k active ground atoms of hidden (non-evidence) predicates. We present a +novel delayed column generation algorithm and provide empirical evidence that +the algorithm efficiently computes k-bounded MAP states for meaningful +real-world graph matching problems. The underlying idea is that, instead of +solving one large optimization problem, it is often more efficient to tackle +several small ones. +","A Delayed Column Generation Strategy for Exact k-Bounded MAP Inference + in Markov Logic Networks" +" Rollating walkers are popular mobility aids used by older adults to improve +balance control. There is a need to automatically recognize the activities +performed by walker users to better understand activity patterns, mobility +issues and the context in which falls are more likely to happen. We design and +compare several techniques to recognize walker related activities. A +comprehensive evaluation with control subjects and walker users from a +retirement community is presented. +","Comparative Analysis of Probabilistic Models for Activity Recognition + with an Instrumented Walker" +" Belief merging is an important but difficult problem in Artificial +Intelligence, especially when sources of information are pervaded with +uncertainty. Many merging operators have been proposed to deal with this +problem in possibilistic logic, a weighted logic which is powerful for handling +inconsistency and deal- ing with uncertainty. They often result in a +possibilistic knowledge base which is a set of weighted formulas. Although +possibilistic logic is inconsistency tolerant, it suers from the well-known +""drowning effect"". Therefore, we may still want to obtain a consistent possi- +bilistic knowledge base as the result of merg- ing. In such a case, we argue +that it is not always necessary to keep weighted informa- tion after merging. +In this paper, we define a merging operator that maps a set of pos- sibilistic +knowledge bases and a formula rep- resenting the integrity constraints to a +clas- sical knowledge base by using lexicographic ordering. We show that it +satisfies nine pos- tulates that generalize basic postulates for propositional +merging given in [11]. These postulates capture the principle of minimal change +in some sense. We then provide an algorithm for generating the resulting knowl- +edge base of our merging operator. Finally, we discuss the compatibility of our +merging operator with propositional merging and es- tablish the advantage of +our merging opera- tor over existing semantic merging operators in the +propositional case. +","Merging Knowledge Bases in Possibilistic Logic by Lexicographic + Aggregation" +" The standard coherence criterion for lower previsions is expressed using an +infinite number of linear constraints. For lower previsions that are +essentially defined on some finite set of gambles on a finite possibility +space, we present a reformulation of this criterion that only uses a finite +number of constraints. Any such lower prevision is coherent if it lies within +the convex polytope defined by these constraints. The vertices of this polytope +are the extreme coherent lower previsions for the given set of gambles. Our +reformulation makes it possible to compute them. We show how this is done and +illustrate the procedure and its results. +","Characterizing the Set of Coherent Lower Previsions with a Finite Number + of Constraints or Vertices" +" Decision circuits perform efficient evaluation of influence diagrams, +building on the ad- vances in arithmetic circuits for belief net- work +inference [Darwiche, 2003; Bhattachar- jya and Shachter, 2007]. We show how +even more compact decision circuits can be con- structed for dynamic +programming in influ- ence diagrams with separable value functions and +conditionally independent subproblems. Once a decision circuit has been +constructed based on the diagram's ""global"" graphical structure, it can be +compiled to exploit ""lo- cal"" structure for efficient evaluation and sen- +sitivity analysis. +",Dynamic programming in in uence diagrams with decision circuits +" In this paper, we present the Difference- Based Causality Learner (DBCL), an +algorithm for learning a class of discrete-time dynamic models that represents +all causation across time by means of difference equations driving change in a +system. We motivate this representation with real-world mechanical systems and +prove DBCL's correctness for learning structure from time series data, an +endeavour that is complicated by the existence of latent derivatives that have +to be detected. We also prove that, under common assumptions for causal +discovery, DBCL will identify the presence or absence of feedback loops, making +the model more useful for predicting the effects of manipulating variables when +the system is in equilibrium. We argue analytically and show empirically the +advantages of DBCL over vector autoregression (VAR) and Granger causality +models as well as modified forms of Bayesian and constraintbased structure +discovery algorithms. Finally, we show that our algorithm can discover causal +directions of alpha rhythms in human brains from EEG data. +",Learning Why Things Change: The Difference-Based Causality Learner +" We present decentralized rollout sampling policy iteration (DecRSPI) - a new +algorithm for multi-agent decision problems formalized as DEC-POMDPs. DecRSPI +is designed to improve scalability and tackle problems that lack an explicit +model. The algorithm uses Monte- Carlo methods to generate a sample of +reachable belief states. Then it computes a joint policy for each belief state +based on the rollout estimations. A new policy representation allows us to +represent solutions compactly. The key benefits of the algorithm are its linear +time complexity over the number of agents, its bounded memory usage and good +solution quality. It can solve larger problems that are intractable for +existing planning algorithms. Experimental results confirm the effectiveness +and scalability of the approach. +",Rollout Sampling Policy Iteration for Decentralized POMDPs +" A branch-and-bound approach to solving influ- ence diagrams has been +previously proposed in the literature, but appears to have never been +implemented and evaluated - apparently due to the difficulties of computing +effective bounds for the branch-and-bound search. In this paper, we describe +how to efficiently compute effective bounds, and we develop a practical +implementa- tion of depth-first branch-and-bound search for influence diagram +evaluation that outperforms existing methods for solving influence diagrams +with multiple stages. +",Solving Multistage Influence Diagrams using Branch-and-Bound Search +" Despite the intractability of generic optimal partially observable Markov +decision process planning, there exist important problems that have highly +structured models. Previous researchers have used this insight to construct +more efficient algorithms for factored domains, and for domains with +topological structure in the flat state dynamics model. In our work, motivated +by findings from the education community relevant to automated tutoring, we +consider problems that exhibit a form of topological structure in the factored +dynamics model. Our Reachable Anytime Planner for Imprecisely-sensed Domains +(RAPID) leverages this structure to efficiently compute a good initial envelope +of reachable states under the optimal MDP policy in time linear in the number +of state variables. RAPID performs partially-observable planning over the +limited envelope of states, and slowly expands the state space considered as +time allows. RAPID performs well on a large tutoring-inspired problem +simulation with 122 state variables, corresponding to a flat state space of +over 10^30 states. +",RAPID: A Reachable Anytime Planner for Imprecisely-sensed Domains +" UCT has recently emerged as an exciting new adversarial reasoning technique +based on cleverly balancing exploration and exploitation in a Monte-Carlo +sampling setting. It has been particularly successful in the game of Go but the +reasons for its success are not well understood and attempts to replicate its +success in other domains such as Chess have failed. We provide an in-depth +analysis of the potential of UCT in domain-independent settings, in cases where +heuristic values are available, and the effect of enhancing random playouts to +more informed playouts between two weak minimax players. To provide further +insights, we develop synthetic game tree instances and discuss interesting +properties of UCT, both empirically and analytically. +",Understanding Sampling Style Adversarial Search Methods +" Probabilistic Logic Programming (PLP), exemplified by Sato and Kameya's +PRISM, Poole's ICL, De Raedt et al's ProbLog and Vennekens et al's LPAD, +combines statistical and logical knowledge representation and inference. +Inference in these languages is based on enumerative construction of proofs +over logic programs. Consequently, these languages permit very limited use of +random variables with continuous distributions. In this paper, we extend PRISM +with Gaussian random variables and linear equality constraints, and consider +the problem of parameter learning in the extended language. Many statistical +models such as finite mixture models and Kalman filter can be encoded in +extended PRISM. Our EM-based learning algorithm uses a symbolic inference +procedure that represents sets of derivations without enumeration. This permits +us to learn the distribution parameters of extended PRISM programs with +discrete as well as Gaussian variables. The learning algorithm naturally +generalizes the ones used for PRISM and Hybrid Bayesian Networks. +",Parameter Learning in PRISM Programs with Continuous Random Variables +" Decision making whenever and wherever it is happened is key to organizations +success. In order to make correct decision, individuals, teams and +organizations need both knowledge management (to manage content) and +collaboration (to manage group processes) to make that more effective and +efficient. In this paper, we explain the knowledge management and collaboration +convergence. Then, we propose a formal description of mixed and multimodal +decision making (MDM) process where decision may be made by three possible +modes: individual, collective or hybrid. Finally, we explicit the MDM process +based on UML-G profile. +",Modeling of Mixed Decision Making Process +" We consider infinite-horizon $\gamma$-discounted Markov Decision Processes, +for which it is known that there exists a stationary optimal policy. We +consider the algorithm Value Iteration and the sequence of policies +$\pi_1,...,\pi_k$ it implicitely generates until some iteration $k$. We provide +performance bounds for non-stationary policies involving the last $m$ generated +policies that reduce the state-of-the-art bound for the last stationary policy +$\pi_k$ by a factor $\frac{1-\gamma}{1-\gamma^m}$. In particular, the use of +non-stationary policies allows to reduce the usual asymptotic performance +bounds of Value Iteration with errors bounded by $\epsilon$ at each iteration +from $\frac{\gamma}{(1-\gamma)^2}\epsilon$ to +$\frac{\gamma}{1-\gamma}\epsilon$, which is significant in the usual situation +when $\gamma$ is close to 1. Given Bellman operators that can only be computed +with some error $\epsilon$, a surprising consequence of this result is that the +problem of ""computing an approximately optimal non-stationary policy"" is much +simpler than that of ""computing an approximately optimal stationary policy"", +and even slightly simpler than that of ""approximately computing the value of +some fixed policy"", since this last problem only has a guarantee of +$\frac{1}{1-\gamma}\epsilon$. +","On the Use of Non-Stationary Policies for Infinite-Horizon Discounted + Markov Decision Processes" +" Informledge System (ILS) is a knowledge network with autonomous nodes and +intelligent links that integrate and structure the pieces of knowledge. In this +paper, we aim to put forward the link dynamics involved in intelligent +processing of information in ILS. There has been advancement in knowledge +management field which involve managing information in databases from a single +domain. ILS works with information from multiple domains stored in distributed +way in the autonomous nodes termed as Knowledge Network Node (KNN). Along with +the concept under consideration, KNNs store the processed information linking +concepts and processors leading to the appropriate processing of information. +","Creating Intelligent Linking for Information Threading in Knowledge + Networks" +" Expert systems use human knowledge often stored as rules within the computer +to solve problems that generally would entail human intelligence. Today, with +information systems turning out to be more pervasive and with the myriad +advances in information technologies, automating computer fault diagnosis is +becoming so fundamental that soon every enterprise has to endorse it. This +paper proposes an expert system called Expert PC Troubleshooter for diagnosing +computer problems. The system is composed of a user interface, a rule-base, an +inference engine, and an expert interface. Additionally, the system features a +fuzzy-logic module to troubleshoot POST beep errors, and an intelligent agent +that assists in the knowledge acquisition process. The proposed system is meant +to automate the maintenance, repair, and operations (MRO) process, and free-up +human technicians from manually performing routine, laborious, and +timeconsuming maintenance tasks. As future work, the proposed system is to be +parallelized so as to boost its performance and speed-up its various +operations. +",Expert PC Troubleshooter With Fuzzy-Logic And Self-Learning Support +" Some aspects of the result of applying unit resolution on a CNF formula can +be formalized as functions with domain a set of partial truth assignments. We +are interested in two ways for computing such functions, depending on whether +the result is the production of the empty clause or the assignment of a +variable with a given truth value. We show that these two models can compute +the same functions with formulae of polynomially related sizes, and we explain +how this result is related to the CNF encoding of Boolean constraints. +",Unit contradiction versus unit propagation +" The development of expert system for treatment of Diabetes disease by using +natural methods is new information technology derived from Artificial +Intelligent research using ESTA (Expert System Text Animation) System. The +proposed expert system contains knowledge about various methods of natural +treatment methods (Massage, Herbal/Proper Nutrition, Acupuncture, Gems) for +Diabetes diseases of Human Beings. The system is developed in the ESTA (Expert +System shell for Text Animation) which is Visual Prolog 7.3 Application. The +knowledge for the said system will be acquired from domain experts, texts and +other related sources. +","Development of knowledge Base Expert System for Natural treatment of + Diabetes disease" +" In this report, we will be interested at Dynamic Bayesian Network (DBNs) as a +model that tries to incorporate temporal dimension with uncertainty. We start +with basics of DBN where we especially focus in Inference and Learning concepts +and algorithms. Then we will present different levels and methods of creating +DBNs as well as approaches of incorporating temporal dimension in static +Bayesian network. +",Characterization of Dynamic Bayesian Network +" Through history, the human being tried to relay its daily tasks to other +creatures, which was the main reason behind the rise of civilizations. It +started with deploying animals to automate tasks in the field of +agriculture(bulls), transportation (e.g. horses and donkeys), and even +communication (pigeons). Millenniums after, come the Golden age with +""Al-jazari"" and other Muslim inventors, which were the pioneers of automation, +this has given birth to industrial revolution in Europe, centuries after. At +the end of the nineteenth century, a new era was to begin, the computational +era, the most advanced technological and scientific development that is driving +the mankind and the reason behind all the evolutions of science; such as +medicine, communication, education, and physics. At this edge of technology +engineers and scientists are trying to model a machine that behaves the same as +they do, which pushed us to think about designing and implementing ""Things +that-Thinks"", then artificial intelligence was. In this work we will cover each +of the major discoveries and studies in the field of machine cognition, which +are the ""Elementary Perceiver and Memorizer""(EPAM) and ""The General Problem +Solver""(GPS). The First one focus mainly on implementing the human-verbal +learning behavior, while the second one tries to model an architecture that is +able to solve problems generally (e.g. theorem proving, chess playing, and +arithmetic). We will cover the major goals and the main ideas of each model, as +well as comparing their strengths and weaknesses, and finally giving their +fields of applications. And Finally, we will suggest a real life implementation +of a cognitive machine. +",Machine Cognition Models: EPAM and GPS +" We present a system for recognising human activity given a symbolic +representation of video content. The input of our system is a set of +time-stamped short-term activities (STA) detected on video frames. The output +is a set of recognised long-term activities (LTA), which are pre-defined +temporal combinations of STA. The constraints on the STA that, if satisfied, +lead to the recognition of a LTA, have been expressed using a dialect of the +Event Calculus. In order to handle the uncertainty that naturally occurs in +human activity recognition, we adapted this dialect to a state-of-the-art +probabilistic logic programming framework. We present a detailed evaluation and +comparison of the crisp and probabilistic approaches through experimentation on +a benchmark dataset of human surveillance videos. +",A Probabilistic Logic Programming Event Calculus +" The article discusses some applications of fuzzy logic ideas to formalizing +of the Case-Based Reasoning (CBR) process and to measuring the effectiveness of +CBR systems +",Applications of fuzzy logic to Case-Based Reasoning +" One of the big challenges in the development of probabilistic relational (or +probabilistic logical) modeling and learning frameworks is the design of +inference techniques that operate on the level of the abstract model +representation language, rather than on the level of ground, propositional +instances of the model. Numerous approaches for such ""lifted inference"" +techniques have been proposed. While it has been demonstrated that these +techniques will lead to significantly more efficient inference on some specific +models, there are only very recent and still quite restricted results that show +the feasibility of lifted inference on certain syntactically defined classes of +models. Lower complexity bounds that imply some limitations for the feasibility +of lifted inference on more expressive model classes were established early on +in (Jaeger 2000). However, it is not immediate that these results also apply to +the type of modeling languages that currently receive the most attention, i.e., +weighted, quantifier-free formulas. In this paper we extend these earlier +results, and show that under the assumption that NETIME =/= ETIME, there is no +polynomial lifted inference algorithm for knowledge bases of weighted, +quantifier- and function-free formulas. Further strengthening earlier results, +this is also shown to hold for approximate inference, and for knowledge bases +not containing the equality predicate. +",Lower Complexity Bounds for Lifted Inference +" Statistical evidence of the influence of neighborhood topology on the +performance of particle swarm optimization (PSO) algorithms has been shown in +many works. However, little has been done about the implications could have the +percolation threshold in determining the topology of this neighborhood. This +work addresses this problem for individuals that, like robots, are able to +sense in a limited neighborhood around them. Based on the concept of +percolation threshold, and more precisely, the disk percolation model in 2D, we +show that better results are obtained for low values of radius, when +individuals occasionally ask others their best visited positions, with the +consequent decrease of computational complexity. On the other hand, since +percolation threshold is a universal measure, it could have a great interest to +compare the performance of different hybrid PSO algorithms. +",On how percolation threshold affects PSO performance +" The solution of the biobjective IRP is rather challenging, even for +metaheuristics. We are still lacking a profound understanding of appropriate +solution representations and effective neighborhood structures. Clearly, both +the delivery volumes and the routing aspects of the alternatives need to be +reflected in an encoding, and must be modified when searching by means of local +search. Our work contributes to the better understanding of such solution +representations. On the basis of an experimental investigation, the advantages +and drawbacks of two encodings are studied and compared. +","Solution Representations and Local Search for the bi-objective Inventory + Routing Problem" +" Today, one's disposes of large datasets composed of thousands of geographic +objects. However, for many processes, which require the appraisal of an expert +or much computational time, only a small part of these objects can be taken +into account. In this context, robust sampling methods become necessary. In +this paper, we propose a sampling method based on clustering techniques. Our +method consists in dividing the objects in clusters, then in selecting in each +cluster, the most representative objects. A case-study in the context of a +process dedicated to knowledge revision for geographic data generalisation is +presented. This case-study shows that our method allows to select relevant +samples of objects. +",Automatic Sampling of Geographic objects +" Both humans and artificial systems frequently use trial and error methods to +problem solving. In order to be effective, this type of strategy implies having +high quality control knowledge to guide the quest for the optimal solution. +Unfortunately, this control knowledge is rarely perfect. Moreover, in +artificial systems-as in humans-self-evaluation of one's own knowledge is often +difficult. Yet, this self-evaluation can be very useful to manage knowledge and +to determine when to revise it. The objective of our work is to propose an +automated approach to evaluate the quality of control knowledge in artificial +systems based on a specific trial and error strategy, namely the informed tree +search strategy. Our revision approach consists in analysing the system's +execution logs, and in using the belief theory to evaluate the global quality +of the knowledge. We present a real-world industrial application in the form of +an experiment using this approach in the domain of cartographic generalisation. +Thus far, the results of using our approach have been encouraging. +","Using Belief Theory to Diagnose Control Knowledge Quality. Application + to cartographic generalisation" +" In this paper we develop a fuzzy model for the description of the process of +Analogical Reasoning by representing its main steps as fuzzy subsets of a set +of linguistic labels characterizing the individuals' performance in each step +and we use the Shannon- Wiener diversity index as a measure of the individuals' +abilities in analogical problem solving. This model is compared with a +stochastic model presented in author's earlier papers by introducing a finite +Markov chain on the steps of the process of Analogical Reasoning. A classroom +experiment is also presented to illustrate the use of our results in practice. +",A Fuzzy Model for Analogical Problem Solving +" Without Linked Data, transport data is limited to applications exclusively +around transport. In this paper, we present a workflow for publishing and +linking transport data on the Web. So we will be able to develop transport +applications and to add other features which will be created from other +datasets. This will be possible because transport data will be linked to these +datasets. We apply this workflow to two datasets: NEPTUNE, a French standard +describing a transport line, and Passim, a directory containing relevant +information on transport services, in every French city. +",Publishing and linking transport data on the Web +" Attribute reduction is viewed as an important preprocessing step for pattern +recognition and data mining. Most of researches are focused on attribute +reduction by using rough sets. Recently, Tsang et al. discussed attribute +reduction with covering rough sets in the paper [E. C.C. Tsang, D. Chen, Daniel +S. Yeung, Approximations and reducts with covering generalized rough sets, +Computers and Mathematics with Applications 56 (2008) 279-289], where an +approach based on discernibility matrix was presented to compute all attribute +reducts. In this paper, we provide an improved approach by constructing simpler +discernibility matrix with covering rough sets, and then proceed to improve +some characterizations of attribute reduction provided by Tsang et al. It is +proved that the improved discernible matrix is equivalent to the old one, but +the computational complexity of discernible matrix is greatly reduced. +",An improved approach to attribute reduction with covering rough sets +" This is the Proceedings of the Twenty-Seventh Conference on Uncertainty in +Artificial Intelligence, which was held in Barcelona, Spain, July 14 - 17 2011. +","Proceedings of the Twenty-Seventh Conference on Uncertainty in + Artificial Intelligence (2011)" +" This is the Proceedings of the Twenty-Sixth Conference on Uncertainty in +Artificial Intelligence, which was held on Catalina Island, CA, July 8 - 11 +2010. +","Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial + Intelligence (2010)" +" Most Relevant Explanation (MRE) is a method for finding multivariate +explanations for given evidence in Bayesian networks [12]. This paper studies +the theoretical properties of MRE and develops an algorithm for finding +multiple top MRE solutions. Our study shows that MRE relies on an implicit soft +relevance measure in automatically identifying the most relevant target +variables and pruning less relevant variables from an explanation. The soft +measure also enables MRE to capture the intuitive phenomenon of explaining away +encoded in Bayesian networks. Furthermore, our study shows that the solution +space of MRE has a special lattice structure which yields interesting dominance +relations among the solutions. A K-MRE algorithm based on these dominance +relations is developed for generating a set of top solutions that are more +representative. Our empirical results show that MRE methods are promising +approaches for explanation in Bayesian networks. +","Most Relevant Explanation: Properties, Algorithms, and Evaluations" +" This paper develops an inconsistency measure on conditional probabilistic +knowledge bases. The measure is based on fundamental principles for +inconsistency measures and thus provides a solid theoretical framework for the +treatment of inconsistencies in probabilistic expert systems. We illustrate its +usefulness and immediate application on several examples and present some +formal results. Building on this measure we use the Shapley value-a well-known +solution for coalition games-to define a sophisticated indicator that is not +only able to measure inconsistencies but to reveal the causes of +inconsistencies in the knowledge base. Altogether these tools guide the +knowledge engineer in his aim to restore consistency and therefore enable him +to build a consistent and usable knowledge base that can be employed in +probabilistic expert systems. +",Measuring Inconsistency in Probabilistic Knowledge Bases +" There has been a great deal of recent interest in methods for performing +lifted inference; however, most of this work assumes that the first-order model +is given as input to the system. Here, we describe lifted inference algorithms +that determine symmetries and automatically lift the probabilistic model to +speedup inference. In particular, we describe approximate lifted inference +techniques that allow the user to trade off inference accuracy for +computational efficiency by using a handful of tunable parameters, while +keeping the error bounded. Our algorithms are closely related to the +graph-theoretic concept of bisimulation. We report experiments on both +synthetic and real data to show that in the presence of symmetries, run-times +for inference can be improved significantly, with approximate lifted inference +providing orders of magnitude speedup over ground inference. +",Bisimulation-based Approximate Lifted Inference +" The specification of aMarkov decision process (MDP) can be difficult. Reward +function specification is especially problematic; in practice, it is often +cognitively complex and time-consuming for users to precisely specify rewards. +This work casts the problem of specifying rewards as one of preference +elicitation and aims to minimize the degree of precision with which a reward +function must be specified while still allowing optimal or near-optimal +policies to be produced. We first discuss how robust policies can be computed +for MDPs given only partial reward information using the minimax regret +criterion. We then demonstrate how regret can be reduced by efficiently +eliciting reward information using bound queries, using regret-reduction as a +means for choosing suitable queries. Empirical results demonstrate that +regret-based reward elicitation offers an effective way to produce near-optimal +policies without resorting to the precise specification of the entire reward +function. +",Regret-based Reward Elicitation for Markov Decision Processes +" Logical inference algorithms for conditional independence (CI) statements +have important applications from testing consistency during knowledge +elicitation to constraintbased structure learning of graphical models. We prove +that the implication problem for CI statements is decidable, given that the +size of the domains of the random variables is known and fixed. We will present +an approximate logical inference algorithm which combines a falsification and a +novel validation algorithm. The validation algorithm represents each set of CI +statements as a sparse 0-1 matrix A and validates instances of the implication +problem by solving specific linear programs with constraint matrix A. We will +show experimentally that the algorithm is both effective and efficient in +validating and falsifying instances of the probabilistic CI implication +problem. +","Logical Inference Algorithms and Matrix Representations for + Probabilistic Conditional Independence" +" Computational analysis of time-course data with an underlying causal +structure is needed in a variety of domains, including neural spike trains, +stock price movements, and gene expression levels. However, it can be +challenging to determine from just the numerical time course data alone what is +coordinating the visible processes, to separate the underlying prima facie +causes into genuine and spurious causes and to do so with a feasible +computational complexity. For this purpose, we have been developing a novel +algorithm based on a framework that combines notions of causality in philosophy +with algorithmic approaches built on model checking and statistical techniques +for multiple hypotheses testing. The causal relationships are described in +terms of temporal logic formulae, reframing the inference problem in terms of +model checking. The logic used, PCTL, allows description of both the time +between cause and effect and the probability of this relationship being +observed. We show that equipped with these causal formulae with their +associated probabilities we may compute the average impact a cause makes to its +effect and then discover statistically significant causes through the concepts +of multiple hypothesis testing (treating each causal relationship as a +hypothesis), and false discovery control. By exploring a well-chosen family of +potentially all significant hypotheses with reasonably minimal description +length, it is possible to tame the algorithm's computational complexity while +exploring the nearly complete search-space of all prima facie causes. We have +tested these ideas in a number of domains and illustrate them here with two +examples. +",The Temporal Logic of Causal Structures +" First-order probabilistic models combine representational power of +first-order logic with graphical models. There is an ongoing effort to design +lifted inference algorithms for first-order probabilistic models. We analyze +lifted inference from the perspective of constraint processing and, through +this viewpoint, we analyze and compare existing approaches and expose their +advantages and limitations. Our theoretical results show that the wrong choice +of constraint processing method can lead to exponential increase in +computational complexity. Our empirical tests confirm the importance of +constraint processing in lifted inference. This is the first theoretical and +empirical study of constraint processing in lifted inference. +",Constraint Processing in Lifted Probabilistic Inference +" A major benefit of graphical models is that most knowledge is captured in the +model structure. Many models, however, produce inference problems with a lot of +symmetries not reflected in the graphical structure and hence not exploitable +by efficient inference techniques such as belief propagation (BP). In this +paper, we present a new and simple BP algorithm, called counting BP, that +exploits such additional symmetries. Starting from a given factor graph, +counting BP first constructs a compressed factor graph of clusternodes and +clusterfactors, corresponding to sets of nodes and factors that are +indistinguishable given the evidence. Then it runs a modified BP algorithm on +the compressed graph that is equivalent to running BP on the original factor +graph. Our experiments show that counting BP is applicable to a variety of +important AI tasks such as (dynamic) relational models and boolean model +counting, and that significant efficiency gains are obtainable, often by orders +of magnitude. +",Counting Belief Propagation +" A Bayesian belief network models a joint distribution with an directed +acyclic graph representing dependencies among variables and network parameters +characterizing conditional distributions. The parameters are viewed as random +variables to quantify uncertainty about their values. Belief nets are used to +compute responses to queries; i.e., conditional probabilities of interest. A +query is a function of the parameters, hence a random variable. Van Allen et +al. (2001, 2008) showed how to quantify uncertainty about a query via a delta +method approximation of its variance. We develop more accurate approximations +for both query mean and variance. The key idea is to extend the query mean +approximation to a ""doubled network"" involving two independent replicates. Our +method assumes complete data and can be applied to discrete, continuous, and +hybrid networks (provided discrete variables have only discrete parents). We +analyze several improvements, and provide empirical studies to demonstrate +their effectiveness. +","Improved Mean and Variance Approximations for Belief Net Responses via + Network Doubling" +" Generating optimal plans in highly dynamic environments is challenging. Plans +are predicated on an assumed initial state, but this state can change +unexpectedly during plan generation, potentially invalidating the planning +effort. In this paper we make three contributions: (1) We propose a novel +algorithm for generating optimal plans in settings where frequent, unexpected +events interfere with planning. It is able to quickly distinguish relevant from +irrelevant state changes, and to update the existing planning search tree if +necessary. (2) We argue for a new criterion for evaluating plan adaptation +techniques: the relative running time compared to the ""size"" of changes. This +is significant since during recovery more changes may occur that need to be +recovered from subsequently, and in order for this process of repeated recovery +to terminate, recovery time has to converge. (3) We show empirically that our +approach can converge and find optimal plans in environments that would +ordinarily defy planning due to their high dynamics. +",Generating Optimal Plans in Highly-Dynamic Domains +" We introduce a challenging real-world planning problem where actions must be +taken at each location in a spatial area at each point in time. We use forestry +planning as the motivating application. In Large Scale Spatial-Temporal (LSST) +planning problems, the state and action spaces are defined as the +cross-products of many local state and action spaces spread over a large +spatial area such as a city or forest. These problems possess state +uncertainty, have complex utility functions involving spatial constraints and +we generally must rely on simulations rather than an explicit transition model. +We define LSST problems as reinforcement learning problems and present a +solution using policy gradients. We compare two different policy formulations: +an explicit policy that identifies each location in space and the action to +take there; and an abstract policy that defines the proportion of actions to +take across all locations in space. We show that the abstract policy is more +robust and achieves higher rewards with far fewer parameters than the +elementary policy. This abstract policy is also a better fit to the properties +that practitioners in LSST problem domains require for such methods to be +widely useful. +","Seeing the Forest Despite the Trees: Large Scale Spatial-Temporal + Decision Making" +" This paper presents complexity analysis and variational methods for inference +in probabilistic description logics featuring Boolean operators, +quantification, qualified number restrictions, nominals, inverse roles and role +hierarchies. Inference is shown to be PEXP-complete, and variational methods +are designed so as to exploit logical inference whenever possible. +","Complexity Analysis and Variational Inference for Interpretation-based + Probabilistic Description Logic" +" Continuous-time Bayesian networks is a natural structured representation +language for multicomponent stochastic processes that evolve continuously over +time. Despite the compact representation, inference in such models is +intractable even in relatively simple structured networks. Here we introduce a +mean field variational approximation in which we use a product of inhomogeneous +Markov processes to approximate a distribution over trajectories. This +variational approach leads to a globally consistent distribution, which can be +efficiently queried. Additionally, it provides a lower bound on the probability +of observations, thus making it attractive for learning tasks. We provide the +theoretical foundations for the approximation, an efficient implementation that +exploits the wide range of highly optimized ordinary differential equations +(ODE) solvers, experimentally explore characterizations of processes for which +this approximation is suitable, and show applications to a large-scale +realworld inference problem. +","Mean Field Variational Approximation for Continuous-Time Bayesian + Networks" +" We study a subclass of POMDPs, called Deterministic POMDPs, that is +characterized by deterministic actions and observations. These models do not +provide the same generality of POMDPs yet they capture a number of interesting +and challenging problems, and permit more efficient algorithms. Indeed, some of +the recent work in planning is built around such assumptions mainly by the +quest of amenable models more expressive than the classical deterministic +models. We provide results about the fundamental properties of Deterministic +POMDPs, their relation with AND/OR search problems and algorithms, and their +computational complexity. +",Deterministic POMDPs Revisited +" We present a new method to propagate lower bounds on conditional probability +distributions in conventional Bayesian networks. Our method guarantees to +provide outer approximations of the exact lower bounds. A key advantage is that +we can use any available algorithms and tools for Bayesian networks in order to +represent and infer lower bounds. This new method yields results that are +provable exact for trees with binary variables, and results which are +competitive to existing approximations in credal networks for all other network +structures. Our method is not limited to a specific kind of network structure. +Basically, it is also not restricted to a specific kind of inference, but we +restrict our analysis to prognostic inference in this article. The +computational complexity is superior to that of other existing approaches. +","Lower Bound Bayesian Networks - An Efficient Inference of Lower Bounds + on Probability Distributions in Bayesian Networks" +" Soft sets, as a mathematical tool for dealing with uncertainty, have recently +gained considerable attention, including some successful applications in +information processing, decision, demand analysis, and forecasting. To +construct new soft sets from given soft sets, some operations on soft sets have +been proposed. Unfortunately, such operations cannot keep all classical +set-theoretic laws true for soft sets. In this paper, we redefine the +intersection, complement, and difference of soft sets and investigate the +algebraic properties of these operations along with a known union operation. We +find that the new operation system on soft sets inherits all basic properties +of operations on classical sets, which justifies our definitions. +",Operations on soft sets revisited +" Modified policy iteration (MPI) is a dynamic programming (DP) algorithm that +contains the two celebrated policy and value iteration methods. Despite its +generality, MPI has not been thoroughly studied, especially its approximation +form which is used when the state and/or action spaces are large or infinite. +In this paper, we propose three implementations of approximate MPI (AMPI) that +are extensions of well-known approximate DP algorithms: fitted-value iteration, +fitted-Q iteration, and classification-based policy iteration. We provide error +propagation analyses that unify those for approximate policy and value +iteration. On the last classification-based implementation, we develop a +finite-sample analysis that shows that MPI's main parameter allows to control +the balance between the estimation error of the classifier and the overall +value function approximation. +",Approximate Modified Policy Iteration +" Even today in Twenty First Century Handwritten communication has its own +stand and most of the times, in daily life it is globally using as means of +communication and recording the information like to be shared with others. +Challenges in handwritten characters recognition wholly lie in the variation +and distortion of handwritten characters, since different people may use +different style of handwriting, and direction to draw the same shape of the +characters of their known script. This paper demonstrates the nature of +handwritten characters, conversion of handwritten data into electronic data, +and the neural network approach to make machine capable of recognizing hand +written characters. +",Machine Recognition of Hand Written Characters using Neural Networks +" A simplified description of Fuzzy TOPSIS (Technique for Order Preference by +Similarity to Ideal Situation) is presented. We have adapted the TOPSIS +description from existing Fuzzy theory literature and distilled the bare +minimum concepts required for understanding and applying TOPSIS. An example has +been worked out to illustrate the application of TOPSIS for a multi-criteria +group decision making scenario. +",A Simplified Description of Fuzzy TOPSIS +" In order to involve user knowledge in determining equality of sets, which may +not be equal in the mathematical sense, three types of approximate (rough) +equalities were introduced by Novotny and Pawlak ([8, 9, 10]). These notions +were generalized by Tripathy, Mitra and Ojha ([13]), who introduced the +concepts of approximate (rough) equivalences of sets. Rough equivalences +capture equality of sets at a higher level than rough equalities. More +properties of these concepts were established in [14]. Combining the conditions +for the two types of approximate equalities, two more approximate equalities +were introduced by Tripathy [12] and a comparative analysis of their relative +efficiency was provided. In [15], the four types of approximate equalities were +extended by considering rough fuzzy sets instead of only rough sets. In fact +the concepts of leveled approximate equalities were introduced and properties +were studied. In this paper we proceed further by introducing and studying the +approximate equalities based on rough intuitionistic fuzzy sets instead of +rough fuzzy sets. That is we introduce the concepts of approximate +(rough)equalities of intuitionistic fuzzy sets and study their properties. We +provide some real life examples to show the applications of rough equalities of +fuzzy sets and rough equalities of intuitionistic fuzzy sets. +","Approximate Equalities on Rough Intuitionistic Fuzzy Sets and an + Analysis of Approximate Equalities" +" The causal structure of cognition can be simulated but not implemented +computationally, just as the causal structure of a comet can be simulated but +not implemented computationally. The only thing that allows us even to imagine +otherwise is that cognition, unlike a comet, is invisible (to all but the +cognizer). +",The Causal Topography of Cognition +" Within the framework proposed in this paper, we address the issue of +extending the certain networks to a fuzzy certain networks in order to cope +with a vagueness and limitations of existing models for decision under +imprecise and uncertain knowledge. This paper proposes a framework that +combines two disciplines to exploit their own advantages in uncertain and +imprecise knowledge representation problems. The framework proposed is a +possibilistic logic based one in which Bayesian nodes and their properties are +represented by local necessity-valued knowledge base. Data in properties are +interpreted as set of valuated formulas. In our contribution possibilistic +Bayesian networks have a qualitative part and a quantitative part, represented +by local knowledge bases. The general idea is to study how a fusion of these +two formalisms would permit representing compact way to solve efficiently +problems for knowledge representation. We show how to apply possibility and +necessity measures to the problem of knowledge representation with large scale +data. On the other hand fuzzification of crisp certainty degrees to fuzzy +variables improves the quality of the network and tends to bring smoothness and +robustness in the network performance. The general aim is to provide a new +approach for decision under uncertainty that combines three methodologies: +Bayesian networks certainty distribution and fuzzy logic. +","Fuzzy Knowledge Representation Based on Possibilistic and Necessary + Bayesian Networks" +" The main objective of this paper is to develop a new semantic Network +structure, based on the fuzzy sets theory, used in Artificial Intelligent +system in order to provide effective on-line assistance to users of new +technological systems. This Semantic Networks is used to describe the knowledge +of an ""ideal"" expert while fuzzy sets are used both to describe the approximate +and uncertain knowledge of novice users who intervene to match fuzzy labels of +a query with categories from an ""ideal"" expert. The technical system we +consider is a word processor software, with Objects such as ""Word"" and Goals +such as ""Cut"" or ""Copy"". We suggest to consider the set of the system's Goals +as a set of linguistic variables to which corresponds a set of possible +linguistic values based on the fuzzy set. We consider, therefore, a set of +interpretation's levels for these possible values to which corresponds a set of +membership functions. We also propose a method to measure the similarity degree +between different fuzzy linguistic variables for the partition of the semantic +network in class of similar objects to make easy the diagnosis of the user's +fuzzy queries. +","Use of Fuzzy Sets in Semantic Nets for Providing On-Line Assistance to + User of Technological Systems" +" Feature weighting is a technique used to approximate the optimal degree of +influence of individual features. This paper presents a feature weighting +method for Document Image Retrieval System (DIRS) based on keyword spotting. In +this method, we weight the feature using coefficient of multiple correlations. +Coefficient of multiple correlations can be used to describe the synthesized +effects and correlation of each feature. The aim of this paper is to show that +feature weighting increases the performance of DIRS. After applying the feature +weighting method to DIRS the average precision is 93.23% and average recall +become 98.66% respectively +","Feature Weighting for Improving Document Image Retrieval System + Performance" +" In this paper, we are trying to examine trade offs between fuzzy logic and +certain Bayesian networks and we propose to combine their respective advantages +into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of +fuzzy random variables. This paper deals with different definitions and +classifications of uncertainty, sources of uncertainty, and theories and +methodologies presented to deal with uncertainty. Fuzzification of crisp +certainty degrees to fuzzy variables improves the quality of the network and +tends to bring smoothness and robustness in the network performance. The aim is +to provide a new approach for decision under uncertainty that combines three +methodologies: Bayesian networks certainty distribution and fuzzy logic. Within +the framework proposed in this paper, we address the issue of extending the +certain networks to a fuzzy certain networks in order to cope with a vagueness +and limitations of existing models for decision under imprecise and uncertain +knowledge. +",Certain Bayesian Network based on Fuzzy knowledge Bases +" Holding commercial negotiations and selecting the best supplier in supply +chain management systems are among weaknesses of producers in production +process. Therefore, applying intelligent systems may have an effective role in +increased speed and improved quality in the selections .This paper introduces a +system which tries to trade using multi-agents systems and holding negotiations +between any agents. In this system, an intelligent agent is considered for each +segment of chains which it tries to send order and receive the response with +attendance in negotiation medium and communication with other agents .This +paper introduces how to communicate between agents, characteristics of +multi-agent and standard registration medium of each agent in the environment. +JADE (Java Application Development Environment) was used for implementation and +simulation of agents cooperation. +","An Intelligent Approach for Negotiating between chains in Supply Chain + Management Systems" +" The similarity between trajectory patterns in clustering has played an +important role in discovering movement behaviour of different groups of mobile +objects. Several approaches have been proposed to measure the similarity +between sequences in trajectory data. Most of these measures are based on +Euclidean space or on spatial network and some of them have been concerned with +temporal aspect or ordering types. However, they are not appropriate to +characteristics of spatiotemporal mobility patterns in wireless networks. In +this paper, we propose a new similarity measure for mobility patterns in +cellular space of wireless network. The framework for constructing our measure +is composed of two phases as follows. First, we present formal definitions to +capture mathematically two spatial and temporal similarity measures for +mobility patterns. And then, we define the total similarity measure by means of +a weighted combination of these similarities. The truth of the partial and +total similarity measures are proved in mathematics. Furthermore, instead of +the time interval or ordering, our work makes use of the timestamp at which two +mobility patterns share the same cell. A case study is also described to give a +comparison of the combination measure with other ones. +","A weighted combination similarity measure for mobility patterns in + wireless networks" +" Feature reduction is an important concept which is used for reducing +dimensions to decrease the computation complexity and time of classification. +Since now many approaches have been proposed for solving this problem, but +almost all of them just presented a fix output for each input dataset that some +of them aren't satisfied cases for classification. In this we proposed an +approach as processing input dataset to increase accuracy rate of each feature +extraction methods. First of all, a new concept called dispelling classes +gradually (DCG) is proposed to increase separability of classes based on their +labels. Next, this method is used to process input dataset of the feature +reduction approaches to decrease the misclassification error rate of their +outputs more than when output is achieved without any processing. In addition +our method has a good quality to collate with noise based on adapting dataset +with feature reduction approaches. In the result part, two conditions (With +process and without that) are compared to support our idea by using some of UCI +datasets. +","Dispelling Classes Gradually to Improve Quality of Feature Reduction + Approaches" +" Software aging is a phenomenon that refers to progressive performance +degradation or transient failures or even crashes in long running software +systems such as web servers. It mainly occurs due to the deterioration of +operating system resource, fragmentation and numerical error accumulation. A +primitive method to fight against software aging is software rejuvenation. +Software rejuvenation is a proactive fault management technique aimed at +cleaning up the system internal state to prevent the occurrence of more severe +crash failures in the future. It involves occasionally stopping the running +software, cleaning its internal state and restarting it. An optimized schedule +for performing the software rejuvenation has to be derived in advance because a +long running application could not be put down now and then as it may lead to +waste of cost. This paper proposes a method to derive an accurate and optimized +schedule for rejuvenation of a web server (Apache) by using Radial Basis +Function (RBF) based Feed Forward Neural Network, a variant of Artificial +Neural Networks (ANN). Aging indicators are obtained through experimental setup +involving Apache web server and clients, which acts as input to the neural +network model. This method is better than existing ones because usage of RBF +leads to better accuracy and speed in convergence. +",Software Aging Analysis of Web Server Using Neural Networks +" A software agent may be a member of a Multi-Agent System (MAS) which is +collectively performing a range of complex and intelligent tasks. In the +hospital, scheduling decisions are finding difficult to schedule because of the +dynamic changes and distribution. In order to face this problem with dynamic +changes in the hospital, a new method, Distributed Optimized Patient Scheduling +with Grouping (DOPSG) has been proposed. The goal of this method is that there +is no necessity for knowing patient agents information globally. With minimal +information this method works effectively. Scheduling problem can be solved for +multiple departments in the hospital. Patient agents have been scheduled to the +resource agent based on the patient priority to reduce the waiting time of +patient agent and to reduce idle time of resources. +",A Distributed Optimized Patient Scheduling using Partial Information +" The approach described here allows using membership function to represent +imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This +representation has a great practical interest due to the possibility to realize +on the one hand, the construction of this membership function from a simple +value expressing the degree of interpretation of an Object or a Goal as +compared to an other and on the other hand, the adjustment of the membership +function during the apprenticeship. We show, how to use these membership +functions to represent the interpretation of an Object (respectively of a Goal) +user as compared to an system Object (respectively to a Goal). We also show the +possibility to make decision for each representation of an user Object compared +to a system Object. This decision is taken by determining decision coefficient +calculates according to the nucleus of the membership function of the user +Object. +","Softening Fuzzy Knowledge Representation Tool with the Learning of New + Words in Natural Language" +" This paper presents a method of optimization, based on both Bayesian Analysis +technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical +System we use learn by interpreting an unknown word using the links created +between this new word and known words. The main link is provided by the context +of the query. When novice's query is confused with an unknown verb (goal) +applied to a known noun denoting either an object in the ideal user's Network +or an object in the user's Network, the system infer that this new verb +corresponds to one of the known goal. With the learning of new words in natural +language as the interpretation, which was produced in agreement with the user, +the system improves its representation scheme at each experiment with a new +user and, in addition, takes advantage of previous discussions with users. The +semantic Net of user objects thus obtained by these kinds of learning is not +always optimal because some relationships between couple of user objects can be +generalized and others suppressed according to values of forces that +characterize them. Indeed, to simplify the obtained Net, we propose to proceed +to an inductive Bayesian analysis, on the Net obtained from Gallois lattice. +The objective of this analysis can be seen as an operation of filtering of the +obtained descriptive graph. +","Fuzzy Knowledge Representation, Learning and Optimization with Bayesian + Analysis in Fuzzy Semantic Networks" +" The approach described here allows to use the fuzzy Object Based +Representation of imprecise and uncertain knowledge. This representation has a +great practical interest due to the possibility to realize reasoning on +classification with a fuzzy semantic network based system. For instance, the +distinction between necessary, possible and user classes allows to take into +account exceptions that may appear on fuzzy knowledge-base and facilitates +integration of user's Objects in the base. This approach describes the +theoretical aspects of the architecture of the whole experimental A.I. system +we built in order to provide effective on-line assistance to users of new +technological systems: the understanding of ""how it works"" and ""how to complete +tasks"" from queries in quite natural languages. In our model, procedural +semantic networks are used to describe the knowledge of an ""ideal"" expert while +fuzzy sets are used both to describe the approximative and uncertain knowledge +of novice users in fuzzy semantic networks which intervene to match fuzzy +labels of a query with categories from our ""ideal"" expert. +","Uncertain and Approximative Knowledge Representation to Reasoning on + Classification with a Fuzzy Networks Based System" +" Answer Set Programming (ASP) is a well-established paradigm of declarative +programming in close relationship with other declarative formalisms such as SAT +Modulo Theories, Constraint Handling Rules, FO(.), PDDL and many others. Since +its first informal editions, ASP systems have been compared in the now +well-established ASP Competition. The Third (Open) ASP Competition, as the +sequel to the ASP Competitions Series held at the University of Potsdam in +Germany (2006-2007) and at the University of Leuven in Belgium in 2009, took +place at the University of Calabria (Italy) in the first half of 2011. +Participants competed on a pre-selected collection of benchmark problems, taken +from a variety of domains as well as real world applications. The Competition +ran on two tracks: the Model and Solve (M&S) Track, based on an open problem +encoding, and open language, and open to any kind of system based on a +declarative specification paradigm; and the System Track, run on the basis of +fixed, public problem encodings, written in a standard ASP language. This paper +discusses the format of the Competition and the rationale behind it, then +reports the results for both tracks. Comparison with the second ASP competition +and state-of-the-art solutions for some of the benchmark domains is eventually +discussed. + To appear in Theory and Practice of Logic Programming (TPLP). +",The third open Answer Set Programming competition +" The paper introduces AND/OR importance sampling for probabilistic graphical +models. In contrast to importance sampling, AND/OR importance sampling caches +samples in the AND/OR space and then extracts a new sample mean from the stored +samples. We prove that AND/OR importance sampling may have lower variance than +importance sampling; thereby providing a theoretical justification for +preferring it over importance sampling. Our empirical evaluation demonstrates +that AND/OR importance sampling is far more accurate than importance sampling +in many cases. +",AND/OR Importance Sampling +" In this paper, we consider planning in stochastic shortest path (SSP) +problems, a subclass of Markov Decision Problems (MDP). We focus on medium-size +problems whose state space can be fully enumerated. This problem has numerous +important applications, such as navigation and planning under uncertainty. We +propose a new approach for constructing a multi-level hierarchy of +progressively simpler abstractions of the original problem. Once computed, the +hierarchy can be used to speed up planning by first finding a policy for the +most abstract level and then recursively refining it into a solution to the +original problem. This approach is fully automated and delivers a speed-up of +two orders of magnitude over a state-of-the-art MDP solver on sample problems +while returning near-optimal solutions. We also prove theoretical bounds on the +loss of solution optimality resulting from the use of abstractions. +","Speeding Up Planning in Markov Decision Processes via Automatically + Constructed Abstractions" +" The problem of learning discrete Bayesian networks from data is encoded as a +weighted MAX-SAT problem and the MaxWalkSat local search algorithm is used to +address it. For each dataset, the per-variable summands of the (BDeu) marginal +likelihood for different choices of parents ('family scores') are computed +prior to applying MaxWalkSat. Each permissible choice of parents for each +variable is encoded as a distinct propositional atom and the associated family +score encoded as a 'soft' weighted single-literal clause. Two approaches to +enforcing acyclicity are considered: either by encoding the ancestor relation +or by attaching a total order to each graph and encoding that. The latter +approach gives better results. Learning experiments have been conducted on 21 +synthetic datasets sampled from 7 BNs. The largest dataset has 10,000 +datapoints and 60 variables producing (for the 'ancestor' encoding) a weighted +CNF input file with 19,932 atoms and 269,367 clauses. For most datasets, +MaxWalkSat quickly finds BNs with higher BDeu score than the 'true' BN. The +effect of adding prior information is assessed. It is further shown that +Bayesian model averaging can be effected by collecting BNs generated during the +search. +",Bayesian network learning by compiling to weighted MAX-SAT +" This paper describes a new algorithm to solve the decision making problem in +Influence Diagrams based on algorithms for credal networks. Decision nodes are +associated to imprecise probability distributions and a reformulation is +introduced that finds the global maximum strategy with respect to the expected +utility. We work with Limited Memory Influence Diagrams, which generalize most +Influence Diagram proposals and handle simultaneous decisions. Besides the +global optimum method, we explore an anytime approximate solution with a +guaranteed maximum error and show that imprecise probabilities are handled in a +straightforward way. Complexity issues and experiments with random diagrams and +an effects-based military planning problem are discussed. +",Strategy Selection in Influence Diagrams using Imprecise Probabilities +" A graphical multiagent model (GMM) represents a joint distribution over the +behavior of a set of agents. One source of knowledge about agents' behavior may +come from gametheoretic analysis, as captured by several graphical game +representations developed in recent years. GMMs generalize this approach to +express arbitrary distributions, based on game descriptions or other sources of +knowledge bearing on beliefs about agent behavior. To illustrate the +flexibility of GMMs, we exhibit game-derived models that allow probabilistic +deviation from equilibrium, as well as models based on heuristic action choice. +We investigate three different methods of integrating these models into a +single model representing the combined knowledge sources. To evaluate the +predictive performance of the combined model, we treat as actual outcome the +behavior produced by a reinforcement learning process. We find that combining +the two knowledge sources, using any of the methods, provides better +predictions than either source alone. Among the combination methods, mixing +data outperforms the opinion pool and direct update methods investigated in +this empirical trial. +",Knowledge Combination in Graphical Multiagent Model +" We conjecture that the worst case number of experiments necessary and +sufficient to discover a causal graph uniquely given its observational Markov +equivalence class can be specified as a function of the largest clique in the +Markov equivalence class. We provide an algorithm that computes intervention +sets that we believe are optimal for the above task. The algorithm builds on +insights gained from the worst case analysis in Eberhardt et al. (2005) for +sequences of experiments when all possible directed acyclic graphs over N +variables are considered. A simulation suggests that our conjecture is correct. +We also show that a generalization of our conjecture to other classes of +possible graph hypotheses cannot be given easily, and in what sense the +algorithm is then no longer optimal. +",Almost Optimal Intervention Sets for Causal Discovery +" Bounded policy iteration is an approach to solving infinite-horizon POMDPs +that represents policies as stochastic finite-state controllers and iteratively +improves a controller by adjusting the parameters of each node using linear +programming. In the original algorithm, the size of the linear programs, and +thus the complexity of policy improvement, depends on the number of parameters +of each node, which grows with the size of the controller. But in practice, the +number of parameters of a node with non-zero values is often very small, and +does not grow with the size of the controller. Based on this observation, we +develop a version of bounded policy iteration that leverages the sparse +structure of a stochastic finite-state controller. In each iteration, it +improves a policy by the same amount as the original algorithm, but with much +better scalability. +",Sparse Stochastic Finite-State Controllers for POMDPs +" Approximate inference in dynamic systems is the problem of estimating the +state of the system given a sequence of actions and partial observations. High +precision estimation is fundamental in many applications like diagnosis, +natural language processing, tracking, planning, and robotics. In this paper we +present an algorithm that samples possible deterministic executions of a +probabilistic sequence. The algorithm takes advantage of a compact +representation (using first order logic) for actions and world states to +improve the precision of its estimation. Theoretical and empirical results show +that the algorithm's expected error is smaller than propositional sampling and +Sequential Monte Carlo (SMC) sampling techniques. +",Sampling First Order Logical Particles +" While known algorithms for sensitivity analysis and parameter tuning in +probabilistic networks have a running time that is exponential in the size of +the network, the exact computational complexity of these problems has not been +established as yet. In this paper we study several variants of the tuning +problem and show that these problems are NPPP-complete in general. We further +show that the problems remain NP-complete or PP-complete, for a number of +restricted variants. These complexity results provide insight in whether or not +recent achievements in sensitivity analysis and tuning can be extended to more +general, practicable methods. +","The Computational Complexity of Sensitivity Analysis and Parameter + Tuning" +" Approximate linear programming (ALP) is an efficient approach to solving +large factored Markov decision processes (MDPs). The main idea of the method is +to approximate the optimal value function by a set of basis functions and +optimize their weights by linear programming (LP). This paper proposes a new +ALP approximation. Comparing to the standard ALP formulation, we decompose the +constraint space into a set of low-dimensional spaces. This structure allows +for solving the new LP efficiently. In particular, the constraints of the LP +can be satisfied in a compact form without an exponential dependence on the +treewidth of ALP constraints. We study both practical and theoretical aspects +of the proposed approach. Moreover, we demonstrate its scale-up potential on an +MDP with more than 2^100 states. +",Partitioned Linear Programming Approximations for MDPs +" Graphical models are usually learned without regard to the cost of doing +inference with them. As a result, even if a good model is learned, it may +perform poorly at prediction, because it requires approximate inference. We +propose an alternative: learning models with a score function that directly +penalizes the cost of inference. Specifically, we learn arithmetic circuits +with a penalty on the number of edges in the circuit (in which the cost of +inference is linear). Our algorithm is equivalent to learning a Bayesian +network with context-specific independence by greedily splitting conditional +distributions, at each step scoring the candidates by compiling the resulting +network into an arithmetic circuit, and using its size as the penalty. We show +how this can be done efficiently, without compiling a circuit from scratch for +each candidate. Experiments on several real-world domains show that our +algorithm is able to learn tractable models with very large treewidth, and +yields more accurate predictions than a standard context-specific Bayesian +network learner, in far less time. +",Learning Arithmetic Circuits +" An efficient policy search algorithm should estimate the local gradient of +the objective function, with respect to the policy parameters, from as few +trials as possible. Whereas most policy search methods estimate this gradient +by observing the rewards obtained during policy trials, we show, both +theoretically and empirically, that taking into account the sensor data as well +gives better gradient estimates and hence faster learning. The reason is that +rewards obtained during policy execution vary from trial to trial due to noise +in the environment; sensor data, which correlates with the noise, can be used +to partially correct for this variation, resulting in an estimatorwith lower +variance. +",Improving Gradient Estimation by Incorporating Sensor Data +" Bayesian networks can be used to extract explanations about the observed +state of a subset of variables. In this paper, we explicate the desiderata of +an explanation and confront them with the concept of explanation proposed by +existing methods. The necessity of taking into account causal approaches when a +causal graph is available is discussed. We then introduce causal explanation +trees, based on the construction of explanation trees using the measure of +causal information ow (Ay and Polani, 2006). This approach is compared to +several other methods on known networks. +",Explanation Trees for Causal Bayesian Networks +" Model-based Bayesian reinforcement learning has generated significant +interest in the AI community as it provides an elegant solution to the optimal +exploration-exploitation tradeoff in classical reinforcement learning. +Unfortunately, the applicability of this type of approach has been limited to +small domains due to the high complexity of reasoning about the joint posterior +over model parameters. In this paper, we consider the use of factored +representations combined with online planning techniques, to improve +scalability of these methods. The main contribution of this paper is a Bayesian +framework for learning the structure and parameters of a dynamical system, +while also simultaneously planning a (near-)optimal sequence of actions. +",Model-Based Bayesian Reinforcement Learning in Large Structured Domains +" In this work we present Cutting Plane Inference (CPI), a Maximum A Posteriori +(MAP) inference method for Statistical Relational Learning. Framed in terms of +Markov Logic and inspired by the Cutting Plane Method, it can be seen as a meta +algorithm that instantiates small parts of a large and complex Markov Network +and then solves these using a conventional MAP method. We evaluate CPI on two +tasks, Semantic Role Labelling and Joint Entity Resolution, while plugging in +two different MAP inference methods: the current method of choice for MAP +inference in Markov Logic, MaxWalkSAT, and Integer Linear Programming. We +observe that when used with CPI both methods are significantly faster than when +used alone. In addition, CPI improves the accuracy of MaxWalkSAT and maintains +the exactness of Integer Linear Programming. +",Improving the Accuracy and Efficiency of MAP Inference for Markov Logic +" Deciding what to sense is a crucial task, made harder by dependencies and by +a nonadditive utility function. We develop approximation algorithms for +selecting an optimal set of measurements, under a dependency structure modeled +by a tree-shaped Bayesian network (BN). Our approach is a generalization of +composing anytime algorithm represented by conditional performance profiles. +This is done by relaxing the input monotonicity assumption, and extending the +local compilation technique to more general classes of performance profiles +(PPs). We apply the extended scheme to selecting a subset of measurements for +choosing a maximum expectation variable in a binary valued BN, and for +minimizing the worst variance in a Gaussian BN. +","Observation Subset Selection as Local Compilation of Performance + Profiles" +" This paper develops a measure for bounding the performance of AND/OR search +algorithms for solving a variety of queries over graphical models. We show how +drawing a connection to the recent notion of hypertree decompositions allows to +exploit determinism in the problem specification and produce tighter bounds. We +demonstrate on a variety of practical problem instances that we are often able +to improve upon existing bounds by several orders of magnitude. +",Bounding Search Space Size via (Hyper)tree Decompositions +" We present and evaluate new techniques for designing algorithm portfolios. In +our view, the problem has both a scheduling aspect and a machine learning +aspect. Prior work has largely addressed one of the two aspects in isolation. +Building on recent work on the scheduling aspect of the problem, we present a +technique that addresses both aspects simultaneously and has attractive +theoretical guarantees. Experimentally, we show that this technique can be used +to improve the performance of state-of-the-art algorithms for Boolean +satisfiability, zero-one integer programming, and A.I. planning. +",New Techniques for Algorithm Portfolio Design +" Numerous temporal inference tasks such as fault monitoring and anomaly +detection exhibit a persistence property: for example, if something breaks, it +stays broken until an intervention. When modeled as a Dynamic Bayesian Network, +persistence adds dependencies between adjacent time slices, often making exact +inference over time intractable using standard inference algorithms. However, +we show that persistence implies a regular structure that can be exploited for +efficient inference. We present three successively more general classes of +models: persistent causal chains (PCCs), persistent causal trees (PCTs) and +persistent polytrees (PPTs), and the corresponding exact inference algorithms +that exploit persistence. We show that analytic asymptotic bounds for our +algorithms compare favorably to junction tree inference; and we demonstrate +empirically that we can perform exact smoothing on the order of 100 times +faster than the approximate Boyen-Koller method on randomly generated instances +of persistent tree models. We also show how to handle non-persistent variables +and how persistence can be exploited effectively for approximate filtering. +",Efficient inference in persistent Dynamic Bayesian Networks +" Planning can often be simpli ed by decomposing the task into smaller tasks +arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy +discovery problem can be framed as a non-convex optimization problem. However, +the inherent computational di culty of solving such an optimization problem +makes it hard to scale to realworld problems. In another line of research, +Toussaint et al. [18] developed a method to solve planning problems by +maximumlikelihood estimation. In this paper, we show how the hierarchy +discovery problem in partially observable domains can be tackled using a +similar maximum likelihood approach. Our technique rst transforms the problem +into a dynamic Bayesian network through which a hierarchical structure can +naturally be discovered while optimizing the policy. Experimental results +demonstrate that this approach scales better than previous techniques based on +non-convex optimization. +",Hierarchical POMDP Controller Optimization by Likelihood Maximization +" We address the problem of identifying dynamic sequential plans in the +framework of causal Bayesian networks, and show that the problem is reduced to +identifying causal effects, for which there are complete identi cation +algorithms available in the literature. +",Identifying Dynamic Sequential Plans +" In this paper we introduce Refractor Importance Sampling (RIS), an +improvement to reduce error variance in Bayesian network importance sampling +propagation under evidential reasoning. We prove the existence of a collection +of importance functions that are close to the optimal importance function under +evidential reasoning. Based on this theoretic result we derive the RIS +algorithm. RIS approaches the optimal importance function by applying localized +arc changes to minimize the divergence between the evidence-adjusted importance +function and the optimal importance function. The validity and performance of +RIS is empirically tested with a large setof synthetic Bayesian networks and +two real-world networks. +",Refractor Importance Sampling +" The paper introduces a generalization for known probabilistic models such as +log-linear and graphical models, called here multiplicative models. These +models, that express probabilities via product of parameters are shown to +capture multiple forms of contextual independence between variables, including +decision graphs and noisy-OR functions. An inference algorithm for +multiplicative models is provided and its correctness is proved. The complexity +analysis of the inference algorithm uses a more refined parameter than the +tree-width of the underlying graph, and shows the computational cost does not +exceed that of the variable elimination algorithm in graphical models. The +paper ends with examples where using the new models and algorithm is +computationally beneficial. +",Inference for Multiplicative Models +" Online learning aims to perform nearly as well as the best hypothesis in +hindsight. For some hypothesis classes, though, even finding the best +hypothesis offline is challenging. In such offline cases, local search +techniques are often employed and only local optimality guaranteed. For online +decision-making with such hypothesis classes, we introduce local regret, a +generalization of regret that aims to perform nearly as well as only nearby +hypotheses. We then present a general algorithm to minimize local regret with +arbitrary locality graphs. We also show how the graph structure can be +exploited to drastically speed learning. These algorithms are then demonstrated +on a diverse set of online problems: online disjunct learning, online Max-SAT, +and online decision tree learning. +",On Local Regret +" The rules of d-separation provide a framework for deriving conditional +independence facts from model structure. However, this theory only applies to +simple directed graphical models. We introduce relational d-separation, a +theory for deriving conditional independence in relational models. We provide a +sound, complete, and computationally efficient method for relational +d-separation, and we present empirical results that demonstrate effectiveness. +",Identifying Independence in Relational Models +" Decision circuits have been developed to perform efficient evaluation of +influence diagrams [Bhattacharjya and Shachter, 2007], building on the advances +in arithmetic circuits for belief network inference [Darwiche,2003]. In the +process of model building and analysis, we perform sensitivity analysis to +understand how the optimal solution changes in response to changes in the +model. When sequential decision problems under uncertainty are represented as +decision circuits, we can exploit the efficient solution process embodied in +the decision circuit and the wealth of derivative information available to +compute the value of information for the uncertainties in the problem and the +effects of changes to model parameters on the value and the optimal strategy. +",Sensitivity analysis in decision circuits +" This is the Proceedings of the Twenty-Fifth Conference on Uncertainty in +Artificial Intelligence, which was held in Montreal, QC, Canada, June 18 - 21 +2009. +","Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial + Intelligence (2009)" +" Computing the probability of evidence even with known error bounds is +NP-hard. In this paper we address this hard problem by settling on an easier +problem. We propose an approximation which provides high confidence lower +bounds on probability of evidence but does not have any guarantees in terms of +relative or absolute error. Our proposed approximation is a randomized +importance sampling scheme that uses the Markov inequality. However, a +straight-forward application of the Markov inequality may lead to poor lower +bounds. We therefore propose several heuristic measures to improve its +performance in practice. Empirical evaluation of our scheme with state-of- +the-art lower bounding schemes reveals the promise of our approach. +","Studies in Lower Bounding Probabilities of Evidence using the Markov + Inequality" +" Choquet expected utility (CEU) is one of the most sophisticated decision +criteria used in decision theory under uncertainty. It provides a +generalisation of expected utility enhancing both descriptive and prescriptive +possibilities. In this paper, we investigate the use of CEU for path-planning +under uncertainty with a special focus on robust solutions. We first recall the +main features of the CEU model and introduce some examples showing its +descriptive potential. Then we focus on the search for Choquet-optimal paths in +multivalued implicit graphs where costs depend on different scenarios. After +discussing complexity issues, we propose two different heuristic search +algorithms to solve the problem. Finally, numerical experiments are reported, +showing the practical efficiency of the proposed algorithms. +",Search for Choquet-optimal paths under uncertainty +" The ways in which an agent's actions affect the world can often be modeled +compactly using a set of relational probabilistic planning rules. This paper +addresses the problem of learning such rule sets for multiple related tasks. We +take a hierarchical Bayesian approach, in which the system learns a prior +distribution over rule sets. We present a class of prior distributions +parameterized by a rule set prototype that is stochastically modified to +produce a task-specific rule set. We also describe a coordinate ascent +algorithm that iteratively optimizes the task-specific rule sets and the prior +distribution. Experiments using this algorithm show that transferring +information from related tasks significantly reduces the amount of training +data required to predict action effects in blocks-world domains. +",Learning Probabilistic Relational Dynamics for Multiple Tasks +" We formulate in this paper the mini-bucket algorithm for approximate +inference in terms of exact inference on an approximate model produced by +splitting nodes in a Bayesian network. The new formulation leads to a number of +theoretical and practical implications. First, we show that branchand- bound +search algorithms that use minibucket bounds may operate in a drastically +reduced search space. Second, we show that the proposed formulation inspires +new minibucket heuristics and allows us to analyze existing heuristics from a +new perspective. Finally, we show that this new formulation allows mini-bucket +approximations to benefit from recent advances in exact inference, allowing one +to significantly increase the reach of these approximations. +","Node Splitting: A Scheme for Generating Upper Bounds in Bayesian + Networks" +" We describe the semantic foundations for elicitation of generalized +additively independent (GAI) utilities using the minimax regret criterion, and +propose several new query types and strategies for this purpose. Computational +feasibility is obtained by exploiting the local GAI structure in the model. Our +results provide a practical approach for implementing preference-based +constrained configuration optimization as well as effective search in +multiattribute product databases. +",Minimax regret based elicitation of generalized additive utilities +" Although a number of related algorithms have been developed to evaluate +influence diagrams, exploiting the conditional independence in the diagram, the +exact solution has remained intractable for many important problems. In this +paper we introduce decision circuits as a means to exploit the local structure +usually found in decision problems and to improve the performance of influence +diagram analysis. This work builds on the probabilistic inference algorithms +using arithmetic circuits to represent Bayesian belief networks [Darwiche, +2003]. Once compiled, these arithmetic circuits efficiently evaluate +probabilistic queries on the belief network, and methods have been developed to +exploit both the global and local structure of the network. We show that +decision circuits can be constructed in a similar fashion and promise similar +benefits. +",Evaluating influence diagrams with decision circuits +" We present a memory-bounded optimization approach for solving +infinite-horizon decentralized POMDPs. Policies for each agent are represented +by stochastic finite state controllers. We formulate the problem of optimizing +these policies as a nonlinear program, leveraging powerful existing nonlinear +optimization techniques for solving the problem. While existing solvers only +guarantee locally optimal solutions, we show that our formulation produces +higher quality controllers than the state-of-the-art approach. We also +incorporate a shared source of randomness in the form of a correlation device +to further increase solution quality with only a limited increase in space and +time. Our experimental results show that nonlinear optimization can be used to +provide high quality, concise solutions to decentralized decision problems +under uncertainty. +",Optimizing Memory-Bounded Controllers for Decentralized POMDPs +" Most real-world dynamic systems are composed of different components that +often evolve at very different rates. In traditional temporal graphical models, +such as dynamic Bayesian networks, time is modeled at a fixed granularity, +generally selected based on the rate at which the fastest component evolves. +Inference must then be performed at this fastest granularity, potentially at +significant computational cost. Continuous Time Bayesian Networks (CTBNs) avoid +time-slicing in the representation by modeling the system as evolving +continuously over time. The expectation-propagation (EP) inference algorithm of +Nodelman et al. (2005) can then vary the inference granularity over time, but +the granularity is uniform across all parts of the system, and must be selected +in advance. In this paper, we provide a new EP algorithm that utilizes a +general cluster graph architecture where clusters contain distributions that +can overlap in both space (set of variables) and time. This architecture allows +different parts of the system to be modeled at very different time +granularities, according to their current rate of evolution. We also provide an +information-theoretic criterion for dynamically re-partitioning the clusters +during inference to tune the level of approximation to the current rate of +evolution. This avoids the need to hand-select the appropriate granularity, and +allows the granularity to adapt as information is transmitted across the +network. We present experiments demonstrating that this approach can result in +significant computational savings. +",Reasoning at the Right Time Granularity +" Compiling graphical models has recently been under intense investigation, +especially for probabilistic modeling and processing. We present here a novel +data structure for compiling weighted graphical models (in particular, +probabilistic models), called AND/OR Multi-Valued Decision Diagram (AOMDD). +This is a generalization of our previous work on constraint networks, to +weighted models. The AOMDD is based on the frameworks of AND/OR search spaces +for graphical models, and Ordered Binary Decision Diagrams (OBDD). The AOMDD is +a canonical representation of a graphical model, and its size and compilation +time are bounded exponentially by the treewidth of the graph, rather than +pathwidth as is known for OBDDs. We discuss a Variable Elimination schedule for +compilation, and present the general APPLY algorithm that combines two weighted +AOMDDs, and also present a search based method for compilation method. The +preliminary experimental evaluation is quite encouraging, showing the potential +of the AOMDD data structure. +","AND/OR Multi-Valued Decision Diagrams (AOMDDs) for Weighted Graphical + Models" +" The paper evaluates the power of best-first search over AND/OR search spaces +for solving the Most Probable Explanation (MPE) task in Bayesian networks. The +main virtue of the AND/OR representation of the search space is its sensitivity +to the structure of the problem, which can translate into significant time +savings. In recent years depth-first AND/OR Branch-and- Bound algorithms were +shown to be very effective when exploring such search spaces, especially when +using caching. Since best-first strategies are known to be superior to +depth-first when memory is utilized, exploring the best-first control strategy +is called for. The main contribution of this paper is in showing that a recent +extension of AND/OR search algorithms from depth-first Branch-and-Bound to +best-first is indeed very effective for computing the MPE in Bayesian networks. +We demonstrate empirically the superiority of the best-first search approach on +various probabilistic networks. +",Best-First AND/OR Search for Most Probable Explanations +" Searching the complete space of possible Bayesian networks is intractable for +problems of interesting size, so Bayesian network structure learning +algorithms, such as the commonly used Sparse Candidate algorithm, employ +heuristics. However, these heuristics also restrict the types of relationships +that can be learned exclusively from data. They are unable to learn +relationships that exhibit ""correlation-immunity"", such as parity. To learn +Bayesian networks in the presence of correlation-immune relationships, we +extend the Sparse Candidate algorithm with a technique called ""skewing"". This +technique uses the observation that relationships that are correlation-immune +under a specific input distribution may not be correlation-immune under +another, sufficiently different distribution. We show that by extending Sparse +Candidate with this technique we are able to discover relationships between +random variables that are approximately correlation-immune, with a +significantly lower computational cost than the alternative of considering +multiple parents of a node at a time. +",Learning Bayesian Network Structure from Correlation-Immune Data +" Survey propagation (SP) is an exciting new technique that has been remarkably +successful at solving very large hard combinatorial problems, such as +determining the satisfiability of Boolean formulas. In a promising attempt at +understanding the success of SP, it was recently shown that SP can be viewed as +a form of belief propagation, computing marginal probabilities over certain +objects called covers of a formula. This explanation was, however, shortly +dismissed by experiments suggesting that non-trivial covers simply do not exist +for large formulas. In this paper, we show that these experiments were +misleading: not only do covers exist for large hard random formulas, SP is +surprisingly accurate at computing marginals over these covers despite the +existence of many cycles in the formulas. This re-opens a potentially simpler +line of reasoning for understanding SP, in contrast to some alternative lines +of explanation that have been proposed assuming covers do not exist. +",Survey Propagation Revisited +" Relational Markov Random Fields are a general and flexible framework for +reasoning about the joint distribution over attributes of a large number of +interacting entities. The main computational difficulty in learning such models +is inference. Even when dealing with complete data, where one can summarize a +large domain by sufficient statistics, learning requires one to compute the +expectation of the sufficient statistics given different parameter choices. The +typical solution to this problem is to resort to approximate inference +procedures, such as loopy belief propagation. Although these procedures are +quite efficient, they still require computation that is on the order of the +number of interactions (or features) in the model. When learning a large +relational model over a complex domain, even such approximations require +unrealistic running time. In this paper we show that for a particular class of +relational MRFs, which have inherent symmetry, we can perform the inference +needed for learning procedures using a template-level belief propagation. This +procedure's running time is proportional to the size of the relational model +rather than the size of the domain. Moreover, we show that this computational +procedure is equivalent to sychronous loopy belief propagation. This enables a +dramatic speedup in inference and learning time. We use this procedure to learn +relational MRFs for capturing the joint distribution of large protein-protein +interaction networks. +",Template Based Inference in Symmetric Relational Markov Random Fields +" Preferences play an important role in our everyday lives. CP-networks, or +CP-nets in short, are graphical models for representing conditional qualitative +preferences under ceteris paribus (""all else being equal"") assumptions. Despite +their intuitive nature and rich representation, dominance testing with CP-nets +is computationally complex, even when the CP-nets are restricted to +binary-valued preferences. Tractable algorithms exist for binary CP-nets, but +these algorithms are incomplete for multi-valued CPnets. In this paper, we +identify a class of multivalued CP-nets, which we call more-or-less CPnets, +that have the same computational complexity as binary CP-nets. More-or-less +CP-nets exploit the monotonicity of the attribute values and use intervals to +aggregate values that induce similar preferences. We then present a search +control rule for dominance testing that effectively prunes the search space +while preserving completeness. +",More-or-Less CP-Networks +" Relational Markov Decision Processes are a useful abstraction for complex +reinforcement learning problems and stochastic planning problems. Recent work +developed representation schemes and algorithms for planning in such problems +using the value iteration algorithm. However, exact versions of more complex +algorithms, including policy iteration, have not been developed or analyzed. +The paper investigates this potential and makes several contributions. First we +observe two anomalies for relational representations showing that the value of +some policies is not well defined or cannot be calculated for restricted +representation schemes used in the literature. On the other hand, we develop a +variant of policy iteration that can get around these anomalies. The algorithm +includes an aspect of policy improvement in the process of policy evaluation +and thus differs from the original algorithm. We show that despite this +difference the algorithm converges to the optimal policy. +",Policy Iteration for Relational MDPs +" Combining first-order logic and probability has long been a goal of AI. +Markov logic (Richardson & Domingos, 2006) accomplishes this by attaching +weights to first-order formulas and viewing them as templates for features of +Markov networks. Unfortunately, it does not have the full power of first-order +logic, because it is only defined for finite domains. This paper extends Markov +logic to infinite domains, by casting it in the framework of Gibbs measures +(Georgii, 1988). We show that a Markov logic network (MLN) admits a Gibbs +measure as long as each ground atom has a finite number of neighbors. Many +interesting cases fall in this category. We also show that an MLN admits a +unique measure if the weights of its non-unit clauses are small enough. We then +examine the structure of the set of consistent measures in the non-unique case. +Many important phenomena, including systems with phase transitions, are +represented by MLNs with non-unique measures. We relate the problem of +satisfiability in first-order logic to the properties of MLN measures, and +discuss how Markov logic relates to previous infinite models. +",Markov Logic in Infinite Domains +" Counterfactual statements, e.g., ""my headache would be gone had I taken an +aspirin"" are central to scientific discourse, and are formally interpreted as +statements derived from ""alternative worlds"". However, since they invoke +hypothetical states of affairs, often incompatible with what is actually known +or observed, testing counterfactuals is fraught with conceptual and practical +difficulties. In this paper, we provide a complete characterization of +""testable counterfactuals,"" namely, counterfactual statements whose +probabilities can be inferred from physical experiments. We provide complete +procedures for discerning whether a given counterfactual is testable and, if +so, expressing its probability in terms of experimental data. +",What Counterfactuals Can Be Tested +" Memory-Bounded Dynamic Programming (MBDP) has proved extremely effective in +solving decentralized POMDPs with large horizons. We generalize the algorithm +and improve its scalability by reducing the complexity with respect to the +number of observations from exponential to polynomial. We derive error bounds +on solution quality with respect to this new approximation and analyze the +convergence behavior. To evaluate the effectiveness of the improvements, we +introduce a new, larger benchmark problem. Experimental results show that +despite the high complexity of decentralized POMDPs, scalable solution +techniques such as MBDP perform surprisingly well. +",Improved Memory-Bounded Dynamic Programming for Decentralized POMDPs +" Assistive systems for persons with cognitive disabilities (e.g. dementia) are +difficult to build due to the wide range of different approaches people can +take to accomplishing the same task, and the significant uncertainties that +arise from both the unpredictability of client's behaviours and from noise in +sensor readings. Partially observable Markov decision process (POMDP) models +have been used successfully as the reasoning engine behind such assistive +systems for small multi-step tasks such as hand washing. POMDP models are a +powerful, yet flexible framework for modelling assistance that can deal with +uncertainty and utility. Unfortunately, POMDPs usually require a very labour +intensive, manual procedure for their definition and construction. Our previous +work has described a knowledge driven method for automatically generating POMDP +activity recognition and context sensitive prompting systems for complex tasks. +We call the resulting POMDP a SNAP (SyNdetic Assistance Process). The +spreadsheet-like result of the analysis does not correspond to the POMDP model +directly and the translation to a formal POMDP representation is required. To +date, this translation had to be performed manually by a trained POMDP expert. +In this paper, we formalise and automate this translation process using a +probabilistic relational model (PRM) encoded in a relational database. We +demonstrate the method by eliciting three assistance tasks from non-experts. We +validate the resulting POMDP models using case-based simulations to show that +they are reasonable for the domains. We also show a complete case study of a +designer specifying one database, including an evaluation in a real-life +experiment with a human actor. +","Relational Approach to Knowledge Engineering for POMDP-based Assistance + Systems as a Translation of a Psychological Model" +" There are several contexts of non-monotonic reasoning where a priority +between rules is established whose purpose is preventing conflicts. + One formalism that has been widely employed for non-monotonic reasoning is +the sceptical one known as Defeasible Logic. In Defeasible Logic the tool used +for conflict resolution is a preference relation between rules, that +establishes the priority among them. + In this paper we investigate how to modify such a preference relation in a +defeasible logic theory in order to change the conclusions of the theory +itself. We argue that the approach we adopt is applicable to legal reasoning +where users, in general, cannot change facts or rules, but can propose their +preferences about the relative strength of the rules. + We provide a comprehensive study of the possible combinatorial cases and we +identify and analyse the cases where the revision process is successful. + After this analysis, we identify three revision/update operators and study +them against the AGM postulates for belief revision operators, to discover that +only a part of these postulates are satisfied by the three operators. +",Revision of Defeasible Logic Preferences +" We apply decision theoretic techniques to construct non-player characters +that are able to assist a human player in collaborative games. The method is +based on solving Markov decision processes, which can be difficult when the +game state is described by many variables. To scale to more complex games, the +method allows decomposition of a game task into subtasks, each of which can be +modelled by a Markov decision process. Intention recognition is used to infer +the subtask that the human is currently performing, allowing the helper to +assist the human in performing the correct task. Experiments show that the +method can be effective, giving near-human level performance in helping a human +in a collaborative game. +",CAPIR: Collaborative Action Planning with Intention Recognition +" We consider the problem of using a heuristic policy to improve the value +approximation by the Upper Confidence Bound applied in Trees (UCT) algorithm in +non-adversarial settings such as planning with large-state space Markov +Decision Processes. Current improvements to UCT focus on either changing the +action selection formula at the internal nodes or the rollout policy at the +leaf nodes of the search tree. In this work, we propose to add an auxiliary arm +to each of the internal nodes, and always use the heuristic policy to roll out +simulations at the auxiliary arms. The method aims to get fast convergence to +optimal values at states where the heuristic policy is optimal, while retaining +similar approximation as the original UCT in other states. We show that +bootstrapping with the proposed method in the new algorithm, UCT-Aux, performs +better compared to the original UCT algorithm and its variants in two benchmark +experiment settings. We also examine conditions under which UCT-Aux works well. +",Bootstrapping Monte Carlo Tree Search with an Imperfect Heuristic +" We consider in this paper the formulation of approximate inference in +Bayesian networks as a problem of exact inference on an approximate network +that results from deleting edges (to reduce treewidth). We have shown in +earlier work that deleting edges calls for introducing auxiliary network +parameters to compensate for lost dependencies, and proposed intuitive +conditions for determining these parameters. We have also shown that our method +corresponds to IBP when enough edges are deleted to yield a polytree, and +corresponds to some generalizations of IBP when fewer edges are deleted. In +this paper, we propose a different criteria for determining auxiliary +parameters based on optimizing the KL-divergence between the original and +approximate networks. We discuss the relationship between the two methods for +selecting parameters, shedding new light on IBP and its generalizations. We +also discuss the application of our new method to approximating inference +problems which are exponential in constrained treewidth, including MAP and +nonmyopic value of information. +","A Variational Approach for Approximating Bayesian Networks by Edge + Deletion" +" In Bayesian networks, a Most Probable Explanation (MPE) is a complete +variable instantiation with a highest probability given the current evidence. +In this paper, we discuss the problem of finding robustness conditions of the +MPE under single parameter changes. Specifically, we ask the question: How much +change in a single network parameter can we afford to apply while keeping the +MPE unchanged? We will describe a procedure, which is the first of its kind, +that computes this answer for each parameter in the Bayesian network variable +in time O(n exp(w)), where n is the number of network variables and w is its +treewidth. +",On the Robustness of Most Probable Explanations +" The paper analyzes theoretically and empirically the performance of +likelihood weighting (LW) on a subset of nodes in Bayesian networks. The +proposed scheme requires fewer samples to converge due to reduction in sampling +variance. The method exploits the structure of the network to bound the +complexity of exact inference used to compute sampling distributions, similar +to Gibbs cutset sampling. Yet, the extension of the previosly proposed cutset +sampling principles to likelihood weighting is non-trivial due to differences +in the sampling processes of Gibbs sampler and LW. We demonstrate empirically +that likelihood weighting on a cutset (LWLC) is effective time-wise and has a +lower rejection rate than LW when applied to networks with many deterministic +probabilities. Finally, we show that the performance of likelihood weighting on +a cutset can be improved further by caching computed sampling distributions +and, consequently, learning 'zeros' of the target distribution. +",Cutset Sampling with Likelihood Weighting +" Linear-time computational techniques have been developed for combining +evidence which is available on a number of contending hypotheses. They offer a +means of making the computation-intensive calculations involved more efficient +in certain circumstances. Unfortunately, they restrict the orthogonal sum of +evidential functions to the dichotomous structure applies only to elements and +their complements. In this paper, we present a novel evidence structure in +terms of a triplet and a set of algorithms for evidential reasoning. The merit +of this structure is that it divides a set of evidence into three subsets, +distinguishing trivial evidential elements from important ones focusing some +particular elements. It avoids the deficits of the dichotomous structure in +representing the preference of evidence and estimating the basic probability +assignment of evidence. We have established a formalism for this structure and +the general formulae for combining pieces of evidence in the form of the +triplet, which have been theoretically justified. +",An Efficient Triplet-based Algorithm for Evidential Reasoning +" Separable Bayesian Networks, or the Influence Model, are dynamic Bayesian +Networks in which the conditional probability distribution can be separated +into a function of only the marginal distribution of a node's neighbors, +instead of the joint distributions. In terms of modeling, separable networks +has rendered possible siginificant reduction in complexity, as the state space +is only linear in the number of variables on the network, in contrast to a +typical state space which is exponential. In this work, We describe the +connection between an arbitrary Conditional Probability Table (CPT) and +separable systems using linear algebra. We give an alternate proof on the +equivalence of sufficiency and separability. We present a computational method +for testing whether a given CPT is separable. +",Linear Algebra Approach to Separable Bayesian Networks +" This paper is concerned with graphical criteria that can be used to solve the +problem of identifying casual effects from nonexperimental data in a causal +Bayesian network structure, i.e., a directed acyclic graph that represents +causal relationships. We first review Pearl's work on this topic [Pearl, 1995], +in which several useful graphical criteria are presented. Then we present a +complete algorithm [Huang and Valtorta, 2006b] for the identifiability problem. +By exploiting the completeness of this algorithm, we prove that the three basic +do-calculus rules that Pearl presents are complete, in the sense that, if a +causal effect is identifiable, there exists a sequence of applications of the +rules of the do-calculus that transforms the causal effect formula into a +formula that only includes observational quantities. +",Pearl's Calculus of Intervention Is Complete +" This paper studies a new and more general axiomatization than one presented +previously for preference on likelihood gambles. Likelihood gambles describe +actions in a situation where a decision maker knows multiple probabilistic +models and a random sample generated from one of those models but does not know +prior probability of models. This new axiom system is inspired by Jensen's +axiomatization of probabilistic gambles. Our approach provides a new +perspective to the role of data in decision making under ambiguity. It avoids +one of the most controversial issue of Bayesian methodology namely the +assumption of prior probability. +",A new axiomatization for likelihood gambles +" Continuous-time Bayesian networks (CTBNs) are graphical representations of +multi-component continuous-time Markov processes as directed graphs. The edges +in the network represent direct influences among components. The joint rate +matrix of the multi-component process is specified by means of conditional rate +matrices for each component separately. This paper addresses the situation +where some of the components evolve on a time scale that is much shorter +compared to the time scale of the other components. In this paper, we prove +that in the limit where the separation of scales is infinite, the Markov +process converges (in distribution, or weakly) to a reduced, or effective +Markov process that only involves the slow components. We also demonstrate that +for reasonable separation of scale (an order of magnitude) the reduced process +is a good approximation of the marginal process over the slow components. We +provide a simple procedure for building a reduced CTBN for this effective +process, with conditional rate matrices that can be directly calculated from +the original CTBN, and discuss the implications for approximate reasoning in +large systems. +","Dimension Reduction in Singularly Perturbed Continuous-Time Bayesian + Networks" +" A popular approach to solving large probabilistic systems relies on +aggregating states based on a measure of similarity. Many approaches in the +literature are heuristic. A number of recent methods rely instead on metrics +based on the notion of bisimulation, or behavioral equivalence between states +(Givan et al, 2001, 2003; Ferns et al, 2004). An integral component of such +metrics is the Kantorovich metric between probability distributions. However, +while this metric enables many satisfying theoretical properties, it is costly +to compute in practice. In this paper, we use techniques from network +optimization and statistical sampling to overcome this problem. We obtain in +this manner a variety of distance functions for MDP state aggregation, which +differ in the tradeoff between time and space complexity, as well as the +quality of the aggregation. We provide an empirical evaluation of these +trade-offs. +",Methods for computing state similarity in Markov Decision Processes +" Inference for probabilistic graphical models is still very much a practical +challenge in large domains. The commonly used and effective belief propagation +(BP) algorithm and its generalizations often do not converge when applied to +hard, real-life inference tasks. While it is widely recognized that the +scheduling of messages in these algorithms may have significant consequences, +this issue remains largely unexplored. In this work, we address the question of +how to schedule messages for asynchronous propagation so that a fixed point is +reached faster and more often. We first show that any reasonable asynchronous +BP converges to a unique fixed point under conditions similar to those that +guarantee convergence of synchronous BP. In addition, we show that the +convergence rate of a simple round-robin schedule is at least as good as that +of synchronous propagation. We then propose residual belief propagation (RBP), +a novel, easy-to-implement, asynchronous propagation algorithm that schedules +messages in an informed way, that pushes down a bound on the distance from the +fixed point. Finally, we demonstrate the superiority of RBP over +state-of-the-art methods for a variety of challenging synthetic and real-life +problems: RBP converges significantly more often than other methods; and it +significantly reduces running time until convergence, even when other methods +converge. +","Residual Belief Propagation: Informed Scheduling for Asynchronous + Message Passing" +" Directed possibly cyclic graphs have been proposed by Didelez (2000) and +Nodelmann et al. (2002) in order to represent the dynamic dependencies among +stochastic processes. These dependencies are based on a generalization of +Granger-causality to continuous time, first developed by Schweder (1970) for +Markov processes, who called them local dependencies. They deserve special +attention as they are asymmetric unlike stochastic (in)dependence. In this +paper we focus on their graphical representation and develop a suitable, i.e. +asymmetric notion of separation, called delta-separation. The properties of +this graph separation as well as of local independence are investigated in +detail within a framework of asymmetric (semi)graphoids allowing a deeper +insight into what information can be read off these graphs. +",Asymmetric separation for local independence graphs +" There exist several architectures to solve influence diagrams using local +computations, such as the Shenoy-Shafer, the HUGIN, or the Lazy Propagation +architectures. They all extend usual variable elimination algorithms thanks to +the use of so-called 'potentials'. In this paper, we introduce a new +architecture, called the Multi-operator Cluster DAG architecture, which can +produce decompositions with an improved constrained induced-width, and +therefore induce potentially exponential gains. Its principle is to benefit +from the composite nature of influence diagrams, instead of using uniform +potentials, in order to better analyze the problem structure. +",From influence diagrams to multi-operator cluster DAGs +" Tasks such as record linkage and multi-target tracking, which involve +reconstructing the set of objects that underlie some observed data, are +particularly challenging for probabilistic inference. Recent work has achieved +efficient and accurate inference on such problems using Markov chain Monte +Carlo (MCMC) techniques with customized proposal distributions. Currently, +implementing such a system requires coding MCMC state representations and +acceptance probability calculations that are specific to a particular +application. An alternative approach, which we pursue in this paper, is to use +a general-purpose probabilistic modeling language (such as BLOG) and a generic +Metropolis-Hastings MCMC algorithm that supports user-supplied proposal +distributions. Our algorithm gains flexibility by using MCMC states that are +only partial descriptions of possible worlds; we provide conditions under which +MCMC over partial worlds yields correct answers to queries. We also show how to +use a context-specific Bayes net to identify the factors in the acceptance +probability that need to be computed for a given proposed move. Experimental +results on a citation matching task show that our general-purpose MCMC engine +compares favorably with an application-specific system. +",General-Purpose MCMC Inference over Relational Structures +" In recent years Bayesian networks (BNs) with a mixture of continuous and +discrete variables have received an increasing level of attention. We present +an architecture for exact belief update in Conditional Linear Gaussian BNs (CLG +BNs). The architecture is an extension of lazy propagation using operations of +Lauritzen & Jensen [6] and Cowell [2]. By decomposing clique and separator +potentials into sets of factors, the proposed architecture takes advantage of +independence and irrelevance properties induced by the structure of the graph +and the evidence. The resulting benefits are illustrated by examples. Results +of a preliminary empirical performance evaluation indicate a significant +potential of the proposed architecture. +",Belief Update in CLG Bayesian Networks With Lazy Propagation +" We set up a model for reasoning about metric spaces with belief theoretic +measures. The uncertainty in these spaces stems from both probability and +metric. To represent both aspect of uncertainty, we choose an expected distance +function as a measure of uncertainty. A formal logical system is constructed +for the reasoning about expected distance. Soundness and completeness are shown +for this logic. For reasoning on product metric space with uncertainty, a new +metric is defined and shown to have good properties. +",Reasoning about Uncertainty in Metric Spaces +" The National Airspace System (NAS) is a large and complex system with +thousands of interrelated components: administration, control centers, +airports, airlines, aircraft, passengers, etc. The complexity of the NAS +creates many difficulties in management and control. One of the most pressing +problems is flight delay. Delay creates high cost to airlines, complaints from +passengers, and difficulties for airport operations. As demand on the system +increases, the delay problem becomes more and more prominent. For this reason, +it is essential for the Federal Aviation Administration to understand the +causes of delay and to find ways to reduce delay. Major contributing factors to +delay are congestion at the origin airport, weather, increasing demand, and air +traffic management (ATM) decisions such as the Ground Delay Programs (GDP). +Delay is an inherently stochastic phenomenon. Even if all known causal factors +could be accounted for, macro-level national airspace system (NAS) delays could +not be predicted with certainty from micro-level aircraft information. This +paper presents a stochastic model that uses Bayesian Networks (BNs) to model +the relationships among different components of aircraft delay and the causal +factors that affect delays. A case study on delays of departure flights from +Chicago O'Hare international airport (ORD) to Hartsfield-Jackson Atlanta +International Airport (ATL) reveals how local and system level environmental +and human-caused factors combine to affect components of delay, and how these +components contribute to the final arrival delay at the destination airport. +",Propagation of Delays in the National Airspace System +" We introduce a new dynamic model with the capability of recognizing both +activities that an individual is performing as well as where that ndividual is +located. Our model is novel in that it utilizes a dynamic graphical model to +jointly estimate both activity and spatial context over time based on the +simultaneous use of asynchronous observations consisting of GPS measurements, +and measurements from a small mountable sensor board. Joint inference is quite +desirable as it has the ability to improve accuracy of the model. A key goal, +however, in designing our overall system is to be able to perform accurate +inference decisions while minimizing the amount of hardware an individual must +wear. This minimization leads to greater comfort and flexibility, decreased +power requirements and therefore increased battery life, and reduced cost. We +show results indicating that our joint measurement model outperforms +measurements from either the sensor board or GPS alone, using two types of +probabilistic inference procedures, namely particle filtering and pruned exact +inference. +",Recognizing Activities and Spatial Context Using Wearable Sensors +" We study the problem of learning the best Bayesian network structure with +respect to a decomposable score such as BDe, BIC or AIC. This problem is known +to be NP-hard, which means that solving it becomes quickly infeasible as the +number of variables increases. Nevertheless, in this paper we show that it is +possible to learn the best Bayesian network structure with over 30 variables, +which covers many practically interesting cases. Our algorithm is less +complicated and more efficient than the techniques presented earlier. It can be +easily parallelized, and offers a possibility for efficient exploration of the +best networks consistent with different variable orderings. In the experimental +part of the paper we compare the performance of the algorithm to the previous +state-of-the-art algorithm. Free source-code and an online-demo can be found at +http://b-course.hiit.fi/bene. +","A simple approach for finding the globally optimal Bayesian network + structure" +" Recent work on approximate linear programming (ALP) techniques for +first-order Markov Decision Processes (FOMDPs) represents the value function +linearly w.r.t. a set of first-order basis functions and uses linear +programming techniques to determine suitable weights. This approach offers the +advantage that it does not require simplification of the first-order value +function, and allows one to solve FOMDPs independent of a specific domain +instantiation. In this paper, we address several questions to enhance the +applicability of this work: (1) Can we extend the first-order ALP framework to +approximate policy iteration to address performance deficiencies of previous +approaches? (2) Can we automatically generate basis functions and evaluate +their impact on value function quality? (3) How can we decompose intractable +problems with universally quantified rewards into tractable subproblems? We +propose answers to these questions along with a number of novel optimizations +and provide a comparative empirical evaluation on logistics problems from the +ICAPS 2004 Probabilistic Planning Competition. +",Practical Linear Value-approximation Techniques for First-order MDPs +" In this paper we promote introducing software verification and control flow +graph similarity measurement in automated evaluation of students' programs. We +present a new grading framework that merges results obtained by combination of +these two approaches with results obtained by automated testing, leading to +improved quality and precision of automated grading. These two approaches are +also useful in providing a comprehensible feedback that can help students to +improve the quality of their programs We also present our corresponding tools +that are publicly available and open source. The tools are based on LLVM +low-level intermediate code representation, so they could be applied to a +number of programming languages. Experimental evaluation of the proposed +grading framework is performed on a corpus of university students' programs +written in programming language C. Results of the experiments show that +automatically generated grades are highly correlated with manually determined +grades suggesting that the presented tools can find real-world applications in +studying and grading. +","Software Verification and Graph Similarity for Automated Evaluation of + Students' Assignments" +" The use of Artificial Intelligence is finding prominence not only in core +computer areas, but also in cross disciplinary areas including medical +diagnosis. In this paper, we present a rule based Expert System used in +diagnosis of Cerebral Palsy. The expert system takes user input and depending +on the symptoms of the patient, diagnoses if the patient is suffering from +Cerebral Palsy. The Expert System also classifies the Cerebral Palsy as mild, +moderate or severe based on the presented symptoms. +",Rule Based Expert System for Cerebral Palsy Diagnosis +" This paper focuses on the restart strategy of CMA-ES on multi-modal +functions. A first alternative strategy proceeds by decreasing the initial +step-size of the mutation while doubling the population size at each restart. A +second strategy adaptively allocates the computational budget among the restart +settings in the BIPOP scheme. Both restart strategies are validated on the BBOB +benchmark; their generality is also demonstrated on an independent real-world +problem suite related to spacecraft trajectory optimization. +",Alternative Restart Strategies for CMA-ES +" Covering is an important type of data structure while covering-based rough +sets provide an efficient and systematic theory to deal with covering data. In +this paper, we use boolean matrices to represent and axiomatize three types of +covering approximation operators. First, we define two types of characteristic +matrices of a covering which are essentially square boolean ones, and their +properties are studied. Through the characteristic matrices, three important +types of covering approximation operators are concisely equivalently +represented. Second, matrix representations of covering approximation operators +are used in boolean matrix decomposition. We provide a sufficient and necessary +condition for a square boolean matrix to decompose into the boolean product of +another one and its transpose. And we develop an algorithm for this boolean +matrix decomposition. Finally, based on the above results, these three types of +covering approximation operators are axiomatized using boolean matrices. In a +word, this work borrows extensively from boolean matrices and present a new +view to study covering-based rough sets. +","Characteristic matrix of covering and its application to boolean matrix + decomposition and axiomatization" +" Principal Component Analysis (PCA) finds a linear mapping and maximizes the +variance of the data which makes PCA sensitive to outliers and may cause wrong +eigendirection. In this paper, we propose techniques to solve this problem; we +use the data-centering method and reestimate the covariance matrix using robust +statistic techniques such as median, robust scaling which is a booster to +data-centering and Huber M-estimator which measures the presentation of +outliers and reweight them with small values. The results on several real world +data sets show that our proposed method handles outliers and gains better +results than the original PCA and provides the same accuracy with lower +computation cost than the Kernel PCA using the polynomial kernel in +classification tasks. +",Robust Principal Component Analysis Using Statistical Estimators +" A new generalized multilinear regression model, termed the Higher-Order +Partial Least Squares (HOPLS), is introduced with the aim to predict a tensor +(multiway array) $\tensor{Y}$ from a tensor $\tensor{X}$ through projecting the +data onto the latent space and performing regression on the corresponding +latent variables. HOPLS differs substantially from other regression models in +that it explains the data by a sum of orthogonal Tucker tensors, while the +number of orthogonal loadings serves as a parameter to control model complexity +and prevent overfitting. The low dimensional latent space is optimized +sequentially via a deflation operation, yielding the best joint subspace +approximation for both $\tensor{X}$ and $\tensor{Y}$. Instead of decomposing +$\tensor{X}$ and $\tensor{Y}$ individually, higher order singular value +decomposition on a newly defined generalized cross-covariance tensor is +employed to optimize the orthogonal loadings. A systematic comparison on both +synthetic data and real-world decoding of 3D movement trajectories from +electrocorticogram (ECoG) signals demonstrate the advantages of HOPLS over the +existing methods in terms of better predictive ability, suitability to handle +small sample sizes, and robustness to noise. +","Higher-Order Partial Least Squares (HOPLS): A Generalized Multi-Linear + Regression Method" +" While POMDPs provide a general platform for non-deterministic conditional +planning under a variety of quality metrics they have limited scalability. On +the other hand, non-deterministic conditional planners scale very well, but +many lack the ability to optimize plan quality metrics. We present a novel +generalization of planning graph based heuristics that helps conditional +planners both scale and generate high quality plans when using actions with +nonuniform costs. We make empirical comparisons with two state of the art +planners to show the benefit of our techniques. +",Cost Sensitive Reachability Heuristics for Handling State Uncertainty +" With the aid of the concept of stable independence we can construct, in an +efficient way, a compact representation of a semi-graphoid independence +relation. We show that this representation provides a new necessary condition +for the existence of a directed perfect map for the relation. The test for this +condition is based to a large extent on the transitivity property of a special +form of d-separation. The complexity of the test is linear in the size of the +representation. The test, moreover, brings the additional benefit that it can +be used to guide the early stages of network construction. +",Stable Independence in Perfect Maps +" Many real world sequences such as protein secondary structures or shell logs +exhibit a rich internal structures. Traditional probabilistic models of +sequences, however, consider sequences of flat symbols only. Logical hidden +Markov models have been proposed as one solution. They deal with logical +sequences, i.e., sequences over an alphabet of logical atoms. This comes at the +expense of a more complex model selection problem. Indeed, different +abstraction levels have to be explored. In this paper, we propose a novel +method for selecting logical hidden Markov models from data called SAGEM. SAGEM +combines generalized expectation maximization, which optimizes parameters, with +structure search for model selection using inductive logic programming +refinement operators. We provide convergence and experimental results that show +SAGEM's effectiveness. +",'Say EM' for Selecting Probabilistic Models for Logical Sequences +" Intelligent systems in an open world must reason about many interacting +entities related to each other in diverse ways and having uncertain features +and relationships. Traditional probabilistic languages lack the expressive +power to handle relational domains. Classical first-order logic is sufficiently +expressive, but lacks a coherent plausible reasoning capability. Recent years +have seen the emergence of a variety of approaches to integrating first-order +logic, probability, and machine learning. This paper presents Multi-entity +Bayesian networks (MEBN), a formal system that integrates First Order Logic +(FOL) with Bayesian probability theory. MEBN extends ordinary Bayesian networks +to allow representation of graphical models with repeated sub-structures, and +can express a probability distribution over models of any consistent, finitely +axiomatizable first-order theory. We present the logic using an example +inspired by the Paramount Series StarTrek. +",Of Starships and Klingons: Bayesian Logic for the 23rd Century +" In this paper we present a differential semantics of Lazy AR Propagation +(LARP) in discrete Bayesian networks. We describe how both single and multi +dimensional partial derivatives of the evidence may easily be calculated from a +junction tree in LARP equilibrium. We show that the simplicity of the +calculations stems from the nature of LARP. Based on the differential semantics +we describe how variable propagation in the LARP architecture may give access +to additional partial derivatives. The cautious LARP (cLARP) scheme is derived +to produce a flexible cLARP equilibrium that offers additional opportunities +for calculating single and multidimensional partial derivatives of the evidence +and subsets of the evidence from a single propagation. The results of an +empirical evaluation illustrates how the access to a largely increased number +of partial derivatives comes at a low computational cost. +",A Differential Semantics of Lazy AR Propagation +" This paper deals with the following problem: modify a Bayesian network to +satisfy a given set of probability constraints by only change its conditional +probability tables, and the probability distribution of the resulting network +should be as close as possible to that of the original network. We propose to +solve this problem by extending IPFP (iterative proportional fitting procedure) +to probability distributions represented by Bayesian networks. The resulting +algorithm E-IPFP is further developed to D-IPFP, which reduces the +computational cost by decomposing a global EIPFP into a set of smaller local +E-IPFP problems. Limited analysis is provided, including the convergence proofs +of the two algorithms. Computer experiments were conducted to validate the +algorithms. The results are consistent with the theoretical analysis. +",Modifying Bayesian Networks by Probability Constraints +" Studying the effects of one-way variation of any number of parameters on any +number of output probabilities quickly becomes infeasible in practice, +especially if various evidence profiles are to be taken into consideration. To +provide for identifying the parameters that have a potentially large effect +prior to actually performing the analysis, we need properties of sensitivity +functions that are independent of the network under study, of the available +evidence, or of both. In this paper, we study properties that depend upon just +the probability of the entered evidence. We demonstrate that these properties +provide for establishing an upper bound on the sensitivity value for a +parameter; they further provide for establishing the region in which the vertex +of the sensitivity function resides, thereby serving to identify parameters +with a low sensitivity value that may still have a large impact on the +probability of interest for relatively small parameter variations. +",Exploiting Evidence-dependent Sensitivity Bounds +" We present multi-agent A* (MAA*), the first complete and optimal heuristic +search algorithm for solving decentralized partially-observable Markov decision +problems (DEC-POMDPs) with finite horizon. The algorithm is suitable for +computing optimal plans for a cooperative group of agents that operate in a +stochastic environment such as multirobot coordination, network traffic +control, `or distributed resource allocation. Solving such problems efiectively +is a major challenge in the area of planning under uncertainty. Our solution is +based on a synthesis of classical heuristic search and decentralized control +theory. Experimental results show that MAA* has significant advantages. We +introduce an anytime variant of MAA* and conclude with a discussion of +promising extensions such as an approach to solving infinite horizon problems. +",MAA*: A Heuristic Search Algorithm for Solving Decentralized POMDPs +" Inferring from inconsistency and making decisions are two problems which have +always been treated separately by researchers in Artificial Intelligence. +Consequently, different models have been proposed for each category. Different +argumentation systems [2, 7, 10, 11] have been developed for handling +inconsistency in knowledge bases. Recently, other argumentation systems [3, 4, +8] have been defined for making decisions under uncertainty. The aim of this +paper is to present a general argumentation framework in which both inferring +from inconsistency and decision making are captured. The proposed framework can +be used for decision under uncertainty, multiple criteria decision, rule-based +decision and finally case-based decision. Moreover, works on classical decision +suppose that the information about environment is coherent, and this no longer +required by this general framework. +","A unified setting for inference and decision: An argumentation-based + approach" +" When a hybrid Bayesian network has conditionally deterministic variables with +continuous parents, the joint density function for the continuous variables +does not exist. Conditional linear Gaussian distributions can handle such cases +when the continuous variables have a multi-variate normal distribution and the +discrete variables do not have continuous parents. In this paper, operations +required for performing inference with conditionally deterministic variables in +hybrid Bayesian networks are developed. These methods allow inference in +networks with deterministic variables where continuous variables may be +non-Gaussian, and their density functions can be approximated by mixtures of +truncated exponentials. There are no constraints on the placement of continuous +and discrete nodes in the network. +",Hybrid Bayesian Networks with Linear Deterministic Variables +" We consider the problem of deleting edges from a Bayesian network for the +purpose of simplifying models in probabilistic inference. In particular, we +propose a new method for deleting network edges, which is based on the evidence +at hand. We provide some interesting bounds on the KL-divergence between +original and approximate networks, which highlight the impact of given evidence +on the quality of approximation and shed some light on good and bad candidates +for edge deletion. We finally demonstrate empirically the promise of the +proposed edge deletion technique as a basis for approximate inference. +",On Bayesian Network Approximation by Edge Deletion +" We define the notion of compiling a Bayesian network with evidence and +provide a specific approach for evidence-based compilation, which makes use of +logical processing. The approach is practical and advantageous in a number of +application areas-including maximum likelihood estimation, sensitivity +analysis, and MAP computations-and we provide specific empirical results in the +domain of genetic linkage analysis. We also show that the approach is +applicable for networks that do not contain determinism, and show that it +empirically subsumes the performance of the quickscore algorithm when applied +to noisy-or networks. +",Exploiting Evidence in Probabilistic Inference +" A model of the world built from sensor data may be incorrect even if the +sensors are functioning correctly. Possible causes include the use of +inappropriate sensors (e.g. a laser looking through glass walls), sensor +inaccuracies accumulate (e.g. localization errors), the a priori models are +wrong, or the internal representation does not match the world (e.g. a static +occupancy grid used with dynamically moving objects). We are interested in the +case where the constructed model of the world is flawed, but there is no access +to the ground truth that would allow the system to see the discrepancy, such as +a robot entering an unknown environment. This paper considers the problem of +determining when something is wrong using only the sensor data used to +construct the world model. It proposes 11 interpretation inconsistency +indicators based on the Dempster-Shafer conflict metric, Con, and evaluates +these indicators according to three criteria: ability to distinguish true +inconsistency from sensor noise (classification), estimate the magnitude of +discrepancies (estimation), and determine the source(s) (if any) of sensing +problems in the environment (isolation). The evaluation is conducted using data +from a mobile robot with sonar and laser range sensors navigating indoor +environments under controlled conditions. The evaluation shows that the Gambino +indicator performed best in terms of estimation (at best 0.77 correlation), +isolation, and classification of the sensing situation as degraded (7% false +negative rate) or normal (0% false positive rate). +","Use of Dempster-Shafer Conflict Metric to Detect Interpretation + Inconsistency" +" The Bayesian Logic (BLOG) language was recently developed for defining +first-order probability models over worlds with unknown numbers of objects. It +handles important problems in AI, including data association and population +estimation. This paper extends BLOG by adopting generative processes over +function spaces - known as nonparametrics in the Bayesian literature. We +introduce syntax for reasoning about arbitrary collections of objects, and +their properties, in an intuitive manner. By exploiting exchangeability, +distributions over unknown objects and their attributes are cast as Dirichlet +processes, which resolve difficulties in model selection and inference caused +by varying numbers of objects. We demonstrate these concepts with application +to citation matching. +",Nonparametric Bayesian Logic +" We propose an efficient algorithm for estimation of possibility based +qualitative expected utility. It is useful for decision making mechanisms where +each possible decision is assigned a multi-attribute possibility distribution. +The computational complexity of ordinary methods calculating the expected +utility based on discretization is growing exponentially with the number of +attributes, and may become infeasible with a high number of these attributes. +We present series of theorems and lemmas proving the correctness of our +algorithm that exibits a linear computational complexity. Our algorithm has +been applied in the context of selecting the most prospective partners in +multi-party multi-attribute negotiation, and can also be used in making +decisions about potential offers during the negotiation as other similar +problems. +","Efficient algorithm for estimation of qualitative expected utility in + possibilistic case-based reasoning" +" The local Markov condition for a DAG to be an independence map of a +probability distribution is well known. For DAGs with latent variables, +represented as bi-directed edges in the graph, the local Markov property may +invoke exponential number of conditional independencies. This paper shows that +the number of conditional independence relations required may be reduced if the +probability distributions satisfy the composition axiom. In certain types of +graphs, only linear number of conditional independencies are required. The +result has applications in testing linear structural equation models with +correlated errors. +",Local Markov Property for Models Satisfying Composition Axiom +" We present a framework to discover and characterize different classes of +everyday activities from event-streams. We begin by representing activities as +bags of event n-grams. This allows us to analyze the global structural +information of activities, using their local event statistics. We demonstrate +how maximal cliques in an undirected edge-weighted graph of activities, can be +used for activity-class discovery in an unsupervised manner. We show how +modeling an activity as a variable length Markov process, can be used to +discover recurrent event-motifs to characterize the discovered +activity-classes. We present results over extensive data-sets, collected from +multiple active environments, to show the competence and generalizability of +our proposed framework. +",Unsupervised Activity Discovery and Characterization From Event-Streams +" This paper describes a general framework called Hybrid Dynamic Mixed Networks +(HDMNs) which are Hybrid Dynamic Bayesian Networks that allow representation of +discrete deterministic information in the form of constraints. We propose +approximate inference algorithms that integrate and adjust well known +algorithmic principles such as Generalized Belief Propagation, +Rao-Blackwellised Particle Filtering and Constraint Propagation to address the +complexity of modeling and reasoning in HDMNs. We use this framework to model a +person's travel activity over time and to predict destination and routes given +the current location. We present a preliminary empirical evaluation +demonstrating the effectiveness of our modeling framework and algorithms using +several variants of the activity model. +",Modeling Transportation Routines using Hybrid Dynamic Mixed Networks +" In this paper, we consider Hybrid Mixed Networks (HMN) which are Hybrid +Bayesian Networks that allow discrete deterministic information to be modeled +explicitly in the form of constraints. We present two approximate inference +algorithms for HMNs that integrate and adjust well known algorithmic principles +such as Generalized Belief Propagation, Rao-Blackwellised Importance Sampling +and Constraint Propagation to address the complexity of modeling and reasoning +in HMNs. We demonstrate the performance of our approximate inference algorithms +on randomly generated HMNs. +","Approximate Inference Algorithms for Hybrid Bayesian Networks with + Discrete Constraints" +" We present metrics for measuring state similarity in Markov decision +processes (MDPs) with infinitely many states, including MDPs with continuous +state spaces. Such metrics provide a stable quantitative analogue of the notion +of bisimulation for MDPs, and are suitable for use in MDP approximation. We +show that the optimal value function associated with a discounted infinite +horizon planning task varies continuously with respect to our metric distances. +",Metrics for Markov Decision Processes with Infinite State Spaces +" Decision-theoretic planning with risk-sensitive planning objectives is +important for building autonomous agents or decision-support systems for +real-world applications. However, this line of research has been largely +ignored in the artificial intelligence and operations research communities +since planning with risk-sensitive planning objectives is more complicated than +planning with risk-neutral planning objectives. To remedy this situation, we +derive conditions that guarantee that the optimal expected utilities of the +total plan-execution reward exist and are finite for fully observable Markov +decision process models with non-linear utility functions. In case of Markov +decision process models with both positive and negative rewards, most of our +results hold for stationary policies only, but we conjecture that they can be +generalized to non stationary policies. +","Existence and Finiteness Conditions for Risk-Sensitive Planning: Results + and Conjectures" +" A fundamental issue in real-world systems, such as sensor networks, is the +selection of observations which most effectively reduce uncertainty. More +specifically, we address the long standing problem of nonmyopically selecting +the most informative subset of variables in a graphical model. We present the +first efficient randomized algorithm providing a constant factor +(1-1/e-epsilon) approximation guarantee for any epsilon > 0 with high +confidence. The algorithm leverages the theory of submodular functions, in +combination with a polynomial bound on sample complexity. We furthermore prove +that no polynomial time algorithm can provide a constant factor approximation +better than (1 - 1/e) unless P = NP. Finally, we provide extensive evidence of +the effectiveness of our method on two complex real-world datasets. +",Near-optimal Nonmyopic Value of Information in Graphical Models +" In this paper, we propose a revision-based approach for conflict resolution +by generalizing the Disjunctive Maxi-Adjustment (DMA) approach (Benferhat et +al. 2004). Revision operators can be classified into two different families: +the model-based ones and the formula-based ones. So the revision-based approach +has two different versions according to which family of revision operators is +chosen. Two particular revision operators are considered, one is the Dalal's +revision operator, which is a model-based revision operator, and the other is +the cardinality-maximal based revision operator, which is a formulabased +revision operator. When the Dalal's revision operator is chosen, the +revision-based approach is independent of the syntactic form in each stratum +and it captures some notion of minimal change. When the cardinalitymaximal +based revision operator is chosen, the revision-based approach is equivalent to +the DMA approach. We also show that both approaches are computationally easier +than the DMA approach. +",A Revision-Based Approach to Resolving Conflicting Information +" Systems such as sensor networks and teams of autonomous robots consist of +multiple autonomous entities that interact with each other in a distributed, +asynchronous manner. These entities need to keep track of the state of the +system as it evolves. Asynchronous systems lead to special challenges for +monitoring, as nodes must update their beliefs independently of each other and +no central coordination is possible. Furthermore, the state of the system +continues to change as beliefs are being updated. Previous approaches to +developing distributed asynchronous probabilistic reasoning systems have used +static models. We present an approach using dynamic models, that take into +account the way the system changes state over time. Our approach, which is +based on belief propagation, is fully distributed and asynchronous, and allows +the world to keep on changing as messages are being sent around. Experimental +results show that our approach compares favorably to the factored frontier +algorithm. +",Asynchronous Dynamic Bayesian Networks +" Continuous time Bayesian networks (CTBNs) describe structured stochastic +processes with finitely many states that evolve over continuous time. A CTBN is +a directed (possibly cyclic) dependency graph over a set of variables, each of +which represents a finite state continuous time Markov process whose transition +model is a function of its parents. As shown previously, exact inference in +CTBNs is intractable. We address the problem of approximate inference, allowing +for general queries conditioned on evidence over continuous time intervals and +at discrete time points. We show how CTBNs can be parameterized within the +exponential family, and use that insight to develop a message passing scheme in +cluster graphs and allows us to apply expectation propagation to CTBNs. The +clusters in our cluster graph do not contain distributions over the cluster +variables at individual time points, but distributions over trajectories of the +variables throughout a duration. Thus, unlike discrete time temporal models +such as dynamic Bayesian networks, we can adapt the time granularity at which +we reason for different variables and in different conditions. +",Expectation Propagation for Continuous Time Bayesian Networks +" Continuous time Bayesian networks (CTBNs) describe structured stochastic +processes with finitely many states that evolve over continuous time. A CTBN is +a directed (possibly cyclic) dependency graph over a set of variables, each of +which represents a finite state continuous time Markov process whose transition +model is a function of its parents. We address the problem of learning the +parameters and structure of a CTBN from partially observed data. We show how to +apply expectation maximization (EM) and structural expectation maximization +(SEM) to CTBNs. The availability of the EM algorithm allows us to extend the +representation of CTBNs to allow a much richer class of transition durations +distributions, known as phase distributions. This class is a highly expressive +semi-parametric representation, which can approximate any duration distribution +arbitrarily closely. This extension to the CTBN framework addresses one of the +main limitations of both CTBNs and DBNs - the restriction to exponentially / +geometrically distributed duration. We present experimental results on a real +data set of people's life spans, showing that our algorithm learns reasonable +models - structure and parameters - from partially observed data, and, with the +use of phase distributions, achieves better performance than DBNs. +","Expectation Maximization and Complex Duration Distributions for + Continuous Time Bayesian Networks" +" We derive novel sufficient conditions for convergence of Loopy Belief +Propagation (also known as the Sum-Product algorithm) to a unique fixed point. +Our results improve upon previously known conditions. For binary variables with +(anti-)ferromagnetic interactions, our conditions seem to be sharp. +",Sufficient conditions for convergence of Loopy Belief Propagation +" In this paper we compare search and inference in graphical models through the +new framework of AND/OR search. Specifically, we compare Variable Elimination +(VE) and memoryintensive AND/OR Search (AO) and place algorithms such as +graph-based backjumping and no-good and good learning, as well as Recursive +Conditioning [7] and Value Elimination [2] within the AND/OR search framework. +",The Relationship Between AND/OR Search and Variable Elimination +" This paper addresses a fundamental issue central to approximation methods for +solving large Markov decision processes (MDPs): how to automatically learn the +underlying representation for value function approximation? A novel +theoretically rigorous framework is proposed that automatically generates +geometrically customized orthonormal sets of basis functions, which can be used +with any approximate MDP solver like least squares policy iteration (LSPI). The +key innovation is a coordinate-free representation of value functions, using +the theory of smooth functions on a Riemannian manifold. Hodge theory yields a +constructive method for generating basis functions for approximating value +functions based on the eigenfunctions of the self-adjoint (Laplace-Beltrami) +operator on manifolds. In effect, this approach performs a global Fourier +analysis on the state space graph to approximate value functions, where the +basis functions reflect the largescale topology of the underlying state space. +A new class of algorithms called Representation Policy Iteration (RPI) are +presented that automatically learn both basis functions and approximately +optimal policies. Illustrative experiments compare the performance of RPI with +that of LSPI using two handcoded basis functions (RBF and polynomial state +encodings). +",Representation Policy Iteration +" Existing complexity bounds for point-based POMDP value iteration algorithms +focus either on the curse of dimensionality or the curse of history. We derive +a new bound that relies on both and uses the concept of discounted +reachability; our conclusions may help guide future algorithm design. We also +discuss recent improvements to our (point-based) heuristic search value +iteration algorithm. Our new implementation calculates tighter initial bounds, +avoids solving linear programs, and makes more effective use of sparsity. +",Point-Based POMDP Algorithms: Improved Analysis and Implementation +" We introduce a new approximate solution technique for first-order Markov +decision processes (FOMDPs). Representing the value function linearly w.r.t. a +set of first-order basis functions, we compute suitable weights by casting the +corresponding optimization as a first-order linear program and show how +off-the-shelf theorem prover and LP software can be effectively used. This +technique allows one to solve FOMDPs independent of a specific domain +instantiation; furthermore, it allows one to determine bounds on approximation +error that apply equally to all domain instantiations. We apply this solution +technique to the task of elevator scheduling with a rich feature space and +multi-criteria additive reward, and demonstrate that it outperforms a number of +intuitive, heuristicallyguided policies. +",Approximate Linear Programming for First-order MDPs +" Models of dynamical systems based on predictive state representations (PSRs) +are defined strictly in terms of observable quantities, in contrast with +traditional models (such as Hidden Markov Models) that use latent variables or +statespace representations. In addition, PSRs have an effectively infinite +memory, allowing them to model some systems that finite memory-based models +cannot. Thus far, PSR models have primarily been developed for domains with +discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG) +model, a class of PSR models for domains with continuous observations. We show +that PLG models subsume Linear Dynamical System models (also called Kalman +filter models or state-space models) while using fewer parameters. We also +introduce an algorithm to estimate PLG parameters from data, and contrast it +with standard Expectation Maximization (EM) algorithms used to estimate Kalman +filter parameters. We show that our algorithm is a consistent estimation +procedure and present preliminary empirical results suggesting that our +algorithm outperforms EM, particularly as the model dimension increases. +",Predictive Linear-Gaussian Models of Stochastic Dynamical Systems +" We consider the problem of diagnosing faults in a system represented by a +Bayesian network, where diagnosis corresponds to recovering the most likely +state of unobserved nodes given the outcomes of tests (observed nodes). Finding +an optimal subset of tests in this setting is intractable in general. We show +that it is difficult even to compute the next most-informative test using +greedy test selection, as it involves several entropy terms whose exact +computation is intractable. We propose an approximate approach that utilizes +the loopy belief propagation infrastructure to simultaneously compute +approximations of marginal and conditional entropies on multiple subsets of +nodes. We apply our method to fault diagnosis in computer networks, and show +the algorithm to be very effective on realistic Internet-like topologies. We +also provide theoretical justification for the greedy test selection approach, +along with some performance guarantees. +",Efficient Test Selection in Active Diagnosis via Entropy Approximation +" One of the main problems of importance sampling in Bayesian networks is +representation of the importance function, which should ideally be as close as +possible to the posterior joint distribution. Typically, we represent an +importance function as a factorization, i.e., product of conditional +probability tables (CPTs). Given diagnostic evidence, we do not have explicit +forms for the CPTs in the networks. We first derive the exact form for the CPTs +of the optimal importance function. Since the calculation is hard, we usually +only use their approximations. We review several popular strategies and point +out their limitations. Based on an analysis of the influence of evidence, we +propose a method for approximating the exact form of importance function by +explicitly modeling the most important additional dependence relations +introduced by evidence. Our experimental results show that the new +approximation strategy offers an immediate improvement in the quality of the +importance function. +","Importance Sampling in Bayesian Networks: An Influence-Based + Approximation Strategy for Importance Functions" +" GBP and EP are two successful algorithms for approximate probabilistic +inference, which are based on different approximation strategies. An open +problem in both algorithms has been how to choose an appropriate approximation +structure. We introduce 'structured region graphs', a formalism which marries +these two strategies, reveals a deep connection between them, and suggests how +to choose good approximation structures. In this formalism, each region has an +internal structure which defines an exponential family, whose sufficient +statistics must be matched by the parent region. Reduction operators on these +structures allow conversion between EP and GBP free energies. Thus it is +revealed that all EP approximations on discrete variables are special cases of +GBP, and conversely that some wellknown GBP approximations, such as overlapping +squares, are special cases of EP. Furthermore, region graphs derived from EP +have a number of good structural properties, including maxent-normality and +overall counting number of one. The result is a convenient framework for +producing high-quality approximations with a user-adjustable level of +complexity +",Structured Region Graphs: Morphing EP into GBP +" In recent years, there has been an increased need for the use of active +systems - systems required to act automatically based on events, or changes in +the environment. Such systems span many areas, from active databases to +applications that drive the core business processes of today's enterprises. +However, in many cases, the events to which the system must respond are not +generated by monitoring tools, but must be inferred from other events based on +complex temporal predicates. In addition, in many applications, such inference +is inherently uncertain. In this paper, we introduce a formal framework for +knowledge representation and reasoning enabling such event inference. Based on +probability theory, we define the representation of the associated uncertainty. +In addition, we formally define the probability space, and show how the +relevant probabilities can be calculated by dynamically constructing a Bayesian +network. To the best of our knowledge, this is the first work that enables +taking such uncertainty into account in the context of active systems. +herefore, our contribution is twofold: We formally define the representation +and semantics of event composition for probabilistic settings, and show how to +apply these extensions to the quantification of the occurrence probability of +events. These results enable any active system to handle such uncertainty. +",A Model for Reasoning with Uncertain Rules in Event Composition Systems +" The feature of our method different from other fuzzy grey relation method for +supermixed multiple attribute group decision-making is that all of the +subjective and objective weights are obtained by interval grey number and that +the group decisionmaking is performed based on the relative approach degree of +grey TOPSIS, the relative approach degree of grey incidence and the relative +membership degree of grey incidence using 4-dimensional Euclidean distance. The +weighted Borda method is used to obtain final rank by using the results of four +methods. An example shows the applicability of the proposed approach. +","Super-Mixed Multiple Attribute Group Decision Making Method Based on + Hybrid Fuzzy Grey Relation Approach Degree" +" The multiple attribute mixed type decision making is performed by four +methods, that is, the relative approach degree of grey TOPSIS method, the +relative approach degree of grey incidence, the relative membership degree of +grey incidence and the grey relation relative approach degree method using the +maximum entropy estimation, respectively. In these decision making methods, the +grey incidence degree in four-dimensional Euclidean space is used. The final +arrangement result is obtained by weighted Borda method. An example illustrates +the applicability of the proposed approach. +","Generalized Hybrid Grey Relation Method for Multiple Attribute Mixed + Type Decision Making" +" We revisit the SeqBin constraint. This meta-constraint subsumes a number of +important global constraints like Change, Smooth and IncreasingNValue. We show +that the previously proposed filtering algorithm for SeqBin has two drawbacks +even under strong restrictions: it does not detect bounds disentailment and it +is not idempotent. We identify the cause for these problems, and propose a new +propagator that overcomes both issues. Our algorithm is based on a connection +to the problem of finding a path of a given cost in a restricted $n$-partite +graph. Our propagator enforces domain consistency in O(nd^2) and, for special +cases of SeqBin that include Change, Smooth and IncreasingNValue, in O(nd) +time. +",The SeqBin Constraint Revisited +" This article introduces the benefits of using computer as a tool for foreign +language teaching and learning. It describes the effect of using Natural +Language Processing (NLP) tools for learning Arabic. The technique explored in +this particular case is the employment of pedagogically indexed corpora. This +text-based method provides the teacher the advantage of building activities +based on texts adapted to a particular pedagogical situation. This paper also +presents ARAC: a Platform dedicated to language educators allowing them to +create activities within their own pedagogical area of interest. +",Arabic CALL system based on pedagogically indexed text +" This article describes different models based on Bayesian networks RB +modeling expertise in the diagnosis of brain tumors. Indeed, they are well +adapted to the representation of the uncertainty in the process of diagnosis of +these tumors. In our work, we first tested several structures derived from the +Bayesian network reasoning performed by doctors on the one hand and structures +generated automatically on the other. This step aims to find the best structure +that increases diagnostic accuracy. The machine learning algorithms relate +MWST-EM algorithms, SEM and SEM + T. To estimate the parameters of the Bayesian +network from a database incomplete, we have proposed an extension of the EM +algorithm by adding a priori knowledge in the form of the thresholds calculated +by the first phase of the algorithm RBE . The very encouraging results obtained +are discussed at the end of the paper +","Etude de Mod\`eles \`a base de r\'eseaux Bay\'esiens pour l'aide au + diagnostic de tumeurs c\'er\'ebrales" +" This paper proposes a grey interval relation TOPSIS for the decision making +in which all of the attribute weights and attribute values are given by the +interval grey numbers. The feature of our method different from other grey +relation decision-making is that all of the subjective and objective weights +are obtained by interval grey number and that decisionmaking is performed based +on the relative approach degree of grey TOPSIS, the relative approach degree of +grey incidence and the relative membership degree of grey incidence using +2-dimensional Euclidean distance. The weighted Borda method is used for +combining the results of three methods. An example shows the applicability of +the proposed approach. +","Novel Grey Interval Weight Determining and Hybrid Grey Interval Relation + Method in Multiple Attribute Decision-Making" +" Symbolic event recognition systems have been successfully applied to a +variety of application domains, extracting useful information in the form of +events, allowing experts or other systems to monitor and respond when +significant events are recognised. In a typical event recognition application, +however, these systems often have to deal with a significant amount of +uncertainty. In this paper, we address the issue of uncertainty in logic-based +event recognition by extending the Event Calculus with probabilistic reasoning. +Markov Logic Networks are a natural candidate for our logic-based formalism. +However, the temporal semantics of the Event Calculus introduce a number of +challenges for the proposed model. We show how and under what assumptions we +can overcome these problems. Additionally, we study how probabilistic modelling +changes the behaviour of the formalism, affecting its key property, the inertia +of fluents. Furthermore, we demonstrate the advantages of the probabilistic +Event Calculus through examples and experiments in the domain of activity +recognition, using a publicly available dataset for video surveillance. +",Probabilistic Event Calculus for Event Recognition +" Understanding the structure and dynamics of biological networks is one of the +important challenges in system biology. In addition, increasing amount of +experimental data in biological networks necessitate the use of efficient +methods to analyze these huge amounts of data. Such methods require to +recognize common patterns to analyze data. As biological networks can be +modeled by graphs, the problem of common patterns recognition is equivalent +with frequent sub graph mining in a set of graphs. In this paper, at first the +challenges of frequent subgrpahs mining in biological networks are introduced +and the existing approaches are classified for each challenge. then the +algorithms are analyzed on the basis of the type of the approach they apply for +each of the challenges. +","Classification of Approaches and Challenges of Frequent Subgraphs Mining + in Biological Networks" +" This paper proposes a grey interval relation TOPSIS method for the decision +making in which all of the attribute weights and attribute values are given by +the interval grey numbers. In this paper, all of the subjective and objective +weights are obtained by interval grey number and decision-making is based on +four methods such as the relative approach degree of grey TOPSIS, the relative +approach degree of grey incidence and the relative approach degree method using +the maximum entropy estimation using 2-dimensional Euclidean distance. A +multiple attribute decision-making example for evaluation of artistic talent of +Kayagum (stringed Korean harp) players is given to show practicability of the +proposed approach. +","Hybrid Grey Interval Relation Decision-Making in Artistic Talent + Evaluation of Player" +" The behavior composition problem involves automatically building a controller +that is able to realize a desired, but unavailable, target system (e.g., a +house surveillance) by suitably coordinating a set of available components +(e.g., video cameras, blinds, lamps, a vacuum cleaner, phones, etc.) Previous +work has almost exclusively aimed at bringing about the desired component in +its totality, which is highly unsatisfactory for unsolvable problems. In this +work, we develop an approach for approximate behavior composition without +departing from the classical setting, thus making the problem applicable to a +much wider range of cases. Based on the notion of simulation, we characterize +what a maximal controller and the ""closest"" implementable target module +(optimal approximation) are, and show how these can be computed using ATL model +checking technology for a special case. We show the uniqueness of optimal +approximations, and prove their soundness and completeness with respect to +their imported controllers. +",Qualitative Approximate Behavior Composition +" We propose a variant of Alternating-time Temporal Logic (ATL) grounded in the +agents' operational know-how, as defined by their libraries of abstract plans. +Inspired by ATLES, a variant itself of ATL, it is possible in our logic to +explicitly refer to ""rational"" strategies for agents developed under the +Belief-Desire-Intention agent programming paradigm. This allows us to express +and verify properties of BDI systems using ATL-type logical frameworks. +",Reasoning about Agent Programs using ATL-like Logics +" We consider the problem of computing optimal generalised policies for +relational Markov decision processes. We describe an approach combining some of +the benefits of purely inductive techniques with those of symbolic dynamic +programming methods. The latter reason about the optimal value function using +first-order decision theoretic regression and formula rewriting, while the +former, when provided with a suitable hypotheses language, are capable of +generalising value functions or policies for small instances. Our idea is to +use reasoning and in particular classical first-order regression to +automatically generate a hypotheses language dedicated to the domain at hand, +which is then used as input by an inductive solver. This approach avoids the +more complex reasoning of symbolic dynamic programming while focusing the +inductive solver's attention on concepts that are specifically relevant to the +optimal value function for the domain considered. +",Exploiting First-Order Regression in Inductive Policy Selection +" This paper proposes a decision theory for a symbolic generalization of +probability theory (SP). Darwiche and Ginsberg [2,3] proposed SP to relax the +requirement of using numbers for uncertainty while preserving desirable +patterns of Bayesian reasoning. SP represents uncertainty by symbolic supports +that are ordered partially rather than completely as in the case of standard +probability. We show that a preference relation on acts that satisfies a number +of intuitive postulates is represented by a utility function whose domain is a +set of pairs of supports. We argue that a subjective interpretation is as +useful and appropriate for SP as it is for numerical probability. It is useful +because the subjective interpretation provides a basis for uncertainty +elicitation. It is appropriate because we can provide a decision theory that +explains how preference on acts is based on support comparison. +",Decision Making for Symbolic Probability +" We present metrics for measuring the similarity of states in a finite Markov +decision process (MDP). The formulation of our metrics is based on the notion +of bisimulation for MDPs, with an aim towards solving discounted infinite +horizon reinforcement learning tasks. Such metrics can be used to aggregate +states, as well as to better structure other value function approximators +(e.g., memory-based or nearest-neighbor approximators). We provide bounds that +relate our metric distances to the optimal values of states in the given MDP. +",Metrics for Finite Markov Decision Processes +" We describe an approach for exploiting structure in Markov Decision Processes +with continuous state variables. At each step of the dynamic programming, the +state space is dynamically partitioned into regions where the value function is +the same throughout the region. We first describe the algorithm for piecewise +constant representations. We then extend it to piecewise linear +representations, using techniques from POMDPs to represent and reason about +linear surfaces efficiently. We show that for complex, structured problems, our +approach exploits the natural structure so that optimal solutions can be +computed efficiently. +",Dynamic Programming for Structured Continuous Markov Decision Problems +" We present a major improvement to the incremental pruning algorithm for +solving partially observable Markov decision processes. Our technique targets +the cross-sum step of the dynamic programming (DP) update, a key source of +complexity in POMDP algorithms. Instead of reasoning about the whole belief +space when pruning the cross-sums, our algorithm divides the belief space into +smaller regions and performs independent pruning in each region. We evaluate +the benefits of the new technique both analytically and experimentally, and +show that it produces very significant performance gains. The results +contribute to the scalability of POMDP algorithms to domains that cannot be +handled by the best existing techniques. +",Region-Based Incremental Pruning for POMDPs +" The aim of this work is to provide a unified framework for ordinal +representations of uncertainty lying at the crosswords between possibility and +probability theories. Such confidence relations between events are commonly +found in monotonic reasoning, inconsistency management, or qualitative decision +theory. They start either from probability theory, making it more qualitative, +or from possibility theory, making it more expressive. We show these two trends +converge to a class of genuine probability theories. We provide +characterization results for these useful tools that preserve the qualitative +nature of possibility rankings, while enjoying the power of expressivity of +additive representations. +",A Unified framework for order-of-magnitude confidence relations +" The paper introduces mixed networks, a new framework for expressing and +reasoning with probabilistic and deterministic information. The framework +combines belief networks with constraint networks, defining the semantics and +graphical representation. We also introduce the AND/OR search space for +graphical models, and develop a new linear space search algorithm. This +provides the basis for understanding the benefits of processing the constraint +information separately, resulting in the pruning of the search space. When the +constraint part is tractable or has a small number of solutions, using the +mixed representation can be exponentially more effective than using pure belief +networks which odel constraints as conditional probability tables. +","Mixtures of Deterministic-Probabilistic Networks and their AND/OR Search + Space" +" The representation of independence relations generally builds upon the +well-known semigraphoid axioms of independence. Recently, a representation has +been proposed that captures a set of dominant statements of an independence +relation from which any other statement can be generated by means of the +axioms; the cardinality of this set is taken to indicate the complexity of the +relation. Building upon the idea of dominance, we introduce the concept of +stability to provide for a more compact representation of independence. We give +an associated algorithm for establishing such a representation.We show that, +with our concept of stability, many independence relations are found to be of +lower complexity than with existing representations. +",Stable Independance and Complexity of Representation +" This paper investigates a representation language with flexibility inspired +by probabilistic logic and compactness inspired by relational Bayesian +networks. The goal is to handle propositional and first-order constructs +together with precise, imprecise, indeterminate and qualitative probabilistic +assessments. The paper shows how this can be achieved through the theory of +credal networks. New exact and approximate inference algorithms based on +multilinear programming and iterated/loopy propagation of interval +probabilities are presented; their superior performance, compared to existing +ones, is shown empirically. +","Propositional and Relational Bayesian Networks Associated with Imprecise + and Qualitative Probabilistic Assesments" +" Defeasible argumentation frameworks have evolved to become a sound setting to +formalize commonsense, qualitative reasoning from incomplete and potentially +inconsistent knowledge. Defeasible Logic Programming (DeLP) is a defeasible +argumentation formalism based on an extension of logic programming. Although +DeLP has been successfully integrated in a number of different real-world +applications, DeLP cannot deal with explicit uncertainty, nor with vague +knowledge, as defeasibility is directly encoded in the object language. This +paper introduces P-DeLP, a new logic programming language that extends original +DeLP capabilities for qualitative reasoning by incorporating the treatment of +possibilistic uncertainty and fuzzy knowledge. Such features will be formalized +on the basis of PGL, a possibilistic logic based on Godel fuzzy logic. +","A Logic Programming Framework for Possibilistic Argumentation with Vague + Knowledge" +" Previous work on sensitivity analysis in Bayesian networks has focused on +single parameters, where the goal is to understand the sensitivity of queries +to single parameter changes, and to identify single parameter changes that +would enforce a certain query constraint. In this paper, we expand the work to +multiple parameters which may be in the CPT of a single variable, or the CPTs +of multiple variables. Not only do we identify the solution space of multiple +parameter changes that would be needed to enforce a query constraint, but we +also show how to find the optimal solution, that is, the one which disturbs the +current probability distribution the least (with respect to a specific measure +of disturbance). We characterize the computational complexity of our new +techniques and discuss their applications to developing and debugging Bayesian +networks, and to the problem of reasoning about the value (reliability) of new +information. +","Sensitivity Analysis in Bayesian Networks: From Single to Multiple + Parameters" +" We consider the challenge of preference elicitation in systems that help +users discover the most desirable item(s) within a given database. Past work on +preference elicitation focused on structured models that provide a factored +representation of users' preferences. Such models require less information to +construct and support efficient reasoning algorithms. This paper makes two +substantial contributions to this area: (1) Strong representation theorems for +factored value functions. (2) A methodology that utilizes our representation +results to address the problem of optimal item selection. +",Compact Value-Function Representations for Qualitative Preferences +" Humans currently use arguments for explaining choices which are already made, +or for evaluating potential choices. Each potential choice has usually pros and +cons of various strengths. In spite of the usefulness of arguments in a +decision making process, there have been few formal proposals handling this +idea if we except works by Fox and Parsons and by Bonet and Geffner. In this +paper we propose a possibilistic logic framework where arguments are built from +an uncertain knowledge base and a set of prioritized goals. The proposed +approach can compute two kinds of decisions by distinguishing between +pessimistic and optimistic attitudes. When the available, maybe uncertain, +knowledge is consistent, as well as the set of prioritized goals (which have to +be fulfilled as far as possible), the method for evaluating decisions on the +basis of arguments agrees with the possibility theory-based approach to +decision-making under uncertainty. Taking advantage of its relation with formal +approaches to defeasible argumentation, the proposed framework can be +generalized in case of partially inconsistent knowledge, or goal bases. +",Using arguments for making decisions: A possibilistic logic approach +" We introduce a probabilistic formalism subsuming Markov random fields of +bounded tree width and probabilistic context free grammars. Our models are +based on a representation of Boolean formulas that we call case-factor diagrams +(CFDs). CFDs are similar to binary decision diagrams (BDDs) but are concise for +circuits of bounded tree width (unlike BDDs) and can concisely represent the +set of parse trees over a given string undera given context free grammar (also +unlike BDDs). A probabilistic model consists of aCFD defining a feasible set of +Boolean assignments and a weight (or cost) for each individual Boolean +variable. We give an insideoutside algorithm for simultaneously computing the +marginal of each Boolean variable, and a Viterbi algorithm for finding the +mininum cost variable assignment. Both algorithms run in time proportional to +the size of the CFD. +",Case-Factor Diagrams for Structured Probabilistic Modeling +" Based on a recent development in the area of error control coding, we +introduce the notion of convolutional factor graphs (CFGs) as a new class of +probabilistic graphical models. In this context, the conventional factor graphs +are referred to as multiplicative factor graphs (MFGs). This paper shows that +CFGs are natural models for probability functions when summation of independent +latent random variables is involved. In particular, CFGs capture a large class +of linear models, where the linearity is in the sense that the observed +variables are obtained as a linear ransformation of the latent variables taking +arbitrary distributions. We use Gaussian models and independent factor models +as examples to emonstrate the use of CFGs. The requirement of a linear +transformation between latent variables (with certain independence restriction) +and the bserved variables, to an extent, limits the modelling flexibility of +CFGs. This structural restriction however provides a powerful analytic tool to +the framework of CFGs; that is, upon taking the Fourier transform of the +function represented by the CFG, the resulting function is represented by a FG +with identical structure. This Fourier transform duality allows inference +problems on a CFG to be solved on the corresponding dual MFG. +",Convolutional Factor Graphs as Probabilistic Models +" As real-world Bayesian networks continue to grow larger and more complex, it +is important to investigate the possibilities for improving the performance of +existing algorithms of probabilistic inference. Motivated by examples, we +investigate the dependency of the performance of Lazy propagation on the +message computation algorithm. We show how Symbolic Probabilistic Inference +(SPI) and Arc-Reversal (AR) can be used for computation of clique to clique +messages in the addition to the traditional use of Variable Elimination (VE). +In addition, the paper resents the results of an empirical evaluation of the +performance of Lazy propagation using VE, SPI, and AR as the message +computation algorithm. The results of the empirical evaluation show that for +most networks, the performance of inference did not depend on the choice of +message computation algorithm, but for some randomly generated networks the +choice had an impact on both space and time performance. In the cases where the +choice had an impact, AR produced the best results. +",An Empirical Evaluation of Possible Variations of Lazy Propagation +" Although many real-world stochastic planning problems are more naturally +formulated by hybrid models with both discrete and continuous variables, +current state-of-the-art methods cannot adequately address these problems. We +present the first framework that can exploit problem structure for modeling and +solving hybrid problems efficiently. We formulate these problems as hybrid +Markov decision processes (MDPs with continuous and discrete state and action +variables), which we assume can be represented in a factored way using a hybrid +dynamic Bayesian network (hybrid DBN). This formulation also allows us to apply +our methods to collaborative multiagent settings. We present a new linear +program approximation method that exploits the structure of the hybrid MDP and +lets us compute approximate value functions more efficiently. In particular, we +describe a new factored discretization of continuous variables that avoids the +exponential blow-up of traditional approaches. We provide theoretical bounds on +the quality of such an approximation and on its scale-up potential. We support +our theoretical arguments with experiments on a set of control problems with up +to 28-dimensional continuous state space and 22-dimensional action space. +",Solving Factored MDPs with Continuous and Discrete Variables +" Maximum a Posteriori assignment (MAP) is the problem of finding the most +probable instantiation of a set of variables given the partial evidence on the +other variables in a Bayesian network. MAP has been shown to be a NP-hard +problem [22], even for constrained networks, such as polytrees [18]. Hence, +previous approaches often fail to yield any results for MAP problems in large +complex Bayesian networks. To address this problem, we propose AnnealedMAP +algorithm, a simulated annealing-based MAP algorithm. The AnnealedMAP algorithm +simulates a non-homogeneous Markov chain whose invariant function is a +probability density that concentrates itself on the modes of the target +density. We tested this algorithm on several real Bayesian networks. The +results show that, while maintaining good quality of the MAP solutions, the +AnnealedMAP algorithm is also able to solve many problems that are beyond the +reach of previous approaches. +",Annealed MAP +" For many real-life Bayesian networks, common knowledge dictates that the +output established for the main variable of interest increases with higher +values for the observable variables. We define two concepts of monotonicity to +capture this type of knowledge. We say that a network is isotone in +distribution if the probability distribution computed for the output variable +given specific observations is stochastically dominated by any such +distribution given higher-ordered observations; a network is isotone in mode if +a probability distribution given higher observations has a higher mode. We show +that establishing whether a network exhibits any of these properties of +monotonicity is coNPPP-complete in general, and remains coNP-complete for +polytrees. We present an approximate algorithm for deciding whether a network +is monotone in distribution and illustrate its application to a real-life +network in oncology. +",Monotonicity in Bayesian Networks +" We present a novel POMDP planning algorithm called heuristic search value +iteration (HSVI).HSVI is an anytime algorithm that returns a policy and a +provable bound on its regret with respect to the optimal policy. HSVI gets its +power by combining two well-known techniques: attention-focusing search +heuristics and piecewise linear convex representations of the value function. +HSVI's soundness and convergence have been proven. On some benchmark problems +from the literature, HSVI displays speedups of greater than 100 with respect to +other state-of-the-art POMDP value iteration algorithms. We also apply HSVI to +a new rover exploration problem 10 times larger than most POMDP problems in the +literature. +",Heuristic Search Value Iteration for POMDPs +" We characterize probabilities in Bayesian networks in terms of algebraic +expressions called quasi-probabilities. These are arrived at by casting +Bayesian networks as noisy AND-OR-NOT networks, and viewing the subnetworks +that lead to a node as arguments for or against a node. Quasi-probabilities are +in a sense the ""natural"" algebra of Bayesian networks: we can easily compute +the marginal quasi-probability of any node recursively, in a compact form; and +we can obtain the joint quasi-probability of any set of nodes by multiplying +their marginals (using an idempotent product operator). Quasi-probabilities are +easily manipulated to improve the efficiency of probabilistic inference. They +also turn out to be representable as square-wave pulse trains, and joint and +marginal distributions can be computed by multiplication and complementation of +pulse trains. +",A New Characterization of Probabilities in Bayesian Networks +" The sensitivities revealed by a sensitivity analysis of a probabilistic +network typically depend on the entered evidence. For a real-life network +therefore, the analysis is performed a number of times, with different +evidence. Although efficient algorithms for sensitivity analysis exist, a +complete analysis is often infeasible because of the large range of possible +combinations of observations. In this paper we present a method for studying +sensitivities that are invariant to the evidence entered. Our method builds +upon the idea of establishing bounds between which a parameter can be varied +without ever inducing a change in the most likely value of a variable of +interest. +",Evidence-invariant Sensitivity Bounds +" We consider the problem of estimating the distribution underlying an observed +sample of data. Instead of maximum likelihood, which maximizes the probability +of the ob served values, we propose a different estimate, the high-profile +distribution, which maximizes the probability of the observed profile the +number of symbols appearing any given number of times. We determine the +high-profile distribution of several data samples, establish some of its +general properties, and show that when the number of distinct symbols observed +is small compared to the data size, the high-profile and maximum-likelihood +distributions are roughly the same, but when the number of symbols is large, +the distributions differ, and high-profile better explains the data. +",On Modeling Profiles instead of Values +" A diagnostic policy specifies what test to perform next, based on the results +of previous tests, and when to stop and make a diagnosis. Cost-sensitive +diagnostic policies perform tradeoffs between (a) the cost of tests and (b) the +cost of misdiagnoses. An optimal diagnostic policy minimizes the expected total +cost. We formalize this diagnosis process as a Markov Decision Process (MDP). +We investigate two types of algorithms for solving this MDP: systematic search +based on AO* algorithm and greedy search (particularly the Value of Information +method). We investigate the issue of learning the MDP probabilities from +examples, but only as they are relevant to the search for good policies. We do +not learn nor assume a Bayesian network for the diagnosis process. Regularizers +are developed to control overfitting and speed up the search. This research is +the first that integrates overfitting prevention into systematic search. The +paper has two contributions: it discusses the factors that make systematic +search feasible for diagnosis, and it shows experimentally, on benchmark data +sets, that systematic search methods produce better diagnostic policies than +greedy methods. +",Learning Diagnostic Policies from Examples by Systematic Search +" Mixtures of truncated exponentials (MTE) potentials are an alternative to +discretization for representing continuous chance variables in influence +diagrams. Also, MTE potentials can be used to approximate utility functions. +This paper introduces MTE influence diagrams, which can represent decision +problems without restrictions on the relationships between continuous and +discrete chance variables, without limitations on the distributions of +continuous chance variables, and without limitations on the nature of the +utility functions. In MTE influence diagrams, all probability distributions and +the joint utility function (or its multiplicative factors) are represented by +MTE potentials and decision nodes are assumed to have discrete state spaces. +MTE influence diagrams are solved by variable elimination using a fusion +algorithm. +",Hybrid Influence Diagrams Using Mixtures of Truncated Exponentials +" Straightedge and compass construction problems are one of the oldest and most +challenging problems in elementary mathematics. The central challenge, for a +human or for a computer program, in solving construction problems is a huge +search space. In this paper we analyze one family of triangle construction +problems, aiming at detecting a small core of the underlying geometry +knowledge. The analysis leads to a small set of needed definitions, lemmas and +primitive construction steps, and consequently, to a simple algorithm for +automated solving of problems from this family. The same approach can be +applied to other families of construction problems. +",Towards Understanding Triangle Construction Problems +" In this article we introduce the Arcade Learning Environment (ALE): both a +challenge problem and a platform and methodology for evaluating the development +of general, domain-independent AI technology. ALE provides an interface to +hundreds of Atari 2600 game environments, each one different, interesting, and +designed to be a challenge for human players. ALE presents significant research +challenges for reinforcement learning, model learning, model-based planning, +imitation learning, transfer learning, and intrinsic motivation. Most +importantly, it provides a rigorous testbed for evaluating and comparing +approaches to these problems. We illustrate the promise of ALE by developing +and benchmarking domain-independent agents designed using well-established AI +techniques for both reinforcement learning and planning. In doing so, we also +propose an evaluation methodology made possible by ALE, reporting empirical +results on over 55 different games. All of the software, including the +benchmark agents, is publicly available. +","The Arcade Learning Environment: An Evaluation Platform for General + Agents" +" Most merging operators are defined by semantics methods which have very high +computational complexity. In order to have operators with a lower computational +complexity, some merging operators defined in a syntactical way have be +proposed. In this work we define some syntactical merging operators and +exploring its rationality properties. To do that we constrain the belief bases +to be sets of formulas very close to logic programs and the underlying logic is +defined through forward chaining rule (Modus Ponens). We propose two types of +operators: arbitration operators when the inputs are only two bases and fusion +with integrity constraints operators. We introduce a set of postulates inspired +of postulates LS, proposed by Liberatore and Shaerf and then we analyzed the +first class of operators through these postulates. We also introduce a set of +postulates inspired of postulates KP, proposed by Konieczny and Pino P\'erez +and then we analyzed the second class of operators through these postulates. +","Exploring the rationality of some syntactic merging operators (extended + version)" +" The Direct Torque Control (DTC) is well known as an effective control +technique for high performance drives in a wide variety of industrial +applications and conventional DTC technique uses two constant reference value: +torque and stator flux. In this paper, fuzzy logic based stator flux +optimization technique for DTC drives that has been proposed. The proposed +fuzzy logic based stator flux optimizer self-regulates the stator flux +reference using induction motor load situation without need of any motor +parameters. Simulation studies have been carried out with Matlab/Simulink to +compare the proposed system behaviors at vary load conditions. Simulation +results show that the performance of the proposed DTC technique has been +improved and especially at low-load conditions torque ripple are greatly +reduced with respect to the conventional DTC. +",Stator flux optimization on direct torque control with fuzzy logic +" Sequential decision problems are often approximately solvable by simulating +possible future action sequences. {\em Metalevel} decision procedures have been +developed for selecting {\em which} action sequences to simulate, based on +estimating the expected improvement in decision quality that would result from +any particular simulation; an example is the recent work on using bandit +algorithms to control Monte Carlo tree search in the game of Go. In this paper +we develop a theoretical basis for metalevel decisions in the statistical +framework of Bayesian {\em selection problems}, arguing (as others have done) +that this is more appropriate than the bandit framework. We derive a number of +basic results applicable to Monte Carlo selection problems, including the first +finite sampling bounds for optimal policies in certain cases; we also provide a +simple counterexample to the intuitive conjecture that an optimal policy will +necessarily reach a decision in all cases. We then derive heuristic +approximations in both Bayesian and distribution-free settings and demonstrate +their superiority to bandit-based heuristics in one-shot decision problems and +in Go. +",Selecting Computations: Theory and Applications +" The rules of Sudoku are often specified using twenty seven +\texttt{all\_different} constraints, referred to as the {\em big} \mrules. +Using graphical proofs and exploratory logic programming, the following main +and new result is obtained: many subsets of six of these big \mrules are +redundant (i.e., they are entailed by the remaining twenty one \mrules), and +six is maximal (i.e., removing more than six \mrules is not possible while +maintaining equivalence). The corresponding result for binary inequality +constraints, referred to as the {\em small} \mrules, is stated as a conjecture. +",Redundant Sudoku Rules +" A recently identified problem is that of finding an optimal investment plan +for a transportation network, given that a disaster such as an earthquake may +destroy links in the network. The aim is to strengthen key links to preserve +the expected network connectivity. A network based on the Istanbul highway +system has thirty links and therefore a billion scenarios, but it has been +estimated that sampling a million scenarios gives reasonable accuracy. In this +paper we use symmetry reasoning to reduce the number of scenarios to a much +smaller number, making sampling unnecessary. This result can be used to +facilitate metaheuristic and exact approaches to the problem. +",Earthquake Scenario Reduction by Symmetry Reasoning +" There is increasing awareness in the planning community that depending on +complete models impedes the applicability of planning technology in many real +world domains where the burden of specifying complete domain models is too +high. In this paper, we consider a novel solution for this challenge that +combines generative planning on incomplete domain models with a library of plan +cases that are known to be correct. While this was arguably the original +motivation for case-based planning, most existing case-based planners assume +(and depend on) from-scratch planners that work on complete domain models. In +contrast, our approach views the plan generated with respect to the incomplete +model as a ""skeletal plan"" and augments it with directed mining of plan +fragments from library cases. We will present the details of our approach and +present an empirical evaluation of our method in comparison to a +state-of-the-art case-based planner that depends on complete domain models. +",Model-Lite Case-Based Planning +" Gas Transmission Networks are large-scale complex systems, and corresponding +design and control problems are challenging. In this paper, we consider the +problem of control and management of these systems in crisis situations. We +present these networks by a hybrid systems framework that provides required +analysis models. Further, we discuss decision-making using computational +discrete and hybrid optimization methods. In particular, several reinforcement +learning methods are employed to explore decision space and achieve the best +policy in a specific crisis situation. Simulations are presented to illustrate +the efficiency of the method. +",Hybrid systems modeling for gas transmission network +" Fuzzy rule based classification systems are one of the most popular fuzzy +modeling systems used in pattern classification problems. This paper +investigates the effect of applying nine different T-norms in fuzzy rule based +classification systems. In the recent researches, fuzzy versions of confidence +and support merits from the field of data mining have been widely used for both +rules selecting and weighting in the construction of fuzzy rule based +classification systems. For calculating these merits the product has been +usually used as a T-norm. In this paper different T-norms have been used for +calculating the confidence and support measures. Therefore, the calculations in +rule selection and rule weighting steps (in the process of constructing the +fuzzy rule based classification systems) are modified by employing these +T-norms. Consequently, these changes in calculation results in altering the +overall accuracy of rule based classification systems. Experimental results +obtained on some well-known data sets show that the best performance is +produced by employing the Aczel-Alsina operator in terms of the classification +accuracy, the second best operator is Dubois-Prade and the third best operator +is Dombi. In experiments, we have used 12 data sets with numerical attributes +from the University of California, Irvine machine learning repository (UCI). +",Comparison of different T-norm operators in classification problems +" This paper presents a novel fuzzy logic based Adaptive Super-twisting Sliding +Mode Controller for the control of dynamic uncertain systems. The proposed +controller combines the advantages of Second order Sliding Mode Control, Fuzzy +Logic Control and Adaptive Control. The reaching conditions, stability and +robustness of the system with the proposed controller are guaranteed. In +addition, the proposed controller is well suited for simple design and +implementation. The effectiveness of the proposed controller over the first +order Sliding Mode Fuzzy Logic controller is illustrated by Matlab based +simulations performed on a DC-DC Buck converter. Based on this comparison, the +proposed controller is shown to obtain the desired transient response without +causing chattering and error under steady-state conditions. The proposed +controller is able to give robust performance in terms of rejection to input +voltage variations and load variations. +","A Novel Fuzzy Logic Based Adaptive Supertwisting Sliding Mode Control + Algorithm for Dynamic Uncertain Systems" +" This paper deals with the implementation of Least Mean Square (LMS) algorithm +in Decision Feedback Equalizer (DFE) for removal of Inter Symbol Interference +(ISI) at the receiver. The channel disrupts the transmitted signal by spreading +it in time. Although, the LMS algorithm is robust and reliable, it is slow in +convergence. In order to increase the speed of convergence, modifications have +been made in the algorithm where the weights get updated depending on the +severity of disturbance. +",Elimination of ISI Using Improved LMS Based Decision Feedback Equalizer +" The early classifications of the computational complexity of planning under +various restrictions in STRIPS (Bylander) and SAS+ (Baeckstroem and Nebel) have +influenced following research in planning in many ways. We go back and +reanalyse their subclasses, but this time using the more modern tool of +parameterized complexity analysis. This provides new results that together with +the old results give a more detailed picture of the complexity landscape. We +demonstrate separation results not possible with standard complexity theory, +which contributes to explaining why certain cases of planning have seemed +simpler in practice than theory has predicted. In particular, we show that +certain restrictions of practical interest are tractable in the parameterized +sense of the term, and that a simple heuristic is sufficient to make a +well-known partial-order planner exploit this fact. +",The Complexity of Planning Revisited - A Parameterized Analysis +" Cumulative resource constraints can model scarce resources in scheduling +problems or a dimension in packing and cutting problems. In order to +efficiently solve such problems with a constraint programming solver, it is +important to have strong and fast propagators for cumulative resource +constraints. One such propagator is the recently developed +time-table-edge-finding propagator, which considers the current resource +profile during the edge-finding propagation. Recently, lazy clause generation +solvers, i.e. constraint programming solvers incorporating nogood learning, +have proved to be excellent at solving scheduling and cutting problems. For +such solvers, concise and accurate explanations of the reasons for propagation +are essential for strong nogood learning. In this paper, we develop the first +explaining version of time-table-edge-finding propagation and show preliminary +results on resource-constrained project scheduling problems from various +standard benchmark suites. On the standard benchmark suite PSPLib, we were able +to close one open instance and to improve the lower bound of about 60% of the +remaining open instances. Moreover, 6 of those instances were closed. +","Explaining Time-Table-Edge-Finding Propagation for the Cumulative + Resource Constraint" +" In the field of ontology matching, the most systematic evaluation of matching +systems is established by the Ontology Alignment Evaluation Initiative (OAEI), +which is an annual campaign for evaluating ontology matching systems organized +by different groups of researchers. In this paper, we report on the results of +an intermediary OAEI campaign called OAEI 2011.5. The evaluations of this +campaign are divided in five tracks. Three of these tracks are new or have been +improved compared to previous OAEI campaigns. Overall, we evaluated 18 matching +systems. We discuss lessons learned, in terms of scalability, multilingual +issues and the ability do deal with real world cases from different domains. +","Evaluating Ontology Matching Systems on Large, Multilingual and + Real-world Test Cases" +" Today, we can find many search engines which provide us with information +which is more operational in nature. None of the search engines provide domain +specific information. This becomes very troublesome to a novice user who wishes +to have information in a particular domain. In this paper, we have developed an +ontology which can be used by a domain specific search engine. We have +developed an ontology on human anatomy, which captures information regarding +cardiovascular system, digestive system, skeleton and nervous system. This +information can be used by people working in medical and health care domain. +",OntoAna: Domain Ontology for Human Anatomy +" Various methods for lifted probabilistic inference have been proposed, but +our understanding of these methods and the relationships between them is still +limited, compared to their propositional counterparts. The only existing +theoretical characterization of lifting is for weighted first-order model +counting (WFOMC), which was shown to be complete domain-lifted for the class of +2-logvar models. This paper makes two contributions to lifted variable +elimination (LVE). First, we introduce a novel inference operator called group +inversion. Second, we prove that LVE augmented with this operator is complete +in the same sense as WFOMC. +",Lifted Variable Elimination: A Novel Operator and Completeness Results +" The Generalized Traveling Salesman Problem (GTSP) is one of the NP-hard +combinatorial optimization problems. A variant of GTSP is E-GTSP where E, +meaning equality, has the constraint: exactly one node from a cluster of a +graph partition is visited. The main objective of the E-GTSP is to find a +minimum cost tour passing through exactly one node from each cluster of an +undirected graph. Agent-based approaches involving are successfully used +nowadays for solving real life complex problems. The aim of the current paper +is to illustrate some variants of agent-based algorithms including ant-based +models with specific properties for solving E-GTSP. +","A Unifying Survey of Reinforced, Sensitive and Stigmergic Agent-Based + Approaches for E-GTSP" +" The current paper introduces a new parallel computing technique based on ant +colony optimization for a dynamic routing problem. In the dynamic traveling +salesman problem the distances between cities as travel times are no longer +fixed. The new technique uses a parallel model for a problem variant that +allows a slight movement of nodes within their Neighborhoods. The algorithm is +tested with success on several large data sets. +",Parallel ACO with a Ring Neighborhood for Dynamic TSP +" This is the Proceedings of the Twenty-Fourth Conference on Uncertainty in +Artificial Intelligence, which was held in Helsinki, Finland, July 9 - 12 2008. +","Proceedings of the Twenty-Fourth Conference on Uncertainty in Artificial + Intelligence (2008)" +" This is the Proceedings of the Twenty-Third Conference on Uncertainty in +Artificial Intelligence, which was held in Vancouver, British Columbia, July 19 +- 22 2007. +","Proceedings of the Twenty-Third Conference on Uncertainty in Artificial + Intelligence (2007)" +" This is the Proceedings of the Twenty-First Conference on Uncertainty in +Artificial Intelligence, which was held in Edinburgh, Scotland July 26 - 29 +2005. +","Proceedings of the Twenty-First Conference on Uncertainty in Artificial + Intelligence (2005)" +" This is the Proceedings of the Twenty-Second Conference on Uncertainty in +Artificial Intelligence, which was held in Cambridge, MA, July 13 - 16 2006. +","Proceedings of the Twenty-Second Conference on Uncertainty in Artificial + Intelligence (2006)" +" This is the Proceedings of the Twentieth Conference on Uncertainty in +Artificial Intelligence, which was held in Banff, Canada, July 7 - 11 2004. +","Proceedings of the Twentieth Conference on Uncertainty in Artificial + Intelligence (2004)" +" In this paper it is introduced a biobjective ant algorithm for constructing +low cost routing networks. The new algorithm is called the Distributed Pharaoh +System (DPS). DPS is based on AntNet algorithm. The algorithm is using Pharaoh +Ant System (PAS) with an extra-exploration phase and a 'no-entry' condition in +order to improve the solutions for the Low Cost Network Routing problem. +Additionally it is used a cost model for overlay network construction that +includes network traffic demands. The Pharaoh ants (Monomorium pharaonis) +includes negative pheromones with signals concentrated at decision points where +trails fork. The negative pheromones may complement positive pheromone or could +help ants to escape from an unnecessarily long route to food that is being +reinforced by attractive signals. Numerical experiments were made for a random +10-node network. The average node degree of the network tested was 4.0. The +results are encouraging. The algorithm converges to the shortest path while +converging on a low cost overlay routing network topology. +",Distributed Pharaoh System for Network Routing +" The Matrix Bandwidth Minimization Problem (MBMP) seeks for a simultaneous +reordering of the rows and the columns of a square matrix such that the nonzero +entries are collected within a band of small width close to the main diagonal. +The MBMP is a NP-complete problem, with applications in many scientific +domains, linear systems, artificial intelligence, and real-life situations in +industry, logistics, information recovery. The complex problems are hard to +solve, that is why any attempt to improve their solutions is beneficent. +Genetic algorithms and ant-based systems are Soft Computing methods used in +this paper in order to solve some MBMP instances. Our approach is based on a +learning agent-based model involving a local search procedure. The algorithm is +compared with the classical Cuthill-McKee algorithm, and with a hybrid genetic +algorithm, using several instances from Matrix Market collection. Computational +experiments confirm a good performance of the proposed algorithms for the +considered set of MBMP instances. On Soft Computing basis, we also propose a +new theoretical Reinforcement Learning model for solving the MBMP problem. +",Soft Computing approaches on the Bandwidth Problem +" Network intrusion detection systems have become a crucial issue for computer +systems security infrastructures. Different methods and algorithms are +developed and proposed in recent years to improve intrusion detection systems. +The most important issue in current systems is that they are poor at detecting +novel anomaly attacks. These kinds of attacks refer to any action that +significantly deviates from the normal behaviour which is considered intrusion. +This paper proposed a model to improve this problem based on data mining +techniques. Apriori algorithm is used to predict novel attacks and generate +real-time rules for firewall. Apriori algorithm extracts interesting +correlation relationships among large set of data items. This paper illustrates +how to use Apriori algorithm in intrusion detection systems to cerate a +automatic firewall rules generator to detect novel anomaly attack. Apriori is +the best-known algorithm to mine association rules. This is an innovative way +to find association rules on large scale. +","Automatic firewall rules generator for anomaly detection systems with + Apriori algorithm" +" Two-dimensional bin packing problems are highly relevant combinatorial +optimization problems. They find a large number of applications, for example, +in the context of transportation or warehousing, and for the cutting of +different materials such as glass, wood or metal. In this work we deal with the +oriented two-dimensional bin packing problem under free guillotine cutting. In +this specific problem a set of oriented rectangular items is given which must +be packed into a minimum number of bins of equal size. The first algorithm +proposed in this work is a randomized multi-start version of a constructive +one-pass heuristic from the literature. Additionally we propose the use of this +randomized one-pass heuristic within an evolutionary algorithm. The results of +the two proposed algorithms are compared to the best approaches from the +literature. In particular the evolutionary algorithm compares very favorably to +current state-of-the-art approaches. The optimal solution for 4 previously +unsolved instances could be found. +","On Solving the Oriented Two-Dimensional Bin Packing Problem under Free + Guillotine Cutting: Exploiting the Power of Probabilistic Solution + Construction" +" Modern ontology debugging methods allow efficient identification and +localization of faulty axioms defined by a user while developing an ontology. +The ontology development process in this case is characterized by rather +frequent and regular calls to a reasoner resulting in an early user awareness +of modeling errors. In such a scenario an ontology usually includes only a +small number of conflict sets, i.e. sets of axioms preserving the faults. This +property allows efficient use of standard model-based diagnosis techniques +based on the application of hitting set algorithms to a number of given +conflict sets. However, in many use cases such as ontology alignment the +ontologies might include many more conflict sets than in usual ontology +development settings, thus making precomputation of conflict sets and +consequently ontology diagnosis infeasible. In this paper we suggest a +debugging approach based on a direct computation of diagnoses that omits +calculation of conflict sets. Embedded in an ontology debugger, the proposed +algorithm is able to identify diagnoses for an ontology which includes a large +number of faults and for which application of standard diagnosis methods fails. +The evaluation results show that the approach is practicable and is able to +identify a fault in adequate time. +",Direct computation of diagnoses for ontology debugging +" The matrices and their sub-blocks are introduced into the study of +determining various extensions in the sense of Dung's theory of argumentation +frameworks. It is showed that each argumentation framework has its matrix +representations, and the core semantics defined by Dung can be characterized by +specific sub-blocks of the matrix. Furthermore, the elementary permutations of +a matrix are employed by which an efficient matrix approach for finding out all +extensions under a given semantics is obtained. Different from several +established approaches, such as the graph labelling algorithm, Constraint +Satisfaction Problem algorithm, the matrix approach not only put the mathematic +idea into the investigation for finding out various extensions, but also +completely achieve the goal to compute all the extensions needed. +",A matrix approach for computing extensions of argumentation frameworks +" The aim of this paper is to develop a methodology that is useful for +analysing from a microeconomic perspective the incentives to entry, permanence +and exit in the market for pharmaceutical generics under fuzzy conditions. In +an empirical application of our proposed methodology, the potential towards +permanence of labs with different characteristics has been estimated. The case +we deal with is set in an open market where global players diversify into +different national markets of pharmaceutical generics. Risk issues are +significantly important in deterring decision makers from expanding in the +generic pharmaceutical business. However, not all players are affected in the +same way and/or to the same extent. Small, non-diversified generics labs are in +the worse position. We have highlighted that the expected NPV and the number of +generics in the portfolio of a pharmaceutical lab are important variables, but +that it is also important to consider the degree of diversification. Labs with +a higher potential for diversification across markets have an advantage over +smaller labs. We have described a fuzzy decision support system based on the +Mamdani model in order to determine the incentives for a laboratory to remain +in the market both when it is stable and when it is growing. +","On firm specific characteristics of pharmaceutical generics and + incentives to permanence under fuzzy conditions" +" Several variants of the Constraint Satisfaction Problem have been proposed +and investigated in the literature for modelling those scenarios where +solutions are associated with some given costs. Within these frameworks +computing an optimal solution is an NP-hard problem in general; yet, when +restricted over classes of instances whose constraint interactions can be +modelled via (nearly-)acyclic graphs, this problem is known to be solvable in +polynomial time. In this paper, larger classes of tractable instances are +singled out, by discussing solution approaches based on exploiting hypergraph +acyclicity and, more generally, structural decomposition methods, such as +(hyper)tree decompositions. +","Tractable Optimization Problems through Hypergraph-Based Structural + Restrictions" +" Efficient ontology debugging is a cornerstone for many activities in the +context of the Semantic Web, especially when automatic tools produce (parts of) +ontologies such as in the field of ontology matching. The best currently known +interactive debugging systems rely upon some meta information in terms of fault +probabilities, which can speed up the debugging procedure in the good case, but +can also have negative impact on the performance in the bad case. The problem +is that assessment of the meta information is only possible a-posteriori. +Consequently, as long as the actual fault is unknown, there is always some risk +of suboptimal interactive diagnoses discrimination. As an alternative, one +might prefer to rely on a tool which pursues a no-risk strategy. In this case, +however, possibly well-chosen meta information cannot be exploited, resulting +again in inefficient debugging actions. In this work we present a reinforcement +learning strategy that continuously adapts its behavior depending on the +performance achieved and minimizes the risk of using low-quality meta +information. Therefore, this method is suitable for application scenarios where +reliable a-priori fault estimates are difficult to obtain. Using problematic +ontologies in the field of ontology matching, we show that the proposed +risk-aware query strategy outperforms both active learning approaches and +no-risk strategies on average in terms of required amount of user interaction. +",RIO: Minimizing User Interaction in Ontology Debugging +" In Programming by Example, a system attempts to infer a program from input +and output examples, generally by searching for a composition of certain base +functions. Performing a naive brute force search is infeasible for even mildly +involved tasks. We note that the examples themselves often present clues as to +which functions to compose, and how to rank the resulting programs. In text +processing, which is our domain of interest, clues arise from simple textual +features: for example, if parts of the input and output strings are +permutations of one another, this suggests that sorting may be useful. We +describe a system that learns the reliability of such clues, allowing for +faster search and a principled ranking over programs. Experiments on a +prototype of this system show that this learning scheme facilitates efficient +inference on a range of text processing tasks. +",Textual Features for Programming by Example +" Knowledge representation (KR) and inference mechanism are most desirable +thing to make the system intelligent. System is known to an intelligent if its +intelligence is equivalent to the intelligence of human being for a particular +domain or general. Because of incomplete ambiguous and uncertain information +the task of making intelligent system is very difficult. The objective of this +paper is to present the hybrid KR technique for making the system effective & +Optimistic. The requirement for (effective & optimistic) is because the system +must be able to reply the answer with a confidence of some factor. This paper +also presents the comparison between various hybrid KR techniques with the +proposed one. +","Hybrid technique for effective knowledge representation & a comparative + study" +" Existing theoretical universal algorithmic intelligence models are not +practically realizable. More pragmatic approach to artificial general +intelligence is based on cognitive architectures, which are, however, +non-universal in sense that they can construct and use models of the +environment only from Turing-incomplete model spaces. We believe that the way +to the real AGI consists in bridging the gap between these two approaches. This +is possible if one considers cognitive functions as a ""cognitive bias"" (priors +and search heuristics) that should be incorporated into the models of universal +algorithmic intelligence without violating their universality. Earlier reported +results suiting this approach and its overall feasibility are discussed on the +example of perception, planning, knowledge representation, attention, theory of +mind, language, and some others. +",Cognitive Bias for Universal Algorithmic Intelligence +" The paper presents a comparison of various soft computing techniques used for +filtering and enhancing speech signals. The three major techniques that fall +under soft computing are neural networks, fuzzy systems and genetic algorithms. +Other hybrid techniques such as neuro-fuzzy systems are also available. In +general, soft computing techniques have been experimentally observed to give +far superior performance as compared to non-soft computing techniques in terms +of robustness and accuracy. +",Speech Signal Filters based on Soft Computing Techniques: A Comparison +" This paper discusses the merits and demerits of crisp logic and fuzzy logic +with respect to their applicability in intelligent response generation by a +human being and by a robot. Intelligent systems must have the capability of +taking decisions that are wise and handle situations intelligently. A direct +relationship exists between the level of perfection in handling a situation and +the level of completeness of the available knowledge or information or data +required to handle the situation. The paper concludes that the use of crisp +logic with complete knowledge leads to perfection in handling situations +whereas fuzzy logic can handle situations imperfectly only. However, in the +light of availability of incomplete knowledge fuzzy theory is more effective +but may be disadvantageous as compared to crisp logic. +","Applicability of Crisp and Fuzzy Logic in Intelligent Response + Generation" +" For the past few decades, man has been trying to create an intelligent +computer which can talk and respond like he can. The task of creating a system +that can talk like a human being is the primary objective of Automatic Speech +Recognition. Various Speech Recognition techniques have been developed in +theory and have been applied in practice. This paper discusses the problems +that have been encountered in developing Speech Recognition, the techniques +that have been applied to automate the task, and a representation of the core +problems of present day Speech Recognition by using Fuzzy Mathematics. +","Application of Fuzzy Mathematics to Speech-to-Text Conversion by + Elimination of Paralinguistic Content" +" A definition of Artificial Intelligence was proposed in [1] but this +definition was not absolutely formal at least because the word ""Human"" was +used. In this paper we will formalize the definition from [1]. The biggest +problem in this definition was that the level of intelligence of AI is compared +to the intelligence of a human being. In order to change this we will introduce +some parameters to which AI will depend. One of this parameters will be the +level of intelligence and we will define one AI to each level of intelligence. +We assume that for some level of intelligence the respective AI will be more +intelligent than a human being. Nevertheless, we cannot say which is this level +because we cannot calculate its exact value. +",Formal Definition of AI +" The theory of rough sets is concerned with the lower and upper approximations +of objects through a binary relation on a universe. It has been applied to +machine learning, knowledge discovery and data mining. The theory of matroids +is a generalization of linear independence in vector spaces. It has been used +in combinatorial optimization and algorithm design. In order to take advantages +of both rough sets and matroids, in this paper we propose a matroidal structure +of rough sets based on a serial and transitive relation on a universe. We +define the family of all minimal neighborhoods of a relation on a universe, and +prove it satisfy the circuit axioms of matroids when the relation is serial and +transitive. In order to further study this matroidal structure, we investigate +the inverse of this construction: inducing a relation by a matroid. The +relationships between the upper approximation operators of rough sets based on +relations and the closure operators of matroids in the above two constructions +are studied. Moreover, we investigate the connections between the above two +constructions. +","Matroidal structure of rough sets based on serial and transitive + relations" +" In this paper, we propose a new type of matroids, namely covering matroids, +and investigate the connections with the second type of covering-based rough +sets and some existing special matroids. Firstly, as an extension of +partitions, coverings are more natural combinatorial objects and can sometimes +be more efficient to deal with problems in the real world. Through extending +partitions to coverings, we propose a new type of matroids called covering +matroids and prove them to be an extension of partition matroids. Secondly, +since some researchers have successfully applied partition matroids to +classical rough sets, we study the relationships between covering matroids and +covering-based rough sets which are an extension of classical rough sets. +Thirdly, in matroid theory, there are many special matroids, such as +transversal matroids, partition matroids, 2-circuit matroid and +partition-circuit matroids. The relationships among several special matroids +and covering matroids are studied. +",Covering matroid +" Recently, the relationship between matroids and generalized rough sets based +on relations has been studied from the viewpoint of linear independence of +matrices. In this paper, we reveal more relationships by the predecessor and +successor neighborhoods from relations. First, through these two neighborhoods, +we propose a pair of matroids, namely predecessor relation matroid and +successor relation matroid, respectively. Basic characteristics of this pair of +matroids, such as dependent sets, circuits, the rank function and the closure +operator, are described by the predecessor and successor neighborhoods from +relations. Second, we induce a relation from a matroid through the circuits of +the matroid. We prove that the induced relation is always an equivalence +relation. With these two inductions, a relation induces a relation matroid, and +the relation matroid induces an equivalence relation, then the connection +between the original relation and the induced equivalence relation is studied. +Moreover, the relationships between the upper approximation operator in +generalized rough sets and the closure operator in matroids are investigated. +","Relation matroid and its relationship with generalized rough set based + on relation" +" Rough sets are efficient for data pre-process in data mining. Lower and upper +approximations are two core concepts of rough sets. This paper studies +generalized rough sets based on symmetric and transitive relations from the +operator-oriented view by matroidal approaches. We firstly construct a +matroidal structure of generalized rough sets based on symmetric and transitive +relations, and provide an approach to study the matroid induced by a symmetric +and transitive relation. Secondly, this paper establishes a close relationship +between matroids and generalized rough sets. Approximation quality and +roughness of generalized rough sets can be computed by the circuit of matroid +theory. At last, a symmetric and transitive relation can be constructed by a +matroid with some special properties. +","Matroidal structure of generalized rough sets based on symmetric and + transitive relations" +" At present, practical application and theoretical discussion of rough sets +are two hot problems in computer science. The core concepts of rough set theory +are upper and lower approximation operators based on equivalence relations. +Matroid, as a branch of mathematics, is a structure that generalizes linear +independence in vector spaces. Further, matroid theory borrows extensively from +the terminology of linear algebra and graph theory. We can combine rough set +theory with matroid theory through using rough sets to study some +characteristics of matroids. In this paper, we apply rough sets to matroids +through defining a family of sets which are constructed from the upper +approximation operator with respect to an equivalence relation. First, we prove +the family of sets satisfies the support set axioms of matroids, and then we +obtain a matroid. We say the matroids induced by the equivalence relation and a +type of matroid, namely support matroid, is induced. Second, through rough +sets, some characteristics of matroids such as independent sets, support sets, +bases, hyperplanes and closed sets are investigated. +",Some characteristics of matroids through rough sets +" Neighborhood is an important concept in covering based rough sets. That under +what condition neighborhoods form a partition is a meaningful issue induced by +this concept. Many scholars have paid attention to this issue and presented +some necessary and sufficient conditions. However, there exists one common +trait among these conditions, that is they are established on the basis of all +neighborhoods have been obtained. In this paper, we provide a necessary and +sufficient condition directly based on the covering itself. First, we +investigate the influence of that there are reducible elements in the covering +on neighborhoods. Second, we propose the definition of uniform block and obtain +a sufficient condition from it. Third, we propose the definitions of repeat +degree and excluded number. By means of the two concepts, we obtain a necessary +and sufficient condition for neighborhoods to form a partition. In a word, we +have gained a deeper and more direct understanding of the essence over that +neighborhoods form a partition. +","Condition for neighborhoods in covering based rough sets to form a + partition" +" Rough sets are efficient for data pre-processing in data mining. As a +generalization of the linear independence in vector spaces, matroids provide +well-established platforms for greedy algorithms. In this paper, we apply rough +sets to matroids and study the contraction of the dual of the corresponding +matroid. First, for an equivalence relation on a universe, a matroidal +structure of the rough set is established through the lower approximation +operator. Second, the dual of the matroid and its properties such as +independent sets, bases and rank function are investigated. Finally, the +relationships between the contraction of the dual matroid to the complement of +a single point set and the contraction of the dual matroid to the complement of +the equivalence class of this point are studied. +",Rough sets and matroidal contraction +" It is a meaningful issue that under what condition neighborhoods induced by a +covering are equal to the covering itself. A necessary and sufficient condition +for this issue has been provided by some scholars. In this paper, through a +counter-example, we firstly point out the necessary and sufficient condition is +false. Second, we present a necessary and sufficient condition for this issue. +Third, we concentrate on the inverse issue of computing neighborhoods by a +covering, namely giving an arbitrary covering, whether or not there exists +another covering such that the neighborhoods induced by it is just the former +covering. We present a necessary and sufficient condition for this issue as +well. In a word, through the study on the two fundamental issues induced by +neighborhoods, we have gained a deeper understanding of the relationship +between neighborhoods and the covering which induce the neighborhoods. +","Condition for neighborhoods induced by a covering to be equal to the + covering itself" +" Rough sets are efficient for data pre-processing in data mining. Matroids are +based on linear algebra and graph theory, and have a variety of applications in +many fields. Both rough sets and matroids are closely related to lattices. For +a serial and transitive relation on a universe, the collection of all the +regular sets of the generalized rough set is a lattice. In this paper, we use +the lattice to construct a matroid and then study relationships between the +lattice and the closed-set lattice of the matroid. First, the collection of all +the regular sets based on a serial and transitive relation is proved to be a +semimodular lattice. Then, a matroid is constructed through the height function +of the semimodular lattice. Finally, we propose an approach to obtain all the +closed sets of the matroid from the semimodular lattice. Borrowing from +matroids, results show that lattice theory provides an interesting view to +investigate rough sets. +","Closed-set lattice of regular sets based on a serial and transitive + relation through matroids" +" Covering is a common type of data structure and covering-based rough set +theory is an efficient tool to process this data. Lattice is an important +algebraic structure and used extensively in investigating some types of +generalized rough sets. In this paper, we propose two family of sets and study +the conditions that these two sets become some lattice structures. These two +sets are consisted by the fixed point of the lower approximations of the first +type and the sixth type of covering-based rough sets, respectively. These two +sets are called the fixed point set of neighborhoods and the fixed point set of +covering, respectively. First, for any covering, the fixed point set of +neighborhoods is a complete and distributive lattice, at the same time, it is +also a double p-algebra. Especially, when the neighborhood forms a partition of +the universe, the fixed point set of neighborhoods is both a boolean lattice +and a double Stone algebra. Second, for any covering, the fixed point set of +covering is a complete lattice.When the covering is unary, the fixed point set +of covering becomes a distributive lattice and a double p-algebra. a +distributive lattice and a double p-algebra when the covering is unary. +Especially, when the reduction of the covering forms a partition of the +universe, the fixed point set of covering is both a boolean lattice and a +double Stone algebra. +","Lattice structures of fixed points of the lower approximations of two + types of covering-based rough sets" +" Taaable is a case-based reasoning system that adapts cooking recipes to user +constraints. Within it, the preparation part of recipes is formalised as a +graph. This graph is a semantic representation of the sequence of instructions +composing the cooking process and is used to compute the procedure adaptation, +conjointly with the textual adaptation. It is composed of cooking actions and +ingredients, among others, represented as vertices, and semantic relations +between those, shown as arcs, and is built automatically thanks to natural +language processing. The results of the automatic annotation process is often a +disconnected graph, representing an incomplete annotation, or may contain +errors. Therefore, a validating and correcting step is required. In this paper, +we present an existing graphic tool named \kcatos, conceived for representing +and editing decision trees, and show how it has been adapted and integrated in +WikiTaaable, the semantic wiki in which the knowledge used by Taaable is +stored. This interface provides the wiki users with a way to correct the case +representation of the cooking process, improving at the same time the quality +of the knowledge about cooking procedures stored in WikiTaaable. +","Semi-automatic annotation process for procedural texts: An application + on cooking recipes" +" Efficient Natural Evolution Strategies (eNES) is a novel alternative to +conventional evolutionary algorithms, using the natural gradient to adapt the +mutation distribution. Unlike previous methods based on natural gradients, eNES +uses a fast algorithm to calculate the inverse of the exact Fisher information +matrix, thus increasing both robustness and performance of its evolution +gradient estimation, even in higher dimensions. Additional novel aspects of +eNES include optimal fitness baselines and importance mixing (a procedure for +updating the population with very few fitness evaluations). The algorithm +yields competitive results on both unimodal and multimodal benchmarks. +",Efficient Natural Evolution Strategies +" This paper assumes the hypothesis that human learning is perception based, +and consequently, the learning process and perceptions should not be +represented and investigated independently or modeled in different simulation +spaces. In order to keep the analogy between the artificial and human learning, +the former is assumed here as being based on the artificial perception. Hence, +instead of choosing to apply or develop a Computational Theory of (human) +Perceptions, we choose to mirror the human perceptions in a numeric +(computational) space as artificial perceptions and to analyze the +interdependence between artificial learning and artificial perception in the +same numeric space, using one of the simplest tools of Artificial Intelligence +and Soft Computing, namely the perceptrons. As practical applications, we +choose to work around two examples: Optical Character Recognition and Iris +Recognition. In both cases a simple Turing test shows that artificial +perceptions of the difference between two characters and between two irides are +fuzzy, whereas the corresponding human perceptions are, in fact, crisp. +","Examples of Artificial Perceptions in Optical Character Recognition and + Iris Recognition" +" In E-learning, there is still the problem of knowing how to ensure an +individualized and continuous learner's follow-up during learning process, +indeed among the numerous tools proposed, very few systems concentrate on a +real time learner's follow-up. Our work in this field develops the design and +implementation of a Multi-Agents System Based on Dynamic Case Based Reasoning +which can initiate learning and provide an individualized follow-up of learner. +When interacting with the platform, every learner leaves his/her traces in the +machine. These traces are stored in a basis under the form of scenarios which +enrich collective past experience. The system monitors, compares and analyses +these traces to keep a constant intelligent watch and therefore detect +difficulties hindering progress and/or avoid possible dropping out. The system +can support any learning subject. The success of a case-based reasoning system +depends critically on the performance of the retrieval step used and, more +specifically, on similarity measure used to retrieve scenarios that are similar +to the course of the learner (traces in progress). We propose a complementary +similarity measure, named Inverse Longest Common Sub-Sequence (ILCSS). To help +and guide the learner, the system is equipped with combined virtual and human +tutors. +","Multi-Agents Dynamic Case Based Reasoning and The Inverse Longest Common + Sub-Sequence And Individualized Follow-up of Learners in The CEHL" +" Covering-based rough set theory is a useful tool to deal with inexact, +uncertain or vague knowledge in information systems. Topology, one of the most +important subjects in mathematics, provides mathematical tools and interesting +topics in studying information systems and rough sets. In this paper, we +present the topological characterizations to three types of covering +approximation operators. First, we study the properties of topology induced by +the sixth type of covering lower approximation operator. Second, some +topological characterizations to the covering lower approximation operator to +be an interior operator are established. We find that the topologies induced by +this operator and by the sixth type of covering lower approximation operator +are the same. Third, we study the conditions which make the first type of +covering upper approximation operator be a closure operator, and find that the +topology induced by the operator is the same as the topology induced by the +fifth type of covering upper approximation operator. Forth, the conditions of +the second type of covering upper approximation operator to be a closure +operator and the properties of topology induced by it are established. Finally, +these three topologies space are compared. In a word, topology provides a +useful method to study the covering-based rough sets. +","Topological characterizations to three types of covering approximation + operators" +" Covering-based rough set theory is a useful tool to deal with inexact, +uncertain or vague knowledge in information systems. Geometric lattice has +widely used in diverse fields, especially search algorithm design which plays +important role in covering reductions. In this paper, we construct four +geometric lattice structures of covering-based rough sets through matroids, and +compare their relationships. First, a geometric lattice structure of +covering-based rough sets is established through the transversal matroid +induced by the covering, and its characteristics including atoms, modular +elements and modular pairs are studied. We also construct a one-to-one +correspondence between this type of geometric lattices and transversal matroids +in the context of covering-based rough sets. Second, sufficient and necessary +conditions for three types of covering upper approximation operators to be +closure operators of matroids are presented. We exhibit three types of matroids +through closure axioms, and then obtain three geometric lattice structures of +covering-based rough sets. Third, these four geometric lattice structures are +compared. Some core concepts such as reducible elements in covering-based rough +sets are investigated with geometric lattices. In a word, this work points out +an interesting view, namely geometric lattice, to study covering-based rough +sets. +","Geometric lattice structure of covering-based rough sets through + matroids" +" The measurement error with normal distribution is universal in applications. +Generally, smaller measurement error requires better instrument and higher test +cost. In decision making based on attribute values of objects, we shall select +an attribute subset with appropriate measurement error to minimize the total +test cost. Recently, error-range-based covering rough set with uniform +distribution error was proposed to investigate this issue. However, the +measurement errors satisfy normal distribution instead of uniform distribution +which is rather simple for most applications. In this paper, we introduce +normal distribution measurement errors to covering-based rough set model, and +deal with test-cost-sensitive attribute reduction problem in this new model. +The major contributions of this paper are four-fold. First, we build a new data +model based on normal distribution measurement errors. With the new data model, +the error range is an ellipse in a two-dimension space. Second, the +covering-based rough set with normal distribution measurement errors is +constructed through the ""3-sigma"" rule. Third, the test-cost-sensitive +attribute reduction problem is redefined on this covering-based rough set. +Fourth, a heuristic algorithm is proposed to deal with this problem. The +algorithm is tested on ten UCI (University of California - Irvine) datasets. +The experimental results show that the algorithm is more effective and +efficient than the existing one. This study is a step toward realistic +applications of cost-sensitive learning. +","Test-cost-sensitive attribute reduction of data with normal distribution + measurement errors" +" Recently, in order to broad the application and theoretical areas of rough +sets and matroids, some authors have combined them from many different +viewpoints, such as circuits, rank function, spanning sets and so on. In this +paper, we connect the second type of covering-based rough sets and matroids +from the view of closure operators. On one hand, we establish a closure system +through the fixed point family of the second type of covering lower +approximation operator, and then construct a closure operator. For a covering +of a universe, the closure operator is a closure one of a matroid if and only +if the reduct of the covering is a partition of the universe. On the other +hand, we investigate the sufficient and necessary condition that the second +type of covering upper approximation operation is a closure one of a matroid. +","Relationship between the second type of covering-based rough set and + matroid via closure operator" +" The questions which we will consider here are ""What is AI?"" and ""How can we +make AI?"". Here we will present the definition of AI in terms of multi-agent +systems. This means that here you will not find a new answer to the question +""What is AI?"", but an old answer in a new form. + This new form of the definition of AI is of interest for the theory of +multi-agent systems because it gives us better understanding of this theory. +More important is that this work will help us answer the second question. We +want to make a program which is capable of constructing a model of its +environment. Every multi-agent model is equivalent to a single-agent model but +multi-agent models are more natural and accordingly more easily discoverable. +",The Definition of AI in Terms of Multi Agent Systems +" In this paper we offer a formal definition of Artificial Intelligence and +this directly gives us an algorithm for construction of this object. Really, +this algorithm is useless due to the combinatory explosion. + The main innovation in our definition is that it does not include the +knowledge as a part of the intelligence. So according to our definition a newly +born baby also is an Intellect. Here we differs with Turing's definition which +suggests that an Intellect is a person with knowledge gained through the years. +",A Definition of Artificial Intelligence +" Answer Set Programming (ASP) is a well-known problem solving approach based +on nonmonotonic logic programs and efficient solvers. To enable access to +external information, HEX-programs extend programs with external atoms, which +allow for a bidirectional communication between the logic program and external +sources of computation (e.g., description logic reasoners and Web resources). +Current solvers evaluate HEX-programs by a translation to ASP itself, in which +values of external atoms are guessed and verified after the ordinary answer set +computation. This elegant approach does not scale with the number of external +accesses in general, in particular in presence of nondeterminism (which is +instrumental for ASP). In this paper, we present a novel, native algorithm for +evaluating HEX-programs which uses learning techniques. In particular, we +extend conflict-driven ASP solving techniques, which prevent the solver from +running into the same conflict again, from ordinary to HEX-programs. We show +how to gain additional knowledge from external source evaluations and how to +use it in a conflict-driven algorithm. We first target the uninformed case, +i.e., when we have no extra information on external sources, and then extend +our approach to the case where additional meta-information is available. +Experiments show that learning from external sources can significantly decrease +both the runtime and the number of considered candidate compatible sets. +",Conflict-driven ASP Solving with External Sources +" In order to build AI we have to create a program which copes well in an +arbitrary world. In this paper we will restrict our attention on one concrete +world, which represents the game Tick-Tack-Toe. This world is a very simple one +but it is sufficiently complicated for our task because most people cannot +manage with it. The main difficulty in this world is that the player cannot see +the entire internal state of the world so he has to build a model in order to +understand the world. The model which we will offer will consist of final +automata and first order formulas. +",AI in arbitrary world +" Supply Chain coordination has become a critical success factor for Supply +Chain management (SCM) and effectively improving the performance of +organizations in various industries. Companies are increasingly located at the +intersection of one or more corporate networks which are designated by ""Supply +Chain"". Managing this chain is mainly based on an 'information sharing' and +redeployment activities between the various links that comprise it. Several +attempts have been made by industrialists and researchers to educate +policymakers about the gains to be made by the implementation of cooperative +relationships. The approach presented in this paper here is among the works +that aim to propose solutions related to information systems distributed Supply +Chains to enable the different actors of the chain to improve their +performance. We propose in particular solutions that focus on cooperation +between actors in the Supply Chain. +",An Agent-based framework for cooperation in Supply Chain +" Local Optima Networks (LONs) have been recently proposed as an alternative +model of combinatorial fitness landscapes. The model compresses the information +given by the whole search space into a smaller mathematical object that is the +graph having as vertices the local optima and as edges the possible weighted +transitions between them. A new set of metrics can be derived from this model +that capture the distribution and connectivity of the local optima in the +underlying configuration space. This paper departs from the descriptive +analysis of local optima networks, and actively studies the correlation between +network features and the performance of a local search heuristic. The NK family +of landscapes and the Iterated Local Search metaheuristic are considered. With +a statistically-sound approach based on multiple linear regression, it is shown +that some LONs' features strongly influence and can even partly predict the +performance of a heuristic search algorithm. This study validates the +expressive power of LONs as a model of combinatorial fitness landscapes. +",Local optima networks and the performance of iterated local search +" We propose an approach to lifted approximate inference for first-order +probabilistic models, such as Markov logic networks. It is based on performing +exact lifted inference in a simplified first-order model, which is found by +relaxing first-order constraints, and then compensating for the relaxation. +These simplified models can be incrementally improved by carefully recovering +constraints that have been relaxed, also at the first-order level. This leads +to a spectrum of approximations, with lifted belief propagation on one end, and +exact lifted inference on the other. We discuss how relaxation, compensation, +and recovery can be performed, all at the firstorder level, and show +empirically that our approach substantially improves on the approximations of +both propositional solvers and lifted belief propagation. +","Lifted Relax, Compensate and then Recover: From Approximate to Exact + Lifted Probabilistic Inference" +" The MPE (Most Probable Explanation) query plays an important role in +probabilistic inference. MPE solution algorithms for probabilistic relational +models essentially adapt existing belief assessment method, replacing summation +with maximization. But the rich structure and symmetries captured by relational +models together with the properties of the maximization operator offer an +opportunity for additional simplification with potentially significant +computational ramifications. Specifically, these models often have groups of +variables that define symmetric distributions over some population of formulas. +The maximizing choice for different elements of this group is the same. If we +can realize this ahead of time, we can significantly reduce the size of the +model by eliminating a potentially significant portion of random variables. +This paper defines the notion of uniformly assigned and partially uniformly +assigned sets of variables, shows how one can recognize these sets efficiently, +and how the model can be greatly simplified once we recognize them, with little +computational effort. We demonstrate the effectiveness of these ideas +empirically on a number of models. +",Exploiting Uniform Assignments in First-Order MPE +" This paper provides some new guidance in the construction of region graphs +for Generalized Belief Propagation (GBP). We connect the problem of choosing +the outer regions of a LoopStructured Region Graph (SRG) to that of finding a +fundamental cycle basis of the corresponding Markov network. We also define a +new class of tree-robust Loop-SRG for which GBP on any induced (spanning) tree +of the Markov network, obtained by setting to zero the off-tree interactions, +is exact. This class of SRG is then mapped to an equivalent class of +tree-robust cycle bases on the Markov network. We show that a treerobust cycle +basis can be identified by proving that for every subset of cycles, the graph +obtained from the edges that participate in a single cycle only, is multiply +connected. Using this we identify two classes of tree-robust cycle bases: +planar cycle bases and ""star"" cycle bases. In experiments we show that +tree-robustness can be successfully exploited as a design principle to improve +the accuracy and convergence of GBP. +",Generalized Belief Propagation on Tree Robust Structured Region Graphs +" We consider the problem of sampling from solutions defined by a set of hard +constraints on a combinatorial space. We propose a new sampling technique that, +while enforcing a uniform exploration of the search space, leverages the +reasoning power of a systematic constraint solver in a black-box scheme. We +present a series of challenging domains, such as energy barriers and highly +asymmetric spaces, that reveal the difficulties introduced by hard constraints. +We demonstrate that standard approaches such as Simulated Annealing and Gibbs +Sampling are greatly affected, while our new technique can overcome many of +these difficulties. Finally, we show that our sampling scheme naturally defines +a new approximate model counting technique, which we empirically show to be +very accurate on a range of benchmark problems. +",Uniform Solution Sampling Using a Constraint Solver As an Oracle +" Stochastic Shortest Path (SSP) MDPs is a problem class widely studied in AI, +especially in probabilistic planning. They describe a wide range of scenarios +but make the restrictive assumption that the goal is reachable from any state, +i.e., that dead-end states do not exist. Because of this, SSPs are unable to +model various scenarios that may have catastrophic events (e.g., an airplane +possibly crashing if it flies into a storm). Even though MDP algorithms have +been used for solving problems with dead ends, a principled theory of SSP +extensions that would allow dead ends, including theoretically sound algorithms +for solving such MDPs, has been lacking. In this paper, we propose three new +MDP classes that admit dead ends under increasingly weaker assumptions. We +present Value Iteration-based as well as the more efficient heuristic search +algorithms for optimally solving each class, and explore theoretical +relationships between these classes. We also conduct a preliminary empirical +study comparing the performance of our algorithms on different MDP classes, +especially on scenarios with unavoidable dead ends. +",A Theory of Goal-Oriented MDPs with Dead Ends +" We develop several algorithms taking advantage of two common approaches for +bounding MPE queries in graphical models: minibucket elimination and +message-passing updates for linear programming relaxations. Both methods are +quite similar, and offer useful perspectives for the other; our hybrid +approaches attempt to balance the advantages of each. We demonstrate the power +of our hybrid algorithms through extensive empirical evaluation. Most notably, +a Branch and Bound search guided by the heuristic function calculated by one of +our new algorithms has recently won first place in the PASCAL2 inference +challenge. +",Join-graph based cost-shifting schemes +" We consider the problem of selecting a subset of alternatives given noisy +evaluations of the relative strength of different alternatives. We wish to +select a k-subset (for a given k) that provides a maximum likelihood estimate +for one of several objectives, e.g., containing the strongest alternative. +Although this problem is NP-hard, we show that when the noise level is +sufficiently high, intuitive methods provide the optimal solution. We thus +generalize classical results about singling out one alternative and identifying +the hidden ranking of alternatives by strength. Extensive experiments show that +our methods perform well in practical settings. +",A Maximum Likelihood Approach For Selecting Sets of Alternatives +" We study the problem of complexity estimation in the context of parallelizing +an advanced Branch and Bound-type algorithm over graphical models. The +algorithm's pruning power makes load balancing, one crucial element of every +distributed system, very challenging. We propose using a statistical regression +model to identify and tackle disproportionally complex parallel subproblems, +the cause of load imbalance, ahead of time. The proposed model is evaluated and +analyzed on various levels and shown to yield robust predictions. We then +demonstrate its effectiveness for load balancing in practice. +","A Case Study in Complexity Estimation: Towards Parallel Branch-and-Bound + over Graphical Models" +" Influence diagrams allow for intuitive and yet precise description of complex +situations involving decision making under uncertainty. Unfortunately, most of +the problems described by influence diagrams are hard to solve. In this paper +we discuss the complexity of approximately solving influence diagrams. We do +not assume no-forgetting or regularity, which makes the class of problems we +address very broad. Remarkably, we show that when both the tree-width and the +cardinality of the variables are bounded the problem admits a fully +polynomial-time approximation scheme. +",The Complexity of Approximately Solving Influence Diagrams +" Variational inference algorithms such as belief propagation have had +tremendous impact on our ability to learn and use graphical models, and give +many insights for developing or understanding exact and approximate inference. +However, variational approaches have not been widely adoped for decision making +in graphical models, often formulated through influence diagrams and including +both centralized and decentralized (or multi-agent) decisions. In this work, we +present a general variational framework for solving structured cooperative +decision-making problems, use it to propose several belief propagation-like +algorithms, and analyze them both theoretically and empirically. +",Belief Propagation for Structured Decision Making +" We describe multi-objective influence diagrams, based on a set of p +objectives, where utility values are vectors in Rp, and are typically only +partially ordered. These can still be solved by a variable elimination +algorithm, leading to a set of maximal values of expected utility. If the +Pareto ordering is used this set can often be prohibitively large. We consider +approximate representations of the Pareto set based on e-coverings, allowing +much larger problems to be solved. In addition, we define a method for +incorporating user tradeoffs, which also greatly improves the efficiency. +",Multi-objective Influence Diagrams +" Planning in partially observable Markov decision processes (POMDPs) remains a +challenging topic in the artificial intelligence community, in spite of recent +impressive progress in approximation techniques. Previous research has +indicated that online planning approaches are promising in handling large-scale +POMDP domains efficiently as they make decisions ""on demand"" instead of +proactively for the entire state space. We present a Factored Hybrid Heuristic +Online Planning (FHHOP) algorithm for large POMDPs. FHHOP gets its power by +combining a novel hybrid heuristic search strategy with a recently developed +factored state representation. On several benchmark problems, FHHOP +substantially outperformed state-of-the-art online heuristic search approaches +in terms of both scalability and quality. +","FHHOP: A Factored Hybrid Heuristic Online Planning Algorithm for Large + POMDPs" +" We introduce a new cluster-cumulant expansion (CCE) based on the fixed points +of iterative belief propagation (IBP). This expansion is similar in spirit to +the loop-series (LS) recently introduced in [1]. However, in contrast to the +latter, the CCE enjoys the following important qualities: 1) it is defined for +arbitrary state spaces 2) it is easily extended to fixed points of generalized +belief propagation (GBP), 3) disconnected groups of variables will not +contribute to the CCE and 4) the accuracy of the expansion empirically improves +upon that of the LS. The CCE is based on the same M\""obius transform as the +Kikuchi approximation, but unlike GBP does not require storing the beliefs of +the GBP-clusters nor does it suffer from convergence issues during belief +updating. +",A Cluster-Cumulant Expansion at the Fixed Points of Belief Propagation +" Rough set theory is a useful tool to deal with uncertain, granular and +incomplete knowledge in information systems. And it is based on equivalence +relations or partitions. Matroid theory is a structure that generalizes linear +independence in vector spaces, and has a variety of applications in many +fields. In this paper, we propose a new type of matroids, namely, +partition-circuit matroids, which are induced by partitions. Firstly, a +partition satisfies circuit axioms in matroid theory, then it can induce a +matroid which is called a partition-circuit matroid. A partition and an +equivalence relation on the same universe are one-to-one corresponding, then +some characteristics of partition-circuit matroids are studied through rough +sets. Secondly, similar to the upper approximation number which is proposed by +Wang and Zhu, we define the lower approximation number. Some characteristics of +partition-circuit matroids and the dual matroids of them are investigated +through the lower approximation number and the upper approximation number. +",Characteristic of partition-circuit matroid through approximation number +" In this work we investigate the systems that implements algorithms for the +planning problem in Artificial Intelligence, called planners, with especial +attention to the planners based on the plan graph. We analyze the problem of +comparing the performance of the different algorithms and we propose an +environment for the development and analysis of planners. +",Ambiente de Planejamento Ip\^e +" For the exploration of large state spaces, symbolic search using binary +decision diagrams (BDDs) can save huge amounts of memory and computation time. +State sets are represented and modified by accessing and manipulating their +characteristic functions. BDD partitioning is used to compute the image as the +disjunction of smaller subimages. + In this paper, we propose a novel BDD partitioning option. The partitioning +is lexicographical in the binary representation of the states contained in the +set that is represented by a BDD and uniform with respect to the number of +states represented. The motivation of controlling the state set sizes in the +partitioning is to eventually bridge the gap between explicit and symbolic +search. + Let n be the size of the binary state vector. We propose an O(n) ranking and +unranking scheme that supports negated edges and operates on top of precomputed +satcount values. For the uniform split of a BDD, we then use unranking to +provide paths along which we partition the BDDs. In a shared BDD representation +the efforts are O(n). The algorithms are fully integrated in the CUDD library +and evaluated in strongly solving general game playing benchmarks. +",Lex-Partitioning: A New Option for BDD Search +" In this paper we present the results of unstructured data clustering in this +case a textual data from Reuters 21578 corpus with a new biomimetic approach +using immune system. Before experimenting our immune system, we digitalized +textual data by the n-grams approach. The novelty lies on hybridization of +n-grams and immune systems for clustering. The experimental results show that +the recommended ideas are promising and prove that this method can solve the +text clustering problem. +","A Biomimetic Approach Based on Immune Systems for Classification of + Unstructured Data" +" With the increased use of ontologies in semantically-enabled applications, +the issue of debugging defects in ontologies has become increasingly important. +These defects can lead to wrong or incomplete results for the applications. +Debugging consists of the phases of detection and repairing. In this paper we +focus on the repairing phase of a particular kind of defects, i.e. the missing +relations in the is-a hierarchy. Previous work has dealt with the case of +taxonomies. In this work we extend the scope to deal with ALC ontologies that +can be represented using acyclic terminologies. We present algorithms and +discuss a system. +","Get my pizza right: Repairing missing is-a relations in ALC ontologies + (extended version)" +" The Algorithm Selection Problem is concerned with selecting the best +algorithm to solve a given problem on a case-by-case basis. It has become +especially relevant in the last decade, as researchers are increasingly +investigating how to identify the most suitable existing algorithm for solving +a problem instead of developing new algorithms. This survey presents an +overview of this work focusing on the contributions made in the area of +combinatorial search problems, where Algorithm Selection techniques have +achieved significant performance improvements. We unify and organise the vast +literature according to criteria that determine Algorithm Selection systems in +practice. The comprehensive classification of approaches identifies and +analyses the different directions from which Algorithm Selection has been +approached. This paper contrasts and compares different methods for solving the +problem as well as ways of using these solutions. It closes by identifying +directions of current and future research. +",Algorithm Selection for Combinatorial Search Problems: A Survey +" Rough sets were proposed to deal with the vagueness and incompleteness of +knowledge in information systems. There are may optimization issues in this +field such as attribute reduction. Matroids generalized from matrices are +widely used in optimization. Therefore, it is necessary to connect matroids +with rough sets. In this paper, we take field into consideration and introduce +matrix to study rough sets through vector matroids. First, a matrix +representation of an equivalence relation is proposed, and then a matroidal +structure of rough sets over a field is presented by the matrix. Second, the +properties of the matroidal structure including circuits, bases and so on are +studied through two special matrix solution spaces, especially null space. +Third, over a binary field, we construct an equivalence relation from matrix +null space, and establish an algebra isomorphism from the collection of +equivalence relations to the collection of sets, which any member is a family +of the minimal non-empty sets that are supports of members of null space of a +binary dependence matrix. In a word, matrix provides a new viewpoint to study +rough sets. +",Matrix approach to rough sets through vector matroids over a field +" There are few knowledge representation (KR) techniques available for +efficiently representing knowledge. However, with the increase in complexity, +better methods are needed. Some researchers came up with hybrid mechanisms by +combining two or more methods. In an effort to construct an intelligent +computer system, a primary consideration is to represent large amounts of +knowledge in a way that allows effective use and efficiently organizing +information to facilitate making the recommended inferences. There are merits +and demerits of combinations, and standardized method of KR is needed. In this +paper, various hybrid schemes of KR were explored at length and details +presented. +",Hybrid Systems for Knowledge Representation in Artificial Intelligence +" We describe an inference task in which a set of timestamped event +observations must be clustered into an unknown number of temporal sequences +with independent and varying rates of observations. Various existing approaches +to multi-object tracking assume a fixed number of sources and/or a fixed +observation rate; we develop an approach to inferring structure in timestamped +data produced by a mixture of an unknown and varying number of similar Markov +renewal processes, plus independent clutter noise. The inference simultaneously +distinguishes signal from noise as well as clustering signal observations into +separate source streams. We illustrate the technique via a synthetic experiment +as well as an experiment to track a mixture of singing birds. +",Segregating event streams and noise with a Markov renewal process model +" Decision tree is an effective classification approach in data mining and +machine learning. In applications, test costs and misclassification costs +should be considered while inducing decision trees. Recently, some +cost-sensitive learning algorithms based on ID3 such as CS-ID3, IDX, +\lambda-ID3 have been proposed to deal with the issue. These algorithms deal +with only symbolic data. In this paper, we develop a decision tree algorithm +inspired by C4.5 for numeric data. There are two major issues for our +algorithm. First, we develop the test cost weighted information gain ratio as +the heuristic information. According to this heuristic information, our +algorithm is to pick the attribute that provides more gain ratio and costs less +for each selection. Second, we design a post-pruning strategy through +considering the tradeoff between test costs and misclassification costs of the +generated decision tree. In this way, the total cost is reduced. Experimental +results indicate that (1) our algorithm is stable and effective; (2) the +post-pruning technique reduces the total cost significantly; (3) the +competition strategy is effective to obtain a cost-sensitive decision tree with +low cost. +",Cost-sensitive C4.5 with post-pruning and competition +" Case Based Reasoning (CBR) is an intelligent way of thinking based on +experience and capitalization of already solved cases (source cases) to find a +solution to a new problem (target case). Retrieval phase consists on +identifying source cases that are similar to the target case. This phase may +lead to erroneous results if the existing knowledge imperfections are not taken +into account. This work presents a novel solution based on Fuzzy logic +techniques and adaptation measures which aggregate weighted similarities to +improve the retrieval results. To confirm the efficiency of our solution, we +have applied it to the industrial diagnosis domain. The obtained results are +more efficient results than those obtained by applying typical measures. +",A Logic and Adaptive Approach for Efficient Diagnosis Systems using CBR +" This paper advocates the exploration of the full state of recorded real-time +strategy (RTS) games, by human or robotic players, to discover how to reason +about tactics and strategy. We present a dataset of StarCraft games +encompassing the most of the games' state (not only player's orders). We +explain one of the possible usages of this dataset by clustering armies on +their compositions. This reduction of armies compositions to mixtures of +Gaussian allow for strategic reasoning at the level of the components. We +evaluated this clustering method by predicting the outcomes of battles based on +armies compositions' mixtures components +",A Dataset for StarCraft AI \& an Example of Armies Clustering +" Many cognitive systems deploy multiple, closed, individually consistent +models which can represent interpretations of the present state of the world, +moments in the past, possible futures or alternate versions of reality. While +they appear under different names, these structures can be grouped under the +general term of worlds. The Xapagy architecture is a story-oriented cognitive +system which relies exclusively on the autobiographical memory implemented as a +raw collection of events organized into world-type structures called {\em +scenes}. The system performs reasoning by shadowing current events with events +from the autobiography. The shadows are then extrapolated into headless shadows +corresponding to predictions, hidden events or inferred relations. +","Shadows and headless shadows: a worlds-based, autobiographical approach + to reasoning" +" This paper argues that the problem of identity is a critical challenge in +agents which are able to reason about stories. The Xapagy architecture has been +built from scratch to perform narrative reasoning and relies on a somewhat +unusual approach to represent instances and identity. We illustrate the +approach by a representation of the story of Little Red Riding Hood in the +architecture, with a focus on the problem of identity raised by the narrative. +",Modeling problems of identity in Little Red Riding Hood +" The Xapagy architecture is a story-oriented cognitive system which relies +exclusively on the autobiographical memory implemented as a raw collection of +events. Reasoning is performed by shadowing current events with events from the +autobiography. The shadows are then extrapolated into headless shadows (HLSs). +In a story following mood, HLSs can be used to track the level of surprise of +the agent, to infer hidden actions or relations between the participants, and +to summarize ongoing events. In recall mood, the HLSs can be used to create new +stories ranging from exact recall to free-form confabulation. +","Shadows and Headless Shadows: an Autobiographical Approach to Narrative + Reasoning" +" Computational thinking is a new problem soling method named for its extensive +use of computer science techniques. It synthesizes critical thinking and +existing knowledge and applies them in solving complex technological problems. +The term was coined by J. Wing, but the relationship between computational and +critical thinking, the two modes of thiking in solving problems, has not been +yet learly established. This paper aims at shedding some light into this +relationship. We also present two classroom experiments performed recently at +the Graduate Technological Educational Institute of Patras in Greece. The +results of these experiments give a strong indication that the use of computers +as a tool for problem solving enchances the students' abilities in solving real +world problems involving mathematical modelling. This is also crossed by +earlier findings of other researchers for the problem solving process in +general (not only for mathematical problems). +",Problem Solving and Computational Thinking in a Learning Environment +" Traffic regulation must be respected by all vehicles, either human- or +computer- driven. However, extreme traffic situations might exhibit practical +cases in which a vehicle should safely and reasonably relax traffic regulation, +e.g., in order not to be indefinitely blocked and to keep circulating. In this +paper, we propose a high-level representation of an automated vehicle, other +vehicles and their environment, which can assist drivers in taking such +""illegal"" but practical relaxation decisions. This high-level representation +(an ontology) includes topological knowledge and inference rules, in order to +compute the next high-level motion an automated vehicle should take, as +assistance to a driver. Results on practical cases are presented. +","An ontology-based approach to relax traffic regulation for autonomous + vehicle assistance" +" Soft Constraint Logic Programming is a natural and flexible declarative +programming formalism, which allows to model and solve real-life problems +involving constraints of different types. + In this paper, after providing a slightly more general and elegant +presentation of the framework, we show how we can apply it to the e-mobility +problem of coordinating electric vehicles in order to overcome both energetic +and temporal constraints and so to reduce their running cost. In particular, we +focus on the journey optimization sub-problem, considering sequences of trips +from a user's appointment to another one. Solutions provide the best +alternatives in terms of time and energy consumption, including route sequences +and possible charging events. +","Soft Constraint Logic Programming for Electric Vehicle Travel + Optimization" +" We look at the problem of revising fuzzy belief bases, i.e., belief base +revision in which both formulas in the base as well as revision-input formulas +can come attached with varying truth-degrees. Working within a very general +framework for fuzzy logic which is able to capture a variety of types of +inference under uncertainty, such as truth-functional fuzzy logics and certain +types of probabilistic inference, we show how the idea of rational change from +'crisp' base revision, as embodied by the idea of partial meet revision, can be +faithfully extended to revising fuzzy belief bases. We present and axiomatise +an operation of partial meet fuzzy revision and illustrate how the operation +works in several important special instances of the framework. +",On revising fuzzy belief bases +" WA qualitative probabilistic network models the probabilistic relationships +between its variables by means of signs. Non-monotonic influences have +associated an ambiguous sign. These ambiguous signs typically lead to +uninformative results upon inference. A non-monotonic influence can, however, +be associated with a, more informative, sign that indicates its effect in the +current state of the network. To capture this effect, we introduce the concept +of situational sign. Furthermore, if the network converts to a state in which +all variables that provoke the non-monotonicity have been observed, a +non-monotonic influence reduces to a monotonic influence. We study the +persistence and propagation of situational signs upon inference and give a +method to establish the sign of a reduced influence. +",Upgrading Ambiguous Signs in QPNs +" Dependability modeling and evaluation is aimed at investigating that a system +performs its function correctly in time. A usual way to achieve a high +reliability, is to design redundant systems that contain several replicas of +the same subsystem or component. State space methods for dependability analysis +may suffer of the state space explosion problem in such a kind of situation. +Combinatorial models, on the other hand, require the simplified assumption of +statistical independence; however, in case of redundant systems, this does not +guarantee a reduced number of modeled elements. In order to provide a more +compact system representation, parametric system modeling has been investigated +in the literature, in such a way that a set of replicas of a given subsystem is +parameterized so that only one representative instance is explicitly included. +While modeling aspects can be suitably addressed by these approaches, +analytical tools working on parametric characterizations are often more +difficult to be defined and the standard approach is to 'unfold' the parametric +model, in order to exploit standard analysis algorithms working at the unfolded +'ground' level. Moreover, parameterized combinatorial methods still require the +statistical independence assumption. In the present paper we consider the +formalism of Parametric Fault Tree (PFT) and we show how it can be related to +Probabilistic Horn Abduction (PHA). Since PHA is a framework where both +modeling and analysis can be performed in a restricted first-order language, we +aim at showing that converting a PFT into a PHA knowledge base will allow an +approach to dependability analysis directly exploiting parametric +representation. We will show that classical qualitative and quantitative +dependability measures can be characterized within PHA. Furthermore, additional +modeling aspects (such as noisy gates and local dependencies) as well as +additional reliability measures (such as posterior probability analysis) can be +naturally addressed by this conversion. A simple example of a multi-processor +system with several replicated units is used to illustrate the approach. +",Parametric Dependability Analysis through Probabilistic Horn Abduction +" This paper introduces new methodology to triangulate dynamic Bayesian +networks (DBNs) and dynamic graphical models (DGMs). While most methods to +triangulate such networks use some form of constrained elimination scheme based +on properties of the underlying directed graph, we find it useful to view +triangulation and elimination using properties only of the resulting undirected +graph, obtained after the moralization step. We first briefly introduce the +Graphical model toolkit (GMTK) and its notion of dynamic graphical models, one +that slightly extends the standard notion of a DBN. We next introduce the +'boundary algorithm', a method to find the best boundary between partitions in +a dynamic model. We find that using this algorithm, the notions of forward- and +backward-interface become moot - namely, the size and fill-in of the best +forward- and backward- interface are identical. Moreover, we observe that +finding a good partition boundary allows for constrained elimination orders +(and therefore graph triangulations) that are not possible using standard +slice-by-slice constrained eliminations. More interestingly, with certain +boundaries it is possible to obtain constrained elimination schemes that lie +outside the space of possible triangulations using only unconstrained +elimination. Lastly, we report triangulation results on invented graphs, +standard DBNs from the literature, novel DBNs used in speech recognition +research systems, and also random graphs. Using a number of different +triangulation quality measures (max clique size, state-space, etc.), we find +that with our boundary algorithm the triangulation quality can dramatically +improve. +",On Triangulating Dynamic Graphical Models +" The paper studies empirically the time-space trade-off between sampling and +inference in a sl cutset sampling algorithm. The algorithm samples over a +subset of nodes in a Bayesian network and applies exact inference over the +rest. Consequently, while the size of the sampling space decreases, requiring +less samples for convergence, the time for generating each single sample +increases. The w-cutset sampling selects a sampling set such that the +induced-width of the network when the sampling set is observed is bounded by w, +thus requiring inference whose complexity is exponential in w. In this paper, +we investigate performance of w-cutset sampling over a range of w values and +measure the accuracy of w-cutset sampling as a function of w. Our experiments +demonstrate that the cutset sampling idea is quite powerful showing that an +optimal balance between inference and sampling benefits substantially from +restricting the cutset size, even at the cost of more complex inference. +",An Empirical Study of w-Cutset Sampling for Bayesian Networks +" In a standard possibilistic logic, prioritized information are encoded by +means of weighted knowledge base. This paper proposes an extension of +possibilistic logic for dealing with partially ordered information. We Show +that all basic notions of standard possibilitic logic (sumbsumption, syntactic +and semantic inference, etc.) have natural couterparts when dealing with +partially ordered information. We also propose an algorithm which computes +possibilistic conclusions of a partial knowledge base of a partially ordered +knowlege base. +",A possibilistic handling of partially ordered information +" Backtracking search is a powerful algorithmic paradigm that can be used to +solve many problems. It is in a certain sense the dual of variable elimination; +but on many problems, e.g., SAT, it is vastly superior to variable elimination +in practice. Motivated by this we investigate the application of backtracking +search to the problem of Bayesian inference (Bayes). We show that natural +generalizations of known techniques allow backtracking search to achieve +performance guarantees similar to standard algorithms for Bayes, and that there +exist problems on which backtracking can in fact do much better. We also +demonstrate that these ideas can be applied to implement a Bayesian inference +engine whose performance is competitive with standard algorithms. Since +backtracking search can very naturally take advantage of context specific +structure, the potential exists for performance superior to standard algorithms +on many problems. +",Value Elimination: Bayesian Inference via Backtracking Search +" Recursive Conditioning (RC) was introduced recently as the first any-space +algorithm for inference in Bayesian networks which can trade time for space by +varying the size of its cache at the increment needed to store a floating point +number. Under full caching, RC has an asymptotic time and space complexity +which is comparable to mainstream algorithms based on variable elimination and +clustering (exponential in the network treewidth and linear in its size). We +show two main results about RC in this paper. First, we show that its actual +space requirements under full caching are much more modest than those needed by +mainstream methods and study the implications of this finding. Second, we show +that RC can effectively deal with determinism in Bayesian networks by employing +standard logical techniques, such as unit resolution, allowing a significant +reduction in its time requirements in certain cases. We illustrate our results +using a number of benchmark networks, including the very challenging ones that +arise in genetic linkage analysis. +",New Advances in Inference by Recursive Conditioning +" Most methods of exact probability propagation in Bayesian networks do not +carry out the inference directly over the network, but over a secondary +structure known as a junction tree or a join tree (JT). The process of +obtaining a JT is usually termed {sl compilation}. As compilation is usually +viewed as a whole process; each time the network is modified, a new compilation +process has to be carried out. The possibility of reusing an already existing +JT, in order to obtain the new one regarding only the modifications in the +network has received only little attention in the literature. In this paper we +present a method for incremental compilation of a Bayesian network, following +the classical scheme in which triangulation plays the key role. In order to +perform incremental compilation we propose to recompile only those parts of the +JT which can have been affected by the networks modifications. To do so, we +exploit the technique OF maximal prime subgraph decomposition in determining +the minimal subgraph(s) that have to be recompiled, and thereby the minimal +subtree(s) of the JT that should be replaced by new subtree(s).We focus on +structural modifications : addition and deletion of links and variables. +",Incremental Compilation of Bayesian networks +" This paper is directed towards combining Pearl's structural-model approach to +causal reasoning with high-level formalisms for reasoning about actions. More +precisely, we present a combination of Pearl's structural-model approach with +Poole's independent choice logic. We show how probabilistic theories in the +independent choice logic can be mapped to probabilistic causal models. This +mapping provides the independent choice logic with appealing concepts of +causality and explanation from the structural-model approach. We illustrate +this along Halpern and Pearl's sophisticated notions of actual cause, +explanation, and partial explanation. This mapping also adds first-order +modeling capabilities and explicit actions to the structural-model approach. +",Structure-Based Causes and Explanations in the Independent Choice Logic +" Symbolic representations have been used successfully in off-line planning +algorithms for Markov decision processes. We show that they can also improve +the performance of on-line planners. In addition to reducing computation time, +symbolic generalization can reduce the amount of costly real-world interactions +required for convergence. We introduce Symbolic Real-Time Dynamic Programming +(or sRTDP), an extension of RTDP. After each step of on-line interaction with +an environment, sRTDP uses symbolic model-checking techniques to generalizes +its experience by updating a group of states rather than a single state. We +examine two heuristic approaches to dynamic grouping of states and show that +they accelerate the planning process significantly in terms of both CPU time +and the number of steps of interaction with the environment. +",Symbolic Generalization for On-line Planning +" We present the language {m P}{cal C}+ for probabilistic reasoning about +actions, which is a generalization of the action language {cal C}+ that allows +to deal with probabilistic as well as nondeterministic effects of actions. We +define a formal semantics of {m P}{cal C}+ in terms of probabilistic +transitions between sets of states. Using a concept of a history and its belief +state, we then show how several important problems in reasoning about actions +can be concisely formulated in our formalism. +",Probabilistic Reasoning about Actions in Nonmonotonic Causal Theories +" In Non - ergodic belief networks the posterior belief OF many queries given +evidence may become zero.The paper shows that WHEN belief propagation IS +applied iteratively OVER arbitrary networks(the so called, iterative OR loopy +belief propagation(IBP)) it IS identical TO an arc - consistency algorithm +relative TO zero - belief queries(namely assessing zero posterior +probabilities). This implies that zero - belief conclusions derived BY belief +propagation converge AND are sound.More importantly it suggests that the +inference power OF IBP IS AS strong AND AS weak, AS that OF arc - +consistency.This allows the synthesis OF belief networks FOR which belief +propagation IS useless ON one hand, AND focuses the investigation OF classes OF +belief network FOR which belief propagation may be zero - complete.Finally, ALL +the above conclusions apply also TO Generalized belief propagation algorithms +that extend loopy belief propagation AND allow a crisper understanding OF their +power. +",A Simple Insight into Iterative Belief Propagation's Success +" Pearls concept OF a d - connecting path IS one OF the foundations OF the +modern theory OF graphical models : the absence OF a d - connecting path IN a +DAG indicates that conditional independence will hold IN ANY distribution +factorising according TO that graph. IN this paper we show that IN singly - +connected Gaussian DAGs it IS possible TO USE the form OF a d - connection TO +obtain qualitative information about the strength OF conditional +dependence.More precisely, the squared partial correlations BETWEEN two given +variables, conditioned ON different subsets may be partially ordered BY +examining the relationship BETWEEN the d - connecting path AND the SET OF +variables conditioned upon. +","Using the structure of d-connecting paths as a qualitative measure of + the strength of dependence" +" In this paper, we introduce a method for approximating the solution to +inference and optimization tasks in uncertain and deterministic reasoning. Such +tasks are in general intractable for exact algorithms because of the large +number of dependency relationships in their structure. Our method effectively +maps such a dense problem to a sparser one which is in some sense ""closest"". +Exact methods can be run on the sparser problem to derive bounds on the +original answer, which can be quite sharp. We present empirical results +demonstrating that our method works well on the tasks of belief inference and +finding the probability of the most probable explanation in belief networks, +and finding the cost of the solution that violates the smallest number of +constraints in constraint satisfaction problems. On one large CPCS network, for +example, we were able to calculate upper and lower bounds on the conditional +probability of a variable, given evidence, that were almost identical in the +average case. +","Approximate Decomposition: A Method for Bounding and Estimating + Probabilistic and Deterministic Queries" +" Real-world distributed systems and networks are often unreliable and subject +to random failures of its components. Such a stochastic behavior affects +adversely the complexity of optimization tasks performed routinely upon such +systems, in particular, various resource allocation tasks. In this work we +investigate and develop Monte Carlo solutions for a class of two-stage +optimization problems in stochastic networks in which the expected value of +resource allocations before and after stochastic failures needs to be +optimized. The limitation of these problems is that their exact solutions are +exponential in the number of unreliable network components: thus, exact methods +do not scale-up well to large networks often seen in practice. We first prove +that Monte Carlo optimization methods can overcome the exponential bottleneck +of exact methods. Next we support our theoretical findings on resource +allocation experiments and show a very good scale-up potential of the new +methods to large stochastic networks. +","Monte-Carlo optimizations for resource allocation problems in stochastic + network systems" +" This paper examines a number of solution methods for decision processes with +non-Markovian rewards (NMRDPs). They all exploit a temporal logic specification +of the reward function to automatically translate the NMRDP into an equivalent +Markov decision process (MDP) amenable to well-known MDP solution methods. They +differ however in the representation of the target MDP and the class of MDP +solution methods to which they are suited. As a result, they adopt different +temporal logics and different translations. Unfortunately, no implementation of +these methods nor experimental let alone comparative results have ever been +reported. This paper is the first step towards filling this gap. We describe an +integrated system for solving NMRDPs which implements these methods and several +variants under a common interface; we use it to compare the various approaches +and identify the problem features favoring one over the other. +","Implementation and Comparison of Solution Methods for Decision Processes + with Non-Markovian Rewards" +" This paper studies decision making for Walley's partially consonant belief +functions (pcb). In a pcb, the set of foci are partitioned. Within each +partition, the foci are nested. The pcb class includes probability functions +and possibility functions as extreme cases. Unlike earlier proposals for a +decision theory with belief functions, we employ an axiomatic approach. We +adopt an axiom system similar in spirit to von Neumann - Morgenstern's linear +utility theory for a preference relation on pcb lotteries. We prove a +representation theorem for this relation. Utility for a pcb lottery is a +combination of linear utility for probabilistic lottery and binary utility for +possibilistic lottery. +",Decision Making with Partially Consonant Belief Functions +" The two most popular types of graphical model are directed models (Bayesian +networks) and undirected models (Markov random fields, or MRFs). Directed and +undirected models offer complementary properties in model construction, +expressing conditional independencies, expressing arbitrary factorizations of +joint distributions, and formulating message-passing inference algorithms. We +show that the strengths of these two representations can be combined in a +single type of graphical model called a 'factor graph'. Every Bayesian network +or MRF can be easily converted to a factor graph that expresses the same +conditional independencies, expresses the same factorization of the joint +distribution, and can be used for probabilistic inference through application +of a single, simple message-passing algorithm. In contrast to chain graphs, +where message-passing is implemented on a hypergraph, message-passing can be +directly implemented on the factor graph. We describe a modified 'Bayes-ball' +algorithm for establishing conditional independence in factor graphs, and we +show that factor graphs form a strict superset of Bayesian networks and MRFs. +In particular, we give an example of a commonly-used 'mixture of experts' model +fragment, whose independencies cannot be represented in a Bayesian network or +an MRF, but can be represented in a factor graph. We finish by giving examples +of real-world problems that are not well suited to representation in Bayesian +networks and MRFs, but are well-suited to representation in factor graphs. +","Extending Factor Graphs so as to Unify Directed and Undirected Graphical + Models" +" This paper is devoted to the search of robust solutions in state space graphs +when costs depend on scenarios. We first present axiomatic requirements for +preference compatibility with the intuitive idea of robustness.This leads us to +propose the Lorenz dominance rule as a basis for robustness analysis. Then, +after presenting complexity results about the determination of robust +solutions, we propose a new sophistication of A* specially designed to +determine the set of robust paths in a state space graph. The behavior of the +algorithm is illustrated on a small example. Finally, an axiomatic +justification of the refinement of robustness by an OWA criterion is provided. +","An Axiomatic Approach to Robustness in Search Problems with Multiple + Scenarios" +" MAP is the problem of finding a most probable instantiation of a set of +variables in a Bayesian network given some evidence. Unlike computing posterior +probabilities, or MPE (a special case of MAP), the time and space complexity of +structural solutions for MAP are not only exponential in the network treewidth, +but in a larger parameter known as the ""constrained"" treewidth. In practice, +this means that computing MAP can be orders of magnitude more expensive than +computing posterior probabilities or MPE. This paper introduces a new, simple +upper bound on the probability of a MAP solution, which admits a tradeoff +between the bound quality and the time needed to compute it. The bound is shown +to be generally much tighter than those of other methods of comparable +complexity. We use this proposed upper bound to develop a branch-and-bound +search algorithm for solving MAP exactly. Experimental results demonstrate that +the search algorithm is able to solve many problems that are far beyond the +reach of any structure-based method for MAP. For example, we show that the +proposed algorithm can compute MAP exactly and efficiently for some networks +whose constrained treewidth is more than 40. +",Solving MAP Exactly using Systematic Search +" The aim of this research is to develop a reasoning under uncertainty strategy +in the context of the Fuzzy Inductive Reasoning (FIR) methodology. FIR emerged +from the General Systems Problem Solving developed by G. Klir. It is a data +driven methodology based on systems behavior rather than on structural +knowledge. It is a very useful tool for both the modeling and the prediction of +those systems for which no previous structural knowledge is available. FIR +reasoning is based on pattern rules synthesized from the available data. The +size of the pattern rule base can be very large making the prediction process +quite difficult. In order to reduce the size of the pattern rule base, it is +possible to automatically extract classical Sugeno fuzzy rules starting from +the set of pattern rules. The Sugeno rule base preserves pattern rules +knowledge as much as possible. In this process some information is lost but +robustness is considerably increased. In the forecasting process either the +pattern rule base or the Sugeno fuzzy rule base can be used. The first option +is desirable when the computational resources make it possible to deal with the +overall pattern rule base or when the extracted fuzzy rules are not accurate +enough due to uncertainty associated to the original data. In the second +option, the prediction process is done by means of the classical Sugeno +inference system. If the amount of uncertainty associated to the data is small, +the predictions obtained using the Sugeno fuzzy rule base will be very +accurate. In this paper a mixed pattern/fuzzy rules strategy is proposed to +deal with uncertainty in such a way that the best of both perspectives is used. +Areas in the data space with a higher level of uncertainty are identified by +means of the so-called error models. The prediction process in these areas +makes use of a mixed pattern/fuzzy rules scheme, whereas areas identified with +a lower level of uncertainty only use the Sugeno fuzzy rule base. The proposed +strategy is applied to a real biomedical system, i.e., the central nervous +system control of the cardiovascular system. +",Dealing with uncertainty in fuzzy inductive reasoning methodology +" For a given problem, the optimal Markov policy can be considerred as a +conditional or contingent plan containing a (potentially large) number of +branches. Unfortunately, there are applications where it is desirable to +strictly limit the number of decision points and branches in a plan. For +example, it may be that plans must later undergo more detailed simulation to +verify correctness and safety, or that they must be simple enough to be +understood and analyzed by humans. As a result, it may be necessary to limit +consideration to plans with only a small number of branches. This raises the +question of how one goes about finding optimal plans containing only a limited +number of branches. In this paper, we present an any-time algorithm for optimal +k-contingency planning (OKP). It is the first optimal algorithm for limited +contingency planning that is not an explicit enumeration of possible contingent +plans. By modelling the problem as a Partially Observable Markov Decision +Process, it implements the Bellman optimality principle and prunes the solution +space. We present experimental results of applying this algorithm to some +simple test cases. +",Optimal Limited Contingency Planning +" The paper continues the study of partitioning based inference of heuristics +for search in the context of solving the Most Probable Explanation task in +Bayesian Networks. We compare two systematic Branch and Bound search +algorithms, BBBT (for which the heuristic information is constructed during +search and allows dynamic variable/value ordering) and its predecessor BBMB +(for which the heuristic information is pre-compiled), against a number of +popular local search algorithms for the MPE problem. We show empirically that, +when viewed as approximation schemes, BBBT/BBMB are superior to all of these +best known SLS algorithms, especially when the domain sizes increase beyond 2. +This is in contrast with the performance of SLS vs. systematic search on +CSP/SAT problems, where SLS often significantly outperforms systematic +algorithms. As far as we know, BBBT/BBMB are currently the best performing +algorithms for solving the MPE task. +",Systematic vs. Non-systematic Algorithms for Solving the MPE Task +" Precision achieved by stochastic sampling algorithms for Bayesian networks +typically deteriorates in face of extremely unlikely evidence. To address this +problem, we propose the Evidence Pre-propagation Importance Sampling algorithm +(EPIS-BN), an importance sampling algorithm that computes an approximate +importance function by the heuristic methods: loopy belief Propagation and +e-cutoff. We tested the performance of e-cutoff on three large real Bayesian +networks: ANDES, CPCS, and PATHFINDER. We observed that on each of these +networks the EPIS-BN algorithm gives us a considerable improvement over the +current state of the art algorithm, the AIS-BN algorithm. In addition, it +avoids the costly learning stage of the AIS-BN algorithm. +",An Importance Sampling Algorithm Based on Evidence Pre-propagation +" In this paper we examine the problem of inference in Bayesian Networks with +discrete random variables that have very large or even unbounded domains. For +example, in a domain where we are trying to identify a person, we may have +variables that have as domains, the set of all names, the set of all postal +codes, or the set of all credit card numbers. We cannot just have big tables of +the conditional probabilities, but need compact representations. We provide an +inference algorithm, based on variable elimination, for belief networks +containing both large domain and normal discrete random variables. We use +intensional (i.e., in terms of procedures) and extensional (in terms of listing +the elements) representations of conditional probabilities and of the +intermediate factors. +",Efficient Inference in Large Discrete Domains +" We present CLP(BN), a novel approach that aims at expressing Bayesian +networks through the constraint logic programming framework. Arguably, an +important limitation of traditional Bayesian networks is that they are +propositional, and thus cannot represent relations between multiple similar +objects in multiple contexts. Several researchers have thus proposed +first-order languages to describe such networks. Namely, one very successful +example of this approach are the Probabilistic Relational Models (PRMs), that +combine Bayesian networks with relational database technology. The key +difficulty that we had to address when designing CLP(cal{BN}) is that logic +based representations use ground terms to denote objects. With probabilitic +data, we need to be able to uniquely represent an object whose value we are not +sure about. We use {sl Skolem functions} as unique new symbols that uniquely +represent objects with unknown value. The semantics of CLP(cal{BN}) programs +then naturally follow from the general framework of constraint logic +programming, as applied to a specific domain where we have probabilistic data. +This paper introduces and defines CLP(cal{BN}), and it describes an +implementation and initial experiments. The paper also shows how CLP(cal{BN}) +relates to Probabilistic Relational Models (PRMs), Ngo and Haddawys +Probabilistic Logic Programs, AND Kersting AND De Raedts Bayesian Logic +Programs. +",CLP(BN): Constraint Logic Programming for Probabilistic Knowledge +" We use princiles of fuzzy logic to develop a general model representing +several processes in a system's operation characterized by a degree of +vagueness and/or uncertainy. Further, we introduce three altenative measures of +a fuzzy system's effectiveness connected to the above model. An applcation is +also developed for the Mathematical Modelling process illustrating our results. +",A Study on Fuzzy Systems +" In their nature configuration problems are combinatorial (optimization) +problems. In order to find a configuration a solver has to instantiate a number +of components of a some type and each of these components can be used in a +relation defined for a type. Therefore, many solutions of a configuration +problem have symmetric ones which can be obtained by replacing some component +of a solution by another one of the same type. These symmetric solutions +decrease performance of optimization algorithms because of two reasons: a) they +satisfy all requirements and cannot be pruned out from the search space; and b) +existence of symmetric optimal solutions does not allow to prove the optimum in +a feasible time. +",Study: Symmetry breaking for ASP +" Results are presented on the performance of Adaptive Neuro-Fuzzy Inference +system (ANFIS) for wind velocity forecasts in the Isthmus of Tehuantepec region +in the state of Oaxaca, Mexico. The data bank was provided by the +meteorological station located at the University of Isthmus, Tehuantepec +campus, and this data bank covers the period from 2008 to 2011. Three data +models were constructed to carry out 16, 24 and 48 hours forecasts using the +following variables: wind velocity, temperature, barometric pressure, and date. +The performance measure for the three models is the mean standard error (MSE). +In this work, performance analysis in short-term prediction is presented, +because it is essential in order to define an adequate wind speed model for +eolian parks, where a right planning provide economic benefits. +",Performance Analysis of ANFIS in short term Wind Speed Prediction +" ConArg is a Constraint Programming-based tool that can be used to model and +solve different problems related to Abstract Argumentation Frameworks (AFs). To +implement this tool we have used JaCoP, a Java library that provides the user +with a Finite Domain Constraint Programming paradigm. ConArg is able to +randomly generate networks with small-world properties in order to find +conflict-free, admissible, complete, stable grounded, preferred, semi-stable, +stage and ideal extensions on such interaction graphs. We present the main +features of ConArg and we report the performance in time, showing also a +comparison with ASPARTIX [1], a similar tool using Answer Set Programming. The +use of techniques for constraint solving can tackle the complexity of the +problems presented in [2]. Moreover we suggest semiring-based soft constraints +as a mean to parametrically represent and solve Weighted Argumentation +Frameworks: different kinds of preference levels related to attacks, e.g., a +score representing a ""fuzziness"", a ""cost"" or a probability, can be represented +by choosing different instantiation of the semiring algebraic structure. The +basic idea is to provide a common computational and quantitative framework. +","ConArg: a Tool to Solve (Weighted) Abstract Argumentation Frameworks + with (Soft) Constraints" +" The Semantic Web ontology language OWL 2 DL comes with a variety of language +features that enable sophisticated and practically useful modeling. However, +the use of these features has been severely restricted in order to retain +decidability of the language. For example, OWL 2 DL does not allow a property +to be both transitive and asymmetric, which would be desirable, e.g., for +representing an ancestor relation. In this paper, we argue that the so-called +global restrictions of OWL 2 DL preclude many useful forms of modeling, by +providing a catalog of basic modeling patterns that would be available in OWL 2 +DL if the global restrictions were discarded. We then report on the results of +evaluating several state-of-the-art OWL 2 DL reasoners on problems that use +combinations of features in a way that the global restrictions are violated. +The systems turn out to rely heavily on the global restrictions and are thus +largely incapable of coping with the modeling patterns. Next we show how +off-the-shelf first-order logic theorem proving technology can be used to +perform reasoning in the OWL 2 direct semantics, the semantics that underlies +OWL 2 DL, but without requiring the global restrictions. Applying a naive +proof-of-concept implementation of this approach to the test problems was +successful in all cases. Based on our observations, we make suggestions for +future lines of research on expressive description logic-style OWL reasoning. +",Modeling in OWL 2 without Restrictions +" All standard AI planners to-date can only handle a single objective, and the +only way for them to take into account multiple objectives is by aggregation of +the objectives. Furthermore, and in deep contrast with the single objective +case, there exists no benchmark problems on which to test the algorithms for +multi-objective planning. Divide and Evolve (DAE) is an evolutionary planner +that won the (single-objective) deterministic temporal satisficing track in the +last International Planning Competition. Even though it uses intensively the +classical (and hence single-objective) planner YAHSP, it is possible to turn +DAE-YAHSP into a multi-objective evolutionary planner. A tunable benchmark +suite for multi-objective planning is first proposed, and the performances of +several variants of multi-objective DAE-YAHSP are compared on different +instances of this benchmark, hopefully paving the road to further +multi-objective competitions in AI planning. +",Multi-Objective AI Planning: Evaluating DAE-YAHSP on a Tunable Benchmark +" We investigate the concept of symmetry and its role in problem solving. This +paper first defines precisely the elements that constitute a ""problem"" and its +""solution,"" and gives several examples to illustrate these definitions. Given +precise definitions of problems, it is relatively straightforward to construct +a search process for finding solutions. Finally this paper attempts to exploit +the concept of symmetry in improving problem solving. +",Improving problem solving by exploiting the concept of symmetry +" We have a lot of relation to the encoding and the Theory of Information, when +considering thinking. This is a natural process and, at once, the complex thing +we investigate. This always was a challenge - to understand how our mind works, +and we are trying to find some universal models for this. A lot of ways have +been considered so far, but we are looking for Something, we seek for +approaches. And the goal is to find a consistent, noncontradictory view, which +should at once be enough flexible in any dimensions to allow to represent +various kinds of processes and environments, matters of different nature and +diverse objects. Developing of such a model is the destination of this article. +","Irrespective Priority-Based Regular Properties of High-Intensity Virtual + Environments" +" Neuroevolution has yet to scale up to complex reinforcement learning tasks +that require large networks. Networks with many inputs (e.g. raw video) imply a +very high dimensional search space if encoded directly. Indirect methods use a +more compact genotype representation that is transformed into networks of +potentially arbitrary size. In this paper, we present an indirect method where +networks are encoded by a set of Fourier coefficients which are transformed +into network weight matrices via an inverse Fourier-type transform. Because +there often exist network solutions whose weight matrices contain regularity +(i.e. adjacent weights are correlated), the number of coefficients required to +represent these networks in the frequency domain is much smaller than the +number of weights (in the same way that natural images can be compressed by +ignore high-frequency components). This ""compressed"" encoding is compared to +the direct approach where search is conducted in the weight space on the +high-dimensional octopus arm task. The results show that representing networks +in the frequency domain can reduce the search-space dimensionality by as much +as two orders of magnitude, both accelerating convergence and yielding more +general solutions. +",A Frequency-Domain Encoding for Neuroevolution +" We propose AD3, a new algorithm for approximate maximum a posteriori (MAP) +inference on factor graphs based on the alternating directions method of +multipliers. Like dual decomposition algorithms, AD3 uses worker nodes to +iteratively solve local subproblems and a controller node to combine these +local solutions into a global update. The key characteristic of AD3 is that +each local subproblem has a quadratic regularizer, leading to a faster +consensus than subgradient-based dual decomposition, both theoretically and in +practice. We provide closed-form solutions for these AD3 subproblems for binary +pairwise factors and factors imposing first-order logic constraints. For +arbitrary factors (large or combinatorial), we introduce an active set method +which requires only an oracle for computing a local MAP configuration, making +AD3 applicable to a wide range of problems. Experiments on synthetic and +realworld problems show that AD3 compares favorably with the state-of-the-art. +",Alternating Directions Dual Decomposition +" Travel sharing, i.e., the problem of finding parts of routes which can be +shared by several travellers with different points of departure and +destinations, is a complex multiagent problem that requires taking into account +individual agents' preferences to come up with mutually acceptable joint plans. +In this paper, we apply state-of-the-art planning techniques to real-world +public transportation data to evaluate the feasibility of multiagent planning +techniques in this domain. The potential application value of improving travel +sharing technology has great application value due to its ability to reduce the +environmental impact of travelling while providing benefits to travellers at +the same time. We propose a three-phase algorithm that utilises performant +single-agent planners to find individual plans in a simplified domain first, +then merges them using a best-response planner which ensures resulting +solutions are individually rational, and then maps the resulting plan onto the +full temporal planning domain to schedule actual journeys. The evaluation of +our algorithm on real-world, multi-modal public transportation data for the +United Kingdom shows linear scalability both in the scenario size and in the +number of agents, where trade-offs have to be made between total cost +improvement, the percentage of feasible timetables identified for journeys, and +the prolongation of these journeys. Our system constitutes the first +implementation of strategic multiagent planning algorithms in large-scale +domains and provides insights into the engineering process of translating +general domain-independent multiagent planning algorithms to real-world +applications. +","Applying Strategic Multiagent Planning to Real-World Travel Sharing + Problems" +" Robust optimization is one of the fundamental approaches to deal with +uncertainty in combinatorial optimization. This paper considers the robust +spanning tree problem with interval data, which arises in a variety of +telecommunication applications. It proposes a constraint satisfaction approach +using a combinatorial lower bound, a pruning component that removes infeasible +and suboptimal edges, as well as a search strategy exploring the most uncertain +edges first. The resulting algorithm is shown to produce very dramatic +improvements over the mathematical programming approach of Yaman et al. and to +enlarge considerably the class of problems amenable to effective solutions +","A constraint satisfaction approach to the robust spanning tree problem + with interval data" +" The problem of learning Markov equivalence classes of Bayesian network +structures may be solved by searching for the maximum of a scoring metric in a +space of these classes. This paper deals with the definition and analysis of +one such search space. We use a theoretically motivated neighbourhood, the +inclusion boundary, and represent equivalence classes by essential graphs. We +show that this search space is connected and that the score of the neighbours +can be evaluated incrementally. We devise a practical way of building this +neighbourhood for an essential graph that is purely graphical and does not +explicitely refer to the underlying independences. We find that its size can be +intractable, depending on the complexity of the essential graph of the +equivalence class. The emphasis is put on the potential use of this space with +greedy hill -climbing search +","On the Construction of the Inclusion Boundary Neighbourhood for Markov + Equivalence Classes of Bayesian Network Structures" +" Recently, it has been emphasized that the possibility theory framework allows +us to distinguish between i) what is possible because it is not ruled out by +the available knowledge, and ii) what is possible for sure. This distinction +may be useful when representing knowledge, for modelling values which are not +impossible because they are consistent with the available knowledge on the one +hand, and values guaranteed to be possible because reported from observations +on the other hand. It is also of interest when expressing preferences, to point +out values which are positively desired among those which are not rejected. +This distinction can be encoded by two types of constraints expressed in terms +of necessity measures and in terms of guaranteed possibility functions, which +induce a pair of possibility distributions at the semantic level. A consistency +condition should ensure that what is claimed to be guaranteed as possible is +indeed not impossible. The present paper investigates the representation of +this bipolar view, including the case when it is stated by means of conditional +measures, or by means of comparative context-dependent constraints. The +interest of this bipolar framework, which has been recently stressed for +expressing preferences, is also pointed out in the representation of diagnostic +knowledge. +",Bipolar Possibilistic Representations +" We develop a qualitative theory of Markov Decision Processes (MDPs) and +Partially Observable MDPs that can be used to model sequential decision making +tasks when only qualitative information is available. Our approach is based +upon an order-of-magnitude approximation of both probabilities and utilities, +similar to epsilon-semantics. The result is a qualitative theory that has close +ties with the standard maximum-expected-utility theory and is amenable to +general planning techniques. +",Qualitative MDPs and POMDPs: An Order-Of-Magnitude Approximation +" The ability to make decisions and to assess potential courses of action is a +corner-stone of many AI applications, and usually this requires explicit +information about the decision-maker s preferences. IN many applications, +preference elicitation IS a serious bottleneck.The USER either does NOT have +the time, the knowledge, OR the expert support required TO specify complex +multi - attribute utility functions. IN such cases, a method that IS based ON +intuitive, yet expressive, preference statements IS required. IN this paper we +suggest the USE OF TCP - nets, an enhancement OF CP - nets, AS a tool FOR +representing, AND reasoning about qualitative preference statements.We present +AND motivate this framework, define its semantics, AND show how it can be used +TO perform constrained optimization. +",Introducing Variable Importance Tradeoffs into CP-Nets +" We outline a class of problems, typical of Mars rover operations, that are +problematic for current methods of planning under uncertainty. The existing +methods fail because they suffer from one or more of the following limitations: +1) they rely on very simple models of actions and time, 2) they assume that +uncertainty is manifested in discrete action outcomes, 3) they are only +practical for very small problems. For many real world problems, these +assumptions fail to hold. In particular, when planning the activities for a +Mars rover, none of the above assumptions is valid: 1) actions can be +concurrent and have differing durations, 2) there is uncertainty concerning +action durations and consumption of continuous resources like power, and 3) +typical daily plans involve on the order of a hundred actions. This class of +problems may be of particular interest to the UAI community because both +classical and decision-theoretic planning techniques may be useful in solving +it. We describe the rover problem, discuss previous work on planning under +uncertainty, and present a detailed, but very small, example illustrating some +of the difficulties of finding good plans. +","Planning under Continuous Time and Resource Uncertainty: A Challenge for + AI" +" This paper concerns the assessment of direct causal effects from a +combination of: (i) non-experimental data, and (ii) qualitative domain +knowledge. Domain knowledge is encoded in the form of a directed acyclic graph +(DAG), in which all interactions are assumed linear, and some variables are +presumed to be unobserved. We provide a generalization of the well-known method +of Instrumental Variables, which allows its application to models with few +conditional independeces. +",Generalized Instrumental Variables +" In this paper, we derive optimality results for greedy Bayesian-network +search algorithms that perform single-edge modifications at each step and use +asymptotically consistent scoring criteria. Our results extend those of Meek +(1997) and Chickering (2002), who demonstrate that in the limit of large +datasets, if the generative distribution is perfect with respect to a DAG +defined over the observable variables, such search algorithms will identify +this optimal (i.e. generative) DAG model. We relax their assumption about the +generative distribution, and assume only that this distribution satisfies the +{em composition property} over the observable variables, which is a more +realistic assumption for real domains. Under this assumption, we guarantee that +the search algorithms identify an {em inclusion-optimal} model; that is, a +model that (1) contains the generative distribution and (2) has no sub-model +that contains this distribution. In addition, we show that the composition +property is guaranteed to hold whenever the dependence relationships in the +generative distribution can be characterized by paths between singleton +elements in some generative graphical model (e.g. a DAG, a chain graph, or a +Markov network) even when the generative model includes unobserved variables, +and even when the observed data is subject to selection bias. +",Finding Optimal Bayesian Networks +" The paper presents an iterative version of join-tree clustering that applies +the message passing of join-tree clustering algorithm to join-graphs rather +than to join-trees, iteratively. It is inspired by the success of Pearl's +belief propagation algorithm as an iterative approximation scheme on one hand, +and by a recently introduced mini-clustering i. success as an anytime +approximation method, on the other. The proposed Iterative Join-graph +Propagation IJGP belongs to the class of generalized belief propagation +methods, recently proposed using analogy with algorithms in statistical +physics. Empirical evaluation of this approach on a number of problem classes +demonstrates that even the most time-efficient variant is almost always +superior to IBP and MC i, and is sometimes more accurate by as much as several +orders of magnitude. +",Iterative Join-Graph Propagation +" In this paper, we continue our research on the algorithmic aspects of Halpern +and Pearl's causes and explanations in the structural-model approach. To this +end, we present new characterizations of weak causes for certain classes of +causal models, which show that under suitable restrictions deciding causes and +explanations is tractable. To our knowledge, these are the first explicit +tractability results for the structural-model approach. +","Causes and Explanations in the Structural-Model Approach: Tractable + Cases" +" We formulate necessary and sufficient conditions for an arbitrary discrete +probability distribution to factor according to an undirected graphical model, +or a log-linear model, or other more general exponential models. This result +generalizes the well known Hammersley-Clifford Theorem. +",Factorization of Discrete Probability Distributions +" This paper presents a decision-theoretic approach to statistical inference +that satisfies the likelihood principle (LP) without using prior information. +Unlike the Bayesian approach, which also satisfies LP, we do not assume +knowledge of the prior distribution of the unknown parameter. With respect to +information that can be obtained from an experiment, our solution is more +efficient than Wald's minimax solution.However, with respect to information +assumed to be known before the experiment, our solution demands less input than +the Bayesian solution. +","Statistical Decisions Using Likelihood Information Without Prior + Probabilities" +" We present a principled and efficient planning algorithm for collaborative +multiagent dynamical systems. All computation, during both the planning and the +execution phases, is distributed among the agents; each agent only needs to +model and plan for a small part of the system. Each of these local subsystems +is small, but once they are combined they can represent an exponentially larger +problem. The subsystems are connected through a subsystem hierarchy. +Coordination and communication between the agents is not imposed, but derived +directly from the structure of this hierarchy. A globally consistent plan is +achieved by a message passing algorithm, where messages correspond to natural +local reward functions and are computed by local linear programs; another +message passing algorithm allows us to execute the resulting policy. When two +portions of the hierarchy share the same structure, our algorithm can reuse +plans and messages to speed up computation. +",Distributed Planning in Hierarchical Factored MDPs +" We describe expectation propagation for approximate inference in dynamic +Bayesian networks as a natural extension of Pearl s exact belief +propagation.Expectation propagation IS a greedy algorithm, converges IN many +practical cases, but NOT always.We derive a DOUBLE - loop algorithm, guaranteed +TO converge TO a local minimum OF a Bethe free energy.Furthermore, we show that +stable fixed points OF (damped) expectation propagation correspond TO local +minima OF this free energy, but that the converse need NOT be the CASE .We +illustrate the algorithms BY applying them TO switching linear dynamical +systems AND discuss implications FOR approximate inference IN general Bayesian +networks. +","Expectation Propogation for approximate inference in dynamic Bayesian + networks" +" We extend the language of influence diagrams to cope with decision scenarios +where the order of decisions and observations is not determined. As the +ordering of decisions is dependent on the evidence, a step-strategy of such a +scenario is a sequence of dependent choices of the next action. A strategy is a +step-strategy together with selection functions for decision actions. The +structure of a step-strategy can be represented as a DAG with nodes labeled +with action variables. We introduce the concept of GS-DAG: a DAG incorporating +an optimal step-strategy for any instantiation. We give a method for +constructing GS-DAGs, and we show how to use a GS-DAG for determining an +optimal strategy. Finally we discuss how analysis of relevant past can be used +to reduce the size of the GS-DAG. +",Unconstrained Influence Diagrams +" We introduce a new Bayesian network (BN) scoring metric called the Global +Uniform (GU) metric. This metric is based on a particular type of default +parameter prior. Such priors may be useful when a BN developer is not willing +or able to specify domain-specific parameter priors. The GU parameter prior +specifies that every prior joint probability distribution P consistent with a +BN structure S is considered to be equally likely. Distribution P is consistent +with S if P includes just the set of independence relations defined by S. We +show that the GU metric addresses some undesirable behavior of the BDeu and K2 +Bayesian network scoring metrics, which also use particular forms of default +parameter priors. A closed form formula for computing GU for special classes of +BNs is derived. Efficiently computing GU for an arbitrary BN remains an open +problem. +","A Bayesian Network Scoring Metric That Is Based On Globally Uniform + Parameter Priors" +" This paper investigates value function approximation in the context of +zero-sum Markov games, which can be viewed as a generalization of the Markov +decision process (MDP) framework to the two-agent case. We generalize error +bounds from MDPs to Markov games and describe generalizations of reinforcement +learning algorithms to Markov games. We present a generalization of the optimal +stopping problem to a two-player simultaneous move Markov game. For this +special problem, we provide stronger bounds and can guarantee convergence for +LSTD and temporal difference learning with linear value function approximation. +We demonstrate the viability of value function approximation for Markov games +by using the Least squares policy iteration (LSPI) algorithm to learn good +policies for a soccer domain and a flow control problem. +",Value Function Approximation in Zero-Sum Markov Games +" The Reverse Water Gas Shift system (RWGS) is a complex physical system +designed to produce oxygen from the carbon dioxide atmosphere on Mars. If sent +to Mars, it would operate without human supervision, thus requiring a reliable +automated system for monitoring and control. The RWGS presents many challenges +typical of real-world systems, including: noisy and biased sensors, nonlinear +behavior, effects that are manifested over different time granularities, and +unobservability of many important quantities. In this paper we model the RWGS +using a hybrid (discrete/continuous) Dynamic Bayesian Network (DBN), where the +state at each time slice contains 33 discrete and 184 continuous variables. We +show how the system state can be tracked using probabilistic inference over the +model. We discuss how to deal with the various challenges presented by the +RWGS, providing a suite of techniques that are likely to be useful in a wide +range of applications. In particular, we describe a general framework for +dealing with nonlinear behavior using numerical integration techniques, +extending the successful Unscented Filter. We also show how to use a +fixed-point computation to deal with effects that develop at different time +scales, specifically rapid changes occurring during slowly changing processes. +We test our model using real data collected from the RWGS, demonstrating the +feasibility of hybrid DBNs for monitoring complex real-world physical systems. +",Monitoring a Complez Physical System using a Hybrid Dynamic Bayes Net +" We propose a formal treatment of scenarios in the context of a dialectical +argumentation formalism for qualitative reasoning about uncertain propositions. +Our formalism extends prior work in which arguments for and against uncertain +propositions were presented and compared in interaction spaces called Agoras. +We now define the notion of a scenario in this framework and use it to define a +set of qualitative uncertainty labels for propositions across a collection of +scenarios. This work is intended to lead to a formal theory of scenarios and +scenario analysis. +",Formalizing Scenario Analysis +" This paper is about searching the combinatorial space of contingency tables +during the inner loop of a nonlinear statistical optimization. Examples of this +operation in various data analytic communities include searching for nonlinear +combinations of attributes that contribute significantly to a regression +(Statistics), searching for items to include in a decision list (machine +learning) and association rule hunting (Data Mining). + This paper investigates a new, efficient approach to this class of problems, +called RADSEARCH (Real-valued All-Dimensions-tree Search). RADSEARCH finds the +global optimum, and this gives us the opportunity to empirically evaluate the +question: apart from algorithmic elegance what does this attention to +optimality buy us? + We compare RADSEARCH with other recent successful search algorithms such as +CN2, PRIM, APriori, OPUS and DenseMiner. Finally, we introduce RADREG, a new +regression algorithm for learning real-valued outputs based on RADSEARCHing for +high-order interactions. +","Real-valued All-Dimensions search: Low-overhead rapid searching over + subsets of attributes" +" Exact monitoring in dynamic Bayesian networks is intractable, so approximate +algorithms are necessary. This paper presents a new family of approximate +monitoring algorithms that combine the best qualities of the particle filtering +and Boyen-Koller methods. Our algorithms maintain an approximate representation +the belief state in the form of sets of factored particles, that correspond to +samples of clusters of state variables. Empirical results show that our +algorithms outperform both ordinary particle filtering and the Boyen-Koller +algorithm on large systems. +",Factored Particles for Scalable Monitoring +" In this paper we present a language for finite state continuous time Bayesian +networks (CTBNs), which describe structured stochastic processes that evolve +over continuous time. The state of the system is decomposed into a set of local +variables whose values change over time. The dynamics of the system are +described by specifying the behavior of each local variable as a function of +its parents in a directed (possibly cyclic) graph. The model specifies, at any +given point in time, the distribution over two aspects: when a local variable +changes its value and the next value it takes. These distributions are +determined by the variable s CURRENT value AND the CURRENT VALUES OF its +parents IN the graph.More formally, each variable IS modelled AS a finite state +continuous time Markov process whose transition intensities are functions OF +its parents.We present a probabilistic semantics FOR the language IN terms OF +the generative model a CTBN defines OVER sequences OF events.We list types OF +queries one might ask OF a CTBN, discuss the conceptual AND computational +difficulties associated WITH exact inference, AND provide an algorithm FOR +approximate inference which takes advantage OF the structure within the +process. +",Continuous Time Bayesian Networks +" MAP is the problem of finding a most probable instantiation of a set of +nvariables in a Bayesian network, given some evidence. MAP appears to be a +significantly harder problem than the related problems of computing the +probability of evidence Pr, or MPE a special case of MAP. Because of the +complexity of MAP, and the lack of viable algorithms to approximate it,MAP +computations are generally avoided by practitioners. This paper investigates +the complexity of MAP. We show that MAP is complete for NP. We also provide +negative complexity results for elimination based algorithms. It turns out that +MAP remains hard even when MPE, and Pr are easy. We show that MAP is NPcomplete +when the networks are restricted to polytrees, and even then can not be +effectively approximated. Because there is no approximation algorithm with +guaranteed results, we investigate best effort approximations. We introduce a +generic MAP approximation framework. As one instantiation of it, we implement +local search coupled with belief propagation BP to approximate MAP. We show how +to extract approximate evidence retraction information from belief propagation +which allows us to perform efficient local search. This allows MAP +approximation even on networks that are too complex to even exactly solve the +easier problems of computing Pr or MPE. Experimental results indicate that +using BP and local search provides accurate MAP estimates in many cases. +",MAP Complexity Results and Approximation Methods +" Quantification is well known to be a major obstacle in the construction of a +probabilistic network, especially when relying on human experts for this +purpose. The construction of a qualitative probabilistic network has been +proposed as an initial step in a network s quantification, since the +qualitative network can be used TO gain preliminary insight IN the projected +networks reasoning behaviour. We extend on this idea and present a new type of +network in which both signs and numbers are specified; we further present an +associated algorithm for probabilistic inference. Building upon these +semi-qualitative networks, a probabilistic network can be quantified and +studied in a stepwise manner. As a result, modelling inadequacies can be +detected and amended at an early stage in the quantification process. +",From Qualitative to Quantitative Probabilistic Networks +" We present new algorithms for inference in credal networks --- directed +acyclic graphs associated with sets of probabilities. Credal networks are here +interpreted as encoding strong independence relations among variables. We first +present a theory of credal networks based on separately specified sets of +probabilities. We also show that inference with polytrees is NP-hard in this +setting. We then introduce new techniques that reduce the computational effort +demanded by inference, particularly in polytrees, by exploring separability of +credal sets. +","Inference with Seperately Specified Sets of Probabilities in Credal + Networks" +" An increasing number of applications require real-time reasoning under +uncertainty with streaming input. The temporal (dynamic) Bayes net formalism +provides a powerful representational framework for such applications. However, +existing exact inference algorithms for dynamic Bayes nets do not scale to the +size of models required for real world applications which often contain +hundreds or even thousands of variables for each time slice. In addition, +existing algorithms were not developed with real-time processing in mind. We +have developed a new computational approach to support real-time exact +inference in large temporal Bayes nets. Our approach tackles scalability by +recognizing that the complexity of the inference depends on the number of +interface nodes between time slices and by exploiting the distinction between +static and dynamic nodes in order to reduce the number of interface nodes and +to factorize their joint probability distribution. We approach the real-time +issue by organizing temporal Bayes nets into static representations, and then +using the symbolic probabilistic inference algorithm to derive analytic +expressions for the static representations. The parts of these expressions that +do not change at each time step are pre-computed. The remaining parts are +compiled into efficient procedural code so that the memory and CPU resources +required by the inference are small and fixed. +",Real-Time Inference with Large-Scale Temporal Bayes Nets +" We address the question of convergence in the loopy belief propagation (LBP) +algorithm. Specifically, we relate convergence of LBP to the existence of a +weak limit for a sequence of Gibbs measures defined on the LBP s associated +computation tree.Using tools FROM the theory OF Gibbs measures we develop +easily testable sufficient conditions FOR convergence.The failure OF +convergence OF LBP implies the existence OF multiple phases FOR the associated +Gibbs specification.These results give new insight INTO the mechanics OF the +algorithm. +",Loopy Belief Propogation and Gibbs Measures +" A popular approach to solving a decision process with non-Markovian rewards +(NMRDP) is to exploit a compact representation of the reward function to +automatically translate the NMRDP into an equivalent Markov decision process +(MDP) amenable to our favorite MDP solution method. The contribution of this +paper is a representation of non-Markovian reward functions and a translation +into MDP aimed at making the best possible use of state-based anytime +algorithms as the solution method. By explicitly constructing and exploring +only parts of the state space, these algorithms are able to trade computation +time for policy quality, and have proven quite effective in dealing with large +MDPs. Our representation extends future linear temporal logic (FLTL) to express +rewards. Our translation has the effect of embedding model-checking in the +solution method. It results in an MDP of the minimal size achievable without +stepping outside the anytime framework, and consequently in better policies by +the deadline. +","Anytime State-Based Solution Methods for Decision Processes with + non-Markovian Rewards" +" The validity OF a causal model can be tested ONLY IF the model imposes +constraints ON the probability distribution that governs the generated data. IN +the presence OF unmeasured variables, causal models may impose two types OF +constraints : conditional independencies, AS READ through the d - separation +criterion, AND functional constraints, FOR which no general criterion IS +available.This paper offers a systematic way OF identifying functional +constraints AND, thus, facilitates the task OF testing causal models AS well AS +inferring such models FROM data. +",On the Testable Implications of Causal Models with Hidden Variables +" We propose an efficient method for Bayesian network inference in models with +functional dependence. We generalize the multiplicative factorization method +originally designed by Takikawa and D Ambrosio(1999) FOR models WITH +independence OF causal influence.Using a hidden variable, we transform a +probability potential INTO a product OF two - dimensional potentials.The +multiplicative factorization yields more efficient inference. FOR example, IN +junction tree propagation it helps TO avoid large cliques. IN ORDER TO keep +potentials small, the number OF states OF the hidden variable should be +minimized.We transform this problem INTO a combinatorial problem OF minimal +base IN a particular space.We present an example OF a computerized adaptive +test, IN which the factorization method IS significantly more efficient than +previous inference methods. +",Exploiting Functional Dependence in Bayesian Network Inference +" This paper uses decision-theoretic principles to obtain new insights into the +assessment and updating of probabilities. First, a new foundation of +Bayesianism is given. It does not require infinite atomless uncertainties as +did Savage s classical result, AND can therefore be applied TO ANY finite +Bayesian network.It neither requires linear utility AS did de Finetti s +classical result, AND r ntherefore allows FOR the empirically AND normatively +desirable risk r naversion.Finally, BY identifying AND fixing utility IN an +elementary r nmanner, our result can readily be applied TO identify methods OF +r nprobability updating.Thus, a decision - theoretic foundation IS given r nto +the computationally efficient method OF inductive reasoning r ndeveloped BY +Rudolf Carnap.Finally, recent empirical findings ON r nprobability assessments +are discussed.It leads TO suggestions FOR r ncorrecting biases IN probability +assessments, AND FOR an alternative r nto the Dempster - Shafer belief +functions that avoids the reduction TO r ndegeneracy after multiple updatings.r +n +","Decision Principles to justify Carnap's Updating Method and to Suggest + Corrections of Probability Judgments (Invited Talks)" +" We select policies for large Markov Decision Processes (MDPs) with compact +first-order representations. We find policies that generalize well as the +number of objects in the domain grows, potentially without bound. Existing +dynamic-programming approaches based on flat, propositional, or first-order +representations either are impractical here or do not naturally scale as the +number of objects grows without bound. We implement and evaluate an alternative +approach that induces first-order policies using training data constructed by +solving small problem instances using PGraphplan (Blum & Langford, 1999). Our +policies are represented as ensembles of decision lists, using a taxonomic +concept language. This approach extends the work of Martin and Geffner (2000) +to stochastic domains, ensemble learning, and a wider variety of problems. +Empirically, we find ""good"" policies for several stochastic first-order MDPs +that are beyond the scope of previous approaches. We also discuss the +application of this work to the relational reinforcement-learning problem. +",Inductive Policy Selection for First-Order MDPs +" NP-SPEC is a language for specifying problems in NP in a declarative way. +Despite the fact that the semantics of the language was given by referring to +Datalog with circumscription, which is very close to ASP, so far the only +existing implementations are by means of ECLiPSe Prolog and via Boolean +satisfiability solvers. In this paper, we present translations from NP-SPEC +into various forms of ASP and analyze them. We also argue that it might be +useful to incorporate certain language constructs of NP-SPEC into mainstream +ASP. +",Translating NP-SPEC into ASP +" In this paper we continue the work on our extension of Answer Set Programming +by non-Herbrand functions and add to the language support for arithmetic +expressions and various inequality relations over non-Herbrand functions, as +well as consistency-restoring rules from CR-Prolog. We demonstrate the use of +this latest version of the language in the representation of important kinds of +knowledge. +","Language ASP{f} with Arithmetic Expressions and Consistency-Restoring + Rules" +" Within the area of computational models of argumentation, the +instantiation-based approach is gaining more and more attention, not at least +because meaningful input for Dung's abstract frameworks is provided in that +way. In a nutshell, the aim of instantiation-based argumentation is to form, +from a given knowledge base, a set of arguments and to identify the conflicts +between them. The resulting network is then evaluated by means of +extension-based semantics on an abstract level, i.e. on the resulting graph. +While several systems are nowadays available for the latter step, the +automation of the instantiation process itself has received less attention. In +this work, we provide a novel approach to construct and visualize an +argumentation framework from a given knowledge base. The system we propose +relies on Answer-Set Programming and follows a two-step approach. A first +program yields the logic-based arguments as its answer-sets; a second program +is then used to specify the relations between arguments based on the +answer-sets of the first program. As it turns out, this approach not only +allows for a flexible and extensible tool for instantiation-based +argumentation, but also provides a new method for answer-set visualization in +general. +",Utilizing ASP for Generating and Visualizing Argumentation Frameworks +" In this paper we present an Action Language-Answer Set Programming based +approach to solving planning and scheduling problems in hybrid domains - +domains that exhibit both discrete and continuous behavior. We use action +language H to represent the domain and then translate the resulting theory into +an A-Prolog program. In this way, we reduce the problem of finding solutions to +planning and scheduling problems to computing answer sets of A-Prolog programs. +We cite a planning and scheduling example from the literature and show how to +model it in H. We show how to translate the resulting H theory into an +equivalent A-Prolog program. We compute the answer sets of the resulting +program using a hybrid solver called EZCSP which loosely integrates a +constraint solver with an answer set solver. The solver allows us reason about +constraints over reals and compute solutions to complex planning and scheduling +problems. Results have shown that our approach can be applied to any planning +and scheduling problem in hybrid domains. +",Planning and Scheduling in Hybrid Domains Using Answer Set Programming +" The advance of Internet and Sensor technology has brought about new +challenges evoked by the emergence of continuous data streams. Beyond rapid +data processing, application areas like ambient assisted living, robotics, or +dynamic scheduling involve complex reasoning tasks. We address such scenarios +and elaborate upon approaches to knowledge-intense stream reasoning, based on +Answer Set Programming (ASP). While traditional ASP methods are devised for +singular problem solving, we develop new techniques to formulate and process +problems dealing with emerging as well as expiring data in a seamless way. +",Answer Set Programming for Stream Reasoning +" DL-Lite is an important family of description logics. Recently, there is an +increasing interest in handling inconsistency in DL-Lite as the constraint +imposed by a TBox can be easily violated by assertions in ABox in DL-Lite. In +this paper, we present a distance-based paraconsistent semantics based on the +notion of feature in DL-Lite, which provides a novel way to rationally draw +meaningful conclusions even from an inconsistent knowledge base. Finally, we +investigate several important logical properties of this entailment relation +based on the new semantics and show its promising advantages in non-monotonic +reasoning for DL-Lite. +",A Distance-based Paraconsistent Semantics for DL-Lite +" This paper presents a novel approach based on variable forgetting, which is a +useful tool in resolving contradictory by filtering some given variables, to +merging multiple knowledge bases. This paper first builds a relationship +between belief merging and variable forgetting by using dilation. Variable +forgetting is applied to capture belief merging operation. Finally, some new +merging operators are developed by modifying candidate variables to amend the +shortage of traditional merging operators. Different from model selection of +traditional merging operators, as an alternative approach, variable selection +in those new operators could provide intuitive information about an atom +variable among whole knowledge bases. +",A Forgetting-based Approach to Merging Knowledge Bases +" In an open, constantly changing and collaborative environment like the +forthcoming Semantic Web, it is reasonable to expect that knowledge sources +will contain noise and inaccuracies. It is well known, as the logical +foundation of the Semantic Web, description logic is lack of the ability of +tolerating inconsistent or incomplete data. Recently, the ability of +paraconsistent approaches in Semantic Web is weaker in this paper, we present a +tableau algorithm based on sign transformation in Semantic Web which holds the +stronger ability of reasoning. We prove that the tableau algorithm is decidable +which hold the same function of classical tableau algorithm for consistent +knowledge bases. +","A Paraconsistent Tableau Algorithm Based on Sign Transformation in + Semantic Web" +" This volume contains the papers presented at the fifth workshop on Answer Set +Programming and Other Computing Paradigms (ASPOCP 2012) held on September 4th, +2012 in Budapest, co-located with the 28th International Conference on Logic +Programming (ICLP 2012). It thus continues a series of previous events +co-located with ICLP, aiming at facilitating the discussion about crossing the +boundaries of current ASP techniques in theory, solving, and applications, in +combination with or inspired by other computing paradigms. +","Proceedings of Answer Set Programming and Other Computing Paradigms + (ASPOCP 2012), 5th International Workshop, September 4, 2012, Budapest, + Hungary" +" We present a general framework for defining priors on model structure and +sampling from the posterior using the Metropolis-Hastings algorithm. The key +idea is that structure priors are defined via a probability tree and that the +proposal mechanism for the Metropolis-Hastings algorithm operates by traversing +this tree, thereby defining a cheaply computable acceptance probability. We +have applied this approach to Bayesian net structure learning using a number of +priors and tree traversal strategies. Our results show that these must be +chosen appropriately for this approach to be successful. +",Markov Chain Monte Carlo using Tree-Based Priors on Model Structure +" Possibility theory offers either a qualitive, or a numerical framework for +representing uncertainty, in terms of dual measures of possibility and +necessity. This leads to the existence of two kinds of possibilistic causal +graphs where the conditioning is either based on the minimum, or the product +operator. Benferhat et al. (1999) have investigated the connections between +min-based graphs and possibilistic logic bases (made of classical formulas +weighted in terms of certainty). This paper deals with a more difficult issue : +the product-based graphical representations of possibilistic bases, which +provides an easy structural reading of possibilistic bases. Moreover, this +paper also provides another reading of possibilistic bases in terms of +comparative preferences of the form ""in the context p, q is preferred to not +q"". This enables us to explicit preferences underlying a set of goals with +different levels of priority. +",Graphical readings of possibilistic logic bases +" This paper presents a sound and completecalculus for causal relevance, based +onPearl's functional models semantics.The calculus consists of axioms and +rulesof inference for reasoning about causalrelevance relationships.We extend +the set of known axioms for causalrelevance with three new axioms, andintroduce +two new rules of inference forreasoning about specific subclasses +ofmodels.These subclasses give a more refinedcharacterization of causal models +than the one given in Halpern's axiomatizationof counterfactual +reasoning.Finally, we show how the calculus for causalrelevance can be used in +the task ofidentifying causal structure from non-observational data. +",A Calculus for Causal Relevance +" We propose a new directed graphical representation of utility functions, +called UCP-networks, that combines aspects of two existing graphical models: +generalized additive models and CP-networks. The network decomposes a utility +function into a number of additive factors, with the directionality of the arcs +reflecting conditional dependence of preference statements - in the underlying +(qualitative) preference ordering - under a {em ceteris paribus} (all else +being equal) interpretation. This representation is arguably natural in many +settings. Furthermore, the strong CP-semantics ensures that computation of +optimization and dominance queries is very efficient. We also demonstrate the +value of this representation in decision making. Finally, we describe an +interactive elicitation procedure that takes advantage of the linear nature of +the constraints on ""`tradeoff weights"" imposed by a UCP-network. This procedure +allows the network to be refined until the regret of the decision with minimax +regret (with respect to the incompletely specified utility function) falls +below a specified threshold (e.g., the cost of further questioning. +","UCP-Networks: A Directed Graphical Representation of Conditional + Utilities" +" We present two sampling algorithms for probabilistic confidence inference in +Bayesian networks. These two algorithms (we call them AIS-BN-mu and +AIS-BN-sigma algorithms) guarantee that estimates of posterior probabilities +are with a given probability within a desired precision bound. Our algorithms +are based on recent advances in sampling algorithms for (1) estimating the mean +of bounded random variables and (2) adaptive importance sampling in Bayesian +networks. In addition to a simple stopping rule for sampling that they provide, +the AIS-BN-mu and AIS-BN-sigma algorithms are capable of guiding the learning +process in the AIS-BN algorithm. An empirical evaluation of the proposed +algorithms shows excellent performance, even for very unlikely evidence. +",Confidence Inference in Bayesian Networks +" It is ""well known"" that in linear models: (1) testable constraints on the +marginal distribution of observed variables distinguish certain cases in which +an unobserved cause jointly influences several observed variables; (2) the +technique of ""instrumental variables"" sometimes permits an estimation of the +influence of one variable on another even when the association between the +variables may be confounded by unobserved common causes; (3) the association +(or conditional probability distribution of one variable given another) of two +variables connected by a path or trek can be computed directly from the +parameter values associated with each edge in the path or trek; (4) the +association of two variables produced by multiple treks can be computed from +the parameters associated with each trek; and (5) the independence of two +variables conditional on a third implies the corresponding independence of the +sums of the variables over all units conditional on the sums over all units of +each of the original conditioning variables.These properties are exploited in +search procedures. It is also known that properties (2)-(5) do not hold for all +Bayes nets with binary variables. We show that (1) holds for all Bayes nets +with binary variables and (5) holds for all singly trek-connected Bayes nets of +that kind. We further show that all five properties hold for Bayes nets with +any DAG and binary variables parameterized with noisy-or and noisy-and gates. +",Linearity Properties of Bayes Nets with Binary Variables +" This paper explores algorithms for processing probabilistic and deterministic +information when the former is represented as a belief network and the latter +as a set of boolean clauses. The motivating tasks are 1. evaluating beliefs +networks having a large number of deterministic relationships and2. evaluating +probabilities of complex boolean querie over a belief network. We propose a +parameterized family of variable elimination algorithms that exploit both types +of information, and that allows varying levels of constraint propagation +inferences. The complexity of the scheme is controlled by the induced-width of +the graph {em augmented} by the dependencies introduced by the boolean +constraints. Preliminary empirical evaluation demonstrate the effect of +constraint propagation on probabilistic computation. +",Hybrid Processing of Beliefs and Constraints +" In this paper we analyze two recent axiomatic approaches proposed by Dubois +et al and by Giang and Shenoy to qualitative decision making where uncertainty +is described by possibility theory. Both axiomtizations are inspired by von +Neumann and Morgenstern's system of axioms for the case of probability theory. +We show that our approach naturally unifies two axiomatic systems that +correspond respectively to pessimistic and optimistic decision criteria +proposed by Dubois et al. The simplifying unification is achieved by (i) +replacing axioms that are supposed to reflect two informational attitudes +(uncertainty aversion and uncertainty attraction) by an axiom that imposes +order on set of standard lotteries and (ii) using a binary utility scale in +which each utility level is represented by a pair of numbers. +","A Comparison of Axiomatic Approaches to Qualitative Decision Making + Using Possibility Theory" +" Graphical Markov models determined by acyclic digraphs (ADGs), also called +directed acyclic graphs (DAGs), are widely studied in statistics, computer +science (as Bayesian networks), operations research (as influence diagrams), +and many related fields. Because different ADGs may determine the same Markov +equivalence class, it long has been of interest to determine the efficiency +gained in model specification and search by working directly with Markov +equivalence classes of ADGs rather than with ADGs themselves. A computer +program was written to enumerate the equivalence classes of ADG models as +specified by Pearl & Verma's equivalence criterion. The program counted +equivalence classes for models up to and including 10 vertices. The ratio of +number of classes to ADGs appears to approach an asymptote of about 0.267. +Classes were analyzed according to number of edges and class size. By edges, +the distribution of number of classes approaches a Gaussian shape. By class +size, classes of size 1 are most common, with the proportions for larger sizes +initially decreasing but then following a more irregular pattern. The maximum +number of classes generated by any undirected graph was found to increase +approximately factorially. The program also includes a new variation of orderly +algorithm for generating undirected graphs. +",Enumerating Markov Equivalence Classes of Acyclic Digraph Models +" In previous work cite{Ha98:Towards} we presented a case-based approach to +eliciting and reasoning with preferences. A key issue in this approach is the +definition of similarity between user preferences. We introduced the +probabilistic distance as a measure of similarity on user preferences, and +provided an algorithm to compute the distance between two partially specified +{em value} functions. This is for the case of decision making under {em +certainty}. In this paper we address the more challenging issue of computing +the probabilistic distance in the case of decision making under{em +uncertainty}. We provide an algorithm to compute the probabilistic distance +between two partially specified {em utility} functions. We demonstrate the use +of this algorithm with a medical data set of partially specified patient +preferences,where none of the other existing distancemeasures appear definable. +Using this data set, we also demonstrate that the case-based approach to +preference elicitation isapplicable in domains with uncertainty. Finally, we +provide a comprehensive analytical comparison of the probabilistic distance +with some existing distance measures on preferences. +","Similarity Measures on Preference Structures, Part II: Utility Functions" +" We propose a new definition of actual causes, using structural equations to +model counterfactuals.We show that the definitions yield a plausible and +elegant account ofcausation that handles well examples which have caused +problems forother definitions and resolves major difficulties in the +traditionalaccount. In a companion paper, we show how the definition of +causality can beused to give an elegant definition of (causal) explanation. +",Causes and Explanations: A Structural-Model Approach --- Part 1: Causes +" We are developing a general framework for using learned Bayesian models for +decision-theoretic control of search and reasoningalgorithms. We illustrate the +approach on the specific task of controlling both general and domain-specific +solvers on a hard class of structured constraint satisfaction problems. A +successful strategyfor reducing the high (and even infinite) variance in +running time typically exhibited by backtracking search algorithms is to cut +off and restart the search if a solution is not found within a certainamount of +time. Previous work on restart strategies have employed fixed cut off values. +We show how to create a dynamic cut off strategy by learning a Bayesian model +that predicts the ultimate length of a trial based on observing the early +behavior of the search algorithm. Furthermore, we describe the general +conditions under which a dynamic restart strategy can outperform the +theoretically optimal fixed strategy. +",A Bayesian Approach to Tackling Hard Computational Problems +" Every directed acyclic graph (DAG) over a finite non-empty set of variables +(= nodes) N induces an independence model over N, which is a list of +conditional independence statements over N.The inclusion problem is how to +characterize (in graphical terms) whether all independence statements in the +model induced by a DAG K are in the model induced by a second DAG L. Meek +(1997) conjectured that this inclusion holds iff there exists a sequence of +DAGs from L to K such that only certain 'legal' arrow reversal and 'legal' +arrow adding operations are performed to get the next DAG in the sequence.In +this paper we give several characterizations of inclusion of DAG models and +verify Meek's conjecture in the case that the DAGs K and L differ in at most +one adjacency. As a warming up a rigorous proof of well-known graphical +characterizations of equivalence of DAGs, which is a highly related problem, is +given. +",On characterizing Inclusion of Bayesian Networks +" This article deals with plausible reasoning from incomplete knowledge about +large-scale spatial properties. The availableinformation, consisting of a set +of pointwise observations,is extrapolated to neighbour points. We make use of +belief functions to represent the influence of the knowledge at a given point +to another point; the quantitative strength of this influence decreases when +the distance between both points increases. These influences arethen aggregated +using a variant of Dempster's rule of combination which takes into account the +relative dependence between observations. +",Plausible reasoning from spatial observations +" This paper considers the problem of knowledge-based model construction in the +presence of uncertainty about the association of domain entities to random +variables. Multi-entity Bayesian networks (MEBNs) are defined as a +representation for knowledge in domains characterized by uncertainty in the +number of relevant entities, their interrelationships, and their association +with observables. An MEBN implicitly specifies a probability distribution in +terms of a hierarchically structured collection of Bayesian network fragments +that together encode a joint probability distribution over arbitrarily many +interrelated hypotheses. Although a finite query-complete model can always be +constructed, association uncertainty typically makes exact model construction +and evaluation intractable. The objective of hypothesis management is to +balance tractability against accuracy. We describe an application to the +problem of using intelligence reports to infer the organization and activities +of groups of military vehicles. Our approach is compared to related work in the +tracking and fusion literature. +",Hypothesis Management in Situation-Specific Network Construction +" An important subclass of hybrid Bayesian networks are those that represent +Conditional Linear Gaussian (CLG) distributions --- a distribution with a +multivariate Gaussian component for each instantiation of the discrete +variables. In this paper we explore the problem of inference in CLGs. We show +that inference in CLGs can be significantly harder than inference in Bayes +Nets. In particular, we prove that even if the CLG is restricted to an +extremely simple structure of a polytree in which every continuous node has at +most one discrete ancestor, the inference task is NP-hard.To deal with the +often prohibitive computational cost of the exact inference algorithm for CLGs, +we explore several approximate inference algorithms. These algorithms try to +find a small subset of Gaussians which are a good approximation to the full +mixture distribution. We consider two Monte Carlo approaches and a novel +approach that enumerates mixture components in order of prior probability. We +compare these methods on a variety of problems and show that our novel +algorithm is very promising for large, hybrid diagnosis problems. +","Inference in Hybrid Networks: Theoretical Limits and Practical + Algorithms" +" Many real life domains contain a mixture of discrete and continuous variables +and can be modeled as hybrid Bayesian Networks. Animportant subclass of hybrid +BNs are conditional linear Gaussian (CLG) networks, where the conditional +distribution of the continuous variables given an assignment to the discrete +variables is a multivariate Gaussian. Lauritzen's extension to the clique tree +algorithm can be used for exact inference in CLG networks. However, many +domains also include discrete variables that depend on continuous ones, and CLG +networks do not allow such dependencies to berepresented. No exact inference +algorithm has been proposed for these enhanced CLG networks. In this paper, we +generalize Lauritzen's algorithm, providing the first ""exact"" inference +algorithm for augmented CLG networks - networks where continuous nodes are +conditional linear Gaussians but that also allow discrete children ofcontinuous +parents. Our algorithm is exact in the sense that it computes the exact +distributions over the discrete nodes, and the exact first and second moments +of the continuous ones, up to the accuracy obtained by numerical integration +used within thealgorithm. When the discrete children are modeled with softmax +CPDs (as is the case in many real world domains) the approximation of the +continuous distributions using the first two moments is particularly accurate. +Our algorithm is simple to implement and often comparable in its complexity to +Lauritzen's algorithm. We show empirically that it achieves substantially +higher accuracy than previous approximate algorithms. +",Exact Inference in Networks with Discrete Children of Continuous Parents +" We present probabilistic logic programming under inheritance with overriding. +This approach is based on new notions of entailment for reasoning with +conditional constraints, which are obtained from the classical notion of +logical entailment by adding the principle of inheritance with overriding. This +is done by using recent approaches to probabilistic default reasoning with +conditional constraints. We analyze the semantic properties of the new +entailment relations. We also present algorithms for probabilistic logic +programming under inheritance with overriding, and program transformations for +an increased efficiency. +",Probabilistic Logic Programming under Inheritance with Overriding +" In this paper we compare three different architectures for the evaluation of +influence diagrams: HUGIN, Shafer-Shenoy, and Lazy Evaluation architecture. The +computational complexity of the architectures are compared on the LImited +Memory Influence Diagram (LIMID): a diagram where only the requiste information +for the computation of the optimal policies are depicted. Because the requsite +information is explicitly represented in the LIMID the evaluation can take +advantage of it, and significant savings in computational can be obtained. In +this paper we show how the obtained savings is considerably increased when the +computations performed on the LIMID is according to the Lazy Evaluation scheme. +","Solving Influence Diagrams using HUGIN, Shafer-Shenoy and Lazy + Propagation" +" We consider the task of aggregating beliefs of severalexperts. We assume that +these beliefs are represented as probabilitydistributions. We argue that the +evaluation of any aggregationtechnique depends on the semantic context of this +task. We propose aframework, in which we assume that nature generates samples +from a`true' distribution and different experts form their beliefs based onthe +subsets of the data they have a chance to observe. Naturally, theideal +aggregate distribution would be the one learned from thecombined sample sets. +Such a formulation leads to a natural way tomeasure the accuracy of the +aggregation mechanism.We show that the well-known aggregation operator LinOP is +ideallysuited for that task. We propose a LinOP-based learning +algorithm,inspired by the techniques developed for Bayesian learning, +whichaggregates the experts' distributions represented as Bayesiannetworks. Our +preliminary experiments show that this algorithmperforms well in practice. +",Aggregating Learned Probabilistic Beliefs +" We propose using recognition networks for approximate inference inBayesian +networks (BNs). A recognition network is a multilayerperception (MLP) trained +to predict posterior marginals given observedevidence in a particular BN. The +input to the MLP is a vector of thestates of the evidential nodes. The activity +of an output unit isinterpreted as a prediction of the posterior marginal of +thecorresponding variable. The MLP is trained using samples generated fromthe +corresponding BN.We evaluate a recognition network that was trained to do +inference ina large Bayesian network, similar in structure and complexity to +theQuick Medical Reference, Decision Theoretic (QMR-DT). Our networkis a +binary, two-layer, noisy-OR network containing over 4000 potentially observable +nodes and over 600 unobservable, hidden nodes. Inreal medical diagnosis, most +observables are unavailable, and there isa complex and unknown bias that +selects which ones are provided. Weincorporate a very basic type of selection +bias in our network: a knownpreference that available observables are positive +rather than negative.Even this simple bias has a significant effect on the +posterior. We compare the performance of our recognition network +tostate-of-the-art approximate inference algorithms on a large set oftest +cases. In order to evaluate the effect of our simplistic modelof the selection +bias, we evaluate algorithms using a variety ofincorrectly modeled observation +biases. Recognition networks performwell using both correct and incorrect +observation biases. +",Recognition Networks for Approximate Inference in BN20 Networks +" The Factored Frontier (FF) algorithm is a simple approximate +inferencealgorithm for Dynamic Bayesian Networks (DBNs). It is very similar +tothe fully factorized version of the Boyen-Koller (BK) algorithm, butinstead +of doing an exact update at every step followed bymarginalisation (projection), +it always works with factoreddistributions. Hence it can be applied to models +for which the exactupdate step is intractable. We show that FF is equivalent to +(oneiteration of) loopy belief propagation (LBP) on the original DBN, andthat +BK is equivalent (to one iteration of) LBP on a DBN where wecluster some of the +nodes. We then show empirically that byiterating, LBP can improve on the +accuracy of both FF and BK. Wecompare these algorithms on two real-world DBNs: +the first is a modelof a water treatment plant, and the second is a coupled +HMM, used tomodel freeway traffic. +",The Factored Frontier Algorithm for Approximate Inference in DBNs +" Most successful Bayesian network (BN) applications to datehave been built +through knowledge elicitation from experts.This is difficult and time +consuming, which has lead to recentinterest in automated methods for learning +BNs from data. We present a case study in the construction of a BN in +anintelligent tutoring application, specifically decimal misconceptions. +Wedescribe the BN construction using expert elicitation and then investigate +how certainexisting automated knowledge discovery methods might support the BN +knowledge engineering process. +","A Case Study in Knowledge Discovery and Elicitation in an Intelligent + Tutoring Application" +" MAP is the problem of finding a most probable instantiation of a set of +variables in a Bayesian network, given evidence. Unlike computing marginals, +posteriors, and MPE (a special case of MAP), the time and space complexity of +MAP is not only exponential in the network treewidth, but also in a larger +parameter known as the ""constrained"" treewidth. In practice, this means that +computing MAP can be orders of magnitude more expensive than +computingposteriors or MPE. Thus, practitioners generally avoid MAP +computations, resorting instead to approximating them by the most likely value +for each MAP variableseparately, or by MPE.We present a method for +approximating MAP using local search. This method has space complexity which is +exponential onlyin the treewidth, as is the complexity of each search step. We +investigate the effectiveness of different local searchmethods and several +initialization strategies and compare them to otherapproximation +schemes.Experimental results show that local search provides a much more +accurate approximation of MAP, while requiring few search steps.Practically, +this means that the complexity of local search is often exponential only in +treewidth as opposed to the constrained treewidth, making approximating MAP as +efficient as other computations. +",Approximating MAP using Local Search +" Suppose we are given the conditional probability of one variable given some +other variables.Normally the full joint distribution over the conditioning +variablesis required to determine the probability of the conditioned +variable.Under what circumstances are the marginal distributions over the +conditioning variables sufficient to determine the probability ofthe +conditioned variable?Sufficiency in this sense is equivalent to additive +separability ofthe conditional probability distribution.Such separability +structure is natural and can be exploited forefficient inference.Separability +has a natural generalization to conditional separability.Separability provides +a precise notion of weaklyinteracting subsystems in temporal probabilistic +models.Given a system that is decomposed into separable subsystems, +exactmarginal probabilities over subsystems at future points in time can +becomputed by propagating marginal subsystem probabilities, rather thancomplete +system joint probabilities.Thus, separability can make exact prediction +tractable.However, observations can break separability,so exact monitoring of +dynamic systems remains hard. +","Sufficiency, Separability and Temporal Probabilistic Models" +" There is increasing interest within the research community in the design and +use of recursive probability models. Although there still remains concern about +computational complexity costs and the fact that computing exact solutions can +be intractable for many nonrecursive models and impossible in the general case +for recursive problems, several research groups are actively developing +computational techniques for recursive stochastic languages. We have developed +an extension to the traditional lambda-calculus as a framework for families of +Turing complete stochastic languages. We have also developed a class of exact +inference algorithms based on the traditional reductions of the +lambda-calculus. We further propose that using the deBruijn notation (a +lambda-calculus notation with nameless dummies) supports effective caching in +such systems (caching being an essential component of efficient computation). +Finally, our extension to the lambda-calculus offers a foundation and general +theory for the construction of recursive stochastic modeling languages as well +as promise for effective caching and efficient approximation algorithms for +inference. +",Toward General Analysis of Recursive Probability Models +" We propose a new approach to value-directed belief state approximation for +POMDPs. The value-directed model allows one to choose approximation methods for +belief state monitoring that have a small impact on decision quality. Using a +vector space analysis of the problem, we devise two new search procedures for +selecting an approximation scheme that have much better computational +properties than existing methods. Though these provide looser error bounds, we +show empirically that they have a similar impact on decision quality in +practice, and run up to two orders of magnitude more quickly. +",Vector-space Analysis of Belief-state Approximation for POMDPs +" We consider the problem of approximate belief-state monitoring using particle +filtering for the purposes of implementing a policy for a partially-observable +Markov decision process (POMDP). While particle filtering has become a +widely-used tool in AI for monitoring dynamical systems, rather scant attention +has been paid to their use in the context of decision making. Assuming the +existence of a value function, we derive error bounds on decision quality +associated with filtering using importance sampling. We also describe an +adaptive procedure that can be used to dynamically determine the number of +samples required to meet specific error bounds. Empirical evidence is offered +supporting this technique as a profitable means of directing sampling effort +where it is needed to distinguish policies. +",Value-Directed Sampling Methods for POMDPs +" We investigate a model for planning under uncertainty with temporallyextended +actions, where multiple actions can be taken concurrently at each decision +epoch. Our model is based on the options framework, and combines it with +factored state space models,where the set of options can be partitioned into +classes that affectdisjoint state variables. We show that the set of +decisionepochs for concurrent options defines a semi-Markov decisionprocess, if +the underlying temporally extended actions being parallelized arerestricted to +Markov options. This property allows us to use SMDPalgorithms for computing the +value function over concurrentoptions. The concurrent options model allows +overlapping execution ofoptions in order to achieve higher performance or in +order to performa complex task. We describe a simple experiment using a +navigationtask which illustrates how concurrent options results in a faster +planwhen compared to the case when only one option is taken at a time. +",Decision-Theoretic Planning with Concurrent Temporally Extended Actions +" We consider a partially observable Markov decision problem (POMDP) that +models a class of sequencing problems. Although POMDPs are typically +intractable, our formulation admits tractable solution. Instead of maintaining +a value function over a high-dimensional set of belief states, we reduce the +state space to one of smaller dimension, in which grid-based dynamic +programming techniques are effective. We develop an error bound for the +resulting approximation, and discuss an application of the model to a problem +in targeted advertising. +",A Tractable POMDP for a Class of Sequencing Problems +" We propose a new method of discovering causal structures, based on the +detection of local, spontaneous changes in the underlying data-generating +model. We analyze the classes of structures that are equivalent relative to a +stream of distributions produced by local changes, and devise algorithms that +output graphical representations of these equivalence classes. We present +experimental results, using simulated data, and examine the errors associated +with detection of changes and recovery of structures. +",Causal Discovery from Changes +" A Bayesian Belief Network (BN) is a model of a joint distribution over a +setof n variables, with a DAG structure to represent the immediate +dependenciesbetween the variables, and a set of parameters (aka CPTables) to +represent thelocal conditional probabilities of a node, given each assignment +to itsparents. In many situations, these parameters are themselves random +variables - this may reflect the uncertainty of the domain expert, or may come +from atraining sample used to estimate the parameter values. The distribution +overthese ""CPtable variables"" induces a distribution over the response the +BNwill return to any ""What is Pr(H | E)?"" query. This paper investigates +thevariance of this response, showing first that it is asymptotically +normal,then providing its mean and asymptotical variance. We then present +aneffective general algorithm for computing this variance, which has the +samecomplexity as simply computing the (mean value of) the response itself - +ie,O(n 2^w), where n is the number of variables and w is the effective +treewidth. Finally, we provide empirical evidence that this algorithm, +whichincorporates assumptions and approximations, works effectively in +practice,given only small samples. +",Bayesian Error-Bars for Belief Net Inference +" With the advance of efficient analytical methods for sensitivity analysis +ofprobabilistic networks, the interest in the sensitivities revealed by +real-life networks is rekindled. As the amount of data resulting from a +sensitivity analysis of even a moderately-sized network is alreadyoverwhelming, +methods for extracting relevant information are called for. One such methodis +to study the derivative of the sensitivity functions yielded for a network's +parameters. We further propose to build upon the concept of admissible +deviation, that is, the extent to which a parameter can deviate from the true +value without inducing a change in the most likely outcome. We illustrate these +concepts by means of a sensitivity analysis of a real-life probabilistic +network in oncology. +",Analysing Sensitivity Data from Probabilistic Networks +" Uncertainty plays a central role in spoken dialogue systems. Some stochastic +models like Markov decision process (MDP) are used to model the dialogue +manager. But the partially observable system state and user intention hinder +the natural representation of the dialogue state. MDP-based system degrades +fast when uncertainty about a user's intention increases. We propose a novel +dialogue model based on the partially observable Markov decision process +(POMDP). We use hidden system states and user intentions as the state set, +parser results and low-level information as the observation set, domain actions +and dialogue repair actions as the action set. Here the low-level information +is extracted from different input modals, including speech, keyboard, mouse, +etc., using Bayesian networks. Because of the limitation of the exact +algorithms, we focus on heuristic approximation algorithms and their +applicability in POMDP for dialogue management. We also propose two methods for +grid point selection in grid-based approximation algorithms. +","Planning and Acting under Uncertainty: A New Model for Spoken Dialogue + Systems" +" Recently, there has been a burst in the number of research projects on human +computation via crowdsourcing. Multiple choice (or labeling) questions could be +referred to as a common type of problem which is solved by this approach. As an +application, crowd labeling is applied to find true labels for large machine +learning datasets. Since crowds are not necessarily experts, the labels they +provide are rather noisy and erroneous. This challenge is usually resolved by +collecting multiple labels for each sample, and then aggregating them to +estimate the true label. Although the mechanism leads to high-quality labels, +it is not actually cost-effective. As a result, efforts are currently made to +maximize the accuracy in estimating true labels, while fixing the number of +acquired labels. + This paper surveys methods to aggregate redundant crowd labels in order to +estimate unknown true labels. It presents a unified statistical latent model +where the differences among popular methods in the field correspond to +different choices for the parameters of the model. Afterwards, algorithms to +make inference on these models will be surveyed. Moreover, adaptive methods +which iteratively collect labels based on the previously collected labels and +estimated models will be discussed. In addition, this paper compares the +distinguished methods, and provides guidelines for future work required to +address the current open issues. +",Crowd Labeling: a survey +" In this paper we present a propositional logic programming language for +reasoning under possibilistic uncertainty and representing vague knowledge. +Formulas are represented by pairs (A, c), where A is a many-valued proposition +and c is value in the unit interval [0,1] which denotes a lower bound on the +belief on A in terms of necessity measures. Belief states are modeled by +possibility distributions on the set of all many-valued interpretations. In +this framework, (i) we define a syntax and a semantics of the general +underlying uncertainty logic; (ii) we provide a modus ponens-style calculus for +a sublanguage of Horn-rules and we prove that it is complete for determining +the maximum degree of possibilistic belief with which a fuzzy propositional +variable can be entailed from a set of formulas; and finally, (iii) we show how +the computation of a partial matching between fuzzy propositional variables, in +terms of necessity measures for fuzzy sets, can be included in our logic +programming system. +","A Complete Calculus for Possibilistic Logic Programming with Fuzzy + Propositional Variables" +" We show that if a strictly positive joint probability distribution for a set +of binary random variables factors according to a tree, then vertex separation +represents all and only the independence relations enclosed in the +distribution. The same result is shown to hold also for multivariate strictly +positive normal distributions. Our proof uses a new property of conditional +independence that holds for these two classes of probability distributions. +",Perfect Tree-Like Markovian Distributions +" Possibilistic logic offers a qualitative framework for representing pieces of +information associated with levels of uncertainty of priority. The fusion of +multiple sources information is discussed in this setting. Different classes of +merging operators are considered including conjunctive, disjunctive, +reinforcement, adaptive and averaging operators. Then we propose to analyse +these classes in terms of postulates. This is done by first extending the +postulate for merging classical bases to the case where priorites are avaialbe. +",A Principled Analysis of Merging Operations in Possibilistic Logic +" Planning for distributed agents with partial state information is considered +from a decision- theoretic perspective. We describe generalizations of both the +MDP and POMDP models that allow for decentralized control. For even a small +number of agents, the finite-horizon problems corresponding to both of our +models are complete for nondeterministic exponential time. These complexity +results illustrate a fundamental difference between centralized and +decentralized control of Markov processes. In contrast to the MDP and POMDP +problems, the problems we consider provably do not admit polynomial-time +algorithms and most likely require doubly exponential time to solve in the +worst case. We have thus provided mathematical evidence corresponding to the +intuition that decentralized planning problems cannot easily be reduced to +centralized problems and solved exactly using established techniques. +",The Complexity of Decentralized Control of Markov Decision Processes +" Monitoring plan preconditions can allow for replanning when a precondition +fails, generally far in advance of the point in the plan where the precondition +is relevant. However, monitoring is generally costly, and some precondition +failures have a very small impact on plan quality. We formulate a model for +optimal precondition monitoring, using partially-observable Markov decisions +processes, and describe methods for solving this model efficitively, though +approximately. Specifically, we show that the single-precondition monitoring +problem is generally tractable, and the multiple-precondition monitoring +policies can be efficitively approximated using single-precondition soultions. +",Approximately Optimal Monitoring of Plan Preconditions +" Monte Carlo sampling has become a major vehicle for approximate inference in +Bayesian networks. In this paper, we investigate a family of related simulation +approaches, known collectively as quasi-Monte Carlo methods based on +deterministic low-discrepancy sequences. We first outline several theoretical +aspects of deterministic low-discrepancy sequences, show three examples of such +sequences, and then discuss practical issues related to applying them to belief +updating in Bayesian networks. We propose an algorithm for selecting direction +numbers for Sobol sequence. Our experimental results show that low-discrepancy +sequences (especially Sobol sequence) significantly improve the performance of +simulation algorithms in Bayesian networks compared to Monte Carlo sampling. +","Computational Investigation of Low-Discrepancy Sequences in Simulation + Algorithms for Bayesian Networks" +" A simple advertising strategy that can be used to help increase sales of a +product is to mail out special offers to selected potential customers. Because +there is a cost associated with sending each offer, the optimal mailing +strategy depends on both the benefit obtained from a purchase and how the offer +affects the buying behavior of the customers. In this paper, we describe two +methods for partitioning the potential customers into groups, and show how to +perform a simple cost-benefit analysis to decide which, if any, of the groups +should be targeted. In particular, we consider two decision-tree learning +algorithms. The first is an ""off the shelf"" algorithm used to model the +probability that groups of customers will buy the product. The second is a new +algorithm that is similar to the first, except that for each group, it +explicitly models the probability of purchase under the two mailing scenarios: +(1) the mail is sent to members of that group and (2) the mail is not sent to +members of that group. Using data from a real-world advertising experiment, we +compare the algorithms to each other and to a naive mail-to-all strategy. +",A Decision Theoretic Approach to Targeted Advertising +" This paper describes a Bayesian method for learning causal networks using +samples that were selected in a non-random manner from a population of +interest. Examples of data obtained by non-random sampling include convenience +samples and case-control data in which a fixed number of samples with and +without some condition is collected; such data are not uncommon. The paper +describes a method for combining data under selection with prior beliefs in +order to derive a posterior probability for a model of the causal processes +that are generating the data in the population of interest. The priors include +beliefs about the nature of the non-random sampling procedure. Although exact +application of the method would be computationally intractable for most +realistic datasets, efficient special-case and approximation methods are +discussed. Finally, the paper describes how to combine learning under selection +with previous methods for learning from observational and experimental data +that are obtained on random samples of the population of interest. The net +result is a Bayesian methodology that supports causal modeling and discovery +from a rich mixture of different types of data. +",A Bayesian Method for Causal Modeling and Discovery Under Selection +" This paper analyzes independence concepts for sets of probability measures +associated with directed acyclic graphs. The paper shows that epistemic +independence and the standard Markov condition violate desirable separation +properties. The adoption of a contraction condition leads to d-separation but +still fails to guarantee a belief separation property. To overcome this +unsatisfactory situation, a strong Markov condition is proposed, based on +epistemic independence. The main result is that the strong Markov condition +leads to strong independence and does enforce separation properties; this +result implies that (1) separation properties of Bayesian networks do extend to +epistemic independence and sets of probability measures, and (2) strong +independence has a clear justification based on epistemic independence and the +strong Markov condition. +",Separation Properties of Sets of Probability Measures +" Algorithms for exact and approximate inference in stochastic logic programs +(SLPs) are presented, based respectively, on variable elimination and +importance sampling. We then show how SLPs can be used to represent prior +distributions for machine learning, using (i) logic programs and (ii) Bayes net +structures as examples. Drawing on existing work in statistics, we apply the +Metropolis-Hasting algorithm to construct a Markov chain which samples from the +posterior distribution. A Prolog implementation for this is described. We also +discuss the possibility of constructing explicit representations of the +posterior. +","Stochastic Logic Programs: Sampling, Inference and Applications" +" We present a new approach for inference in Bayesian networks, which is mainly +based on partial differentiation. According to this approach, one compiles a +Bayesian network into a multivariate polynomial and then computes the partial +derivatives of this polynomial with respect to each variable. We show that once +such derivatives are made available, one can compute in constant-time answers +to a large class of probabilistic queries, which are central to classical +inference, parameter estimation, model validation and sensitivity analysis. We +present a number of complexity results relating to the compilation of such +polynomials and to the computation of their partial derivatives. We argue that +the combined simplicity, comprehensiveness and computational complexity of the +presented framework is unique among existing frameworks for inference in +Bayesian networks. +",A Differential Approach to Inference in Bayesian Networks +" We have recently introduced an any-space algorithm for exact inference in +Bayesian networks, called Recursive Conditioning, RC, which allows one to trade +space with time at increments of X-bytes, where X is the number of bytes needed +to cache a floating point number. In this paper, we present three key +extensions of RC. First, we modify the algorithm so it applies to more general +factorization of probability distributions, including (but not limited to) +Bayesian network factorizations. Second, we present a forgetting mechanism +which reduces the space requirements of RC considerably and then compare such +requirmenets with those of variable elimination on a number of realistic +networks, showing orders of magnitude improvements in certain cases. Third, we +present a version of RC for computing maximum a posteriori hypotheses (MAP), +which turns out to be the first MAP algorithm allowing a smooth time-space +tradeoff. A key advantage of presented MAP algorithm is that it does not have +to start from scratch each time a new query is presented, but can reuse some of +its computations across multiple queries, leading to significant savings in +ceratain cases. +",Any-Space Probabilistic Inference +" In this paper, we use evidence-specific value abstraction for speeding +Bayesian networks inference. This is done by grouping variable values and +treating the combined values as a single entity. As we show, such abstractions +can exploit regularities in conditional probability distributions and also the +specific values of observed variables. To formally justify value abstraction, +we define the notion of safe value abstraction and devise inference algorithms +that use it to reduce the cost of inference. Our procedure is particularly +useful for learning complex networks with many hidden variables. In such cases, +repeated likelihood computations are required for EM or other parameter +optimization techniques. Since these computations are repeated with respect to +the same evidence set, our methods can provide significant speedup to the +learning procedure. We demonstrate the algorithm on genetic linkage problems +where the use of value abstraction sometimes differentiates between a feasible +and non-feasible solution. +",Likelihood Computations Using Value Abstractions +" In this paper, we formulate a qualitative ""linear"" utility theory for +lotteries in which uncertainty is expressed qualitatively using a Spohnian +disbelief function. We argue that a rational decision maker facing an uncertain +decision problem in which the uncertainty is expressed qualitatively should +behave so as to maximize ""qualitative expected utility."" Our axiomatization of +the qualitative utility is similar to the axiomatization developed by von +Neumann and Morgenstern for probabilistic lotteries. We compare our results +with other recent results in qualitative decision making. +","A Qualitative Linear Utility Theory for Spohn's Theory of Epistemic + Beliefs" +" Many intelligent user interfaces employ application and user models to +determine the user's preferences, goals and likely future actions. Such models +require application analysis, adaptation and expansion. Building and +maintaining such models adds a substantial amount of time and labour to the +application development cycle. We present a system that observes the interface +of an unmodified application and records users' interactions with the +application. From a history of such observations we build a coarse state space +of observed interface states and actions between them. To refine the space, we +hypothesize sub-states based upon the histories that led users to a given +state. We evaluate the information gain of possible state splits, varying the +length of the histories considered in such splits. In this way, we +automatically produce a stochastic dynamic model of the application and of how +it is used. To evaluate our approach, we present models derived from real-world +application usage data. +",Building a Stochastic Dynamic Model of Application Use +" We give an interpretation of the Maximum Entropy (MaxEnt) Principle in +game-theoretic terms. Based on this interpretation, we make a formal +distinction between different ways of {em applying/} Maximum Entropy +distributions. MaxEnt has frequently been criticized on the grounds that it +leads to highly representation dependent results. Our distinction allows us to +avoid this problem in many cases. +",Maximum Entropy and the Glasses You Are Looking Through +" We document a connection between constraint reasoning and probabilistic +reasoning. We present an algorithm, called {em probabilistic arc consistency}, +which is both a generalization of a well known algorithm for arc consistency +used in constraint reasoning, and a specialization of the belief updating +algorithm for singly-connected networks. Our algorithm is exact for singly- +connected constraint problems, but can work well as an approximation for +arbitrary problems. We briefly discuss some empirical results, and related +methods. +","Probabilistic Arc Consistency: A Connection between Constraint Reasoning + and Probabilistic Reasoning" +" Composition of low-dimensional distributions, whose foundations were laid in +the papaer published in the Proceeding of UAI'97 (Jirousek 1997), appeared to +be an alternative apparatus to describe multidimensional probabilistic models. +In contrast to Graphical Markov Models, which define multidomensinoal +distributions in a declarative way, this approach is rather procedural. +Ordering of low-dimensional distributions into a proper sequence fully defines +the resepctive computational procedure; therefore, a stury of different type of +generating sequences is one fo the central problems in this field. Thus, it +appears that an important role is played by special sequences that are called +perfect. Their main characterization theorems are presetned in this paper. +However, the main result of this paper is a solution to the problem of +margnialization for general sequences. The main theorem describes a way to +obtain a generating sequence that defines the model corresponding to the +marginal of the distribution defined by an arbitrary genearting sequence. From +this theorem the reader can see to what extent these comutations are local; +i.e., the sequence consists of marginal distributions whose computation must be +made by summing up over the values of the variable eliminated (the paper deals +with finite model). +",Marginalization in Composed Probabilistic Models +" To investigate the robustness of the output probabilities of a Bayesian +network, a sensitivity analysis can be performed. A one-way sensitivity +analysis establishes, for each of the probability parameters of a network, a +function expressing a posterior marginal probability of interest in terms of +the parameter. Current methods for computing the coefficients in such a +function rely on a large number of network evaluations. In this paper, we +present a method that requires just a single outward propagation in a junction +tree for establishing the coefficients in the functions for all possible +parameters; in addition, an inward propagation is required for processing +evidence. Conversely, the method requires a single outward propagation for +computing the coefficients in the functions expressing all possible posterior +marginals in terms of a single parameter. We extend these results to an n-way +sensitivity analysis in which sets of parameters are studied. +",Making Sensitivity Analysis Computationally Efficient +" Many large MDPs can be represented compactly using a dynamic Bayesian +network. Although the structure of the value function does not retain the +structure of the process, recent work has shown that value functions in +factored MDPs can often be approximated well using a decomposed value function: +a linear combination of restricted basis functions, each of which refers +only to a small subset of variables. An approximate value function for a +particular policy can be computed using approximate dynamic programming, but +this approach (and others) can only produce an approximation relative to a +distance metric which is weighted by the stationary distribution of the current +policy. This type of weighted projection is ill-suited to policy improvement. +We present a new approach to value determination, that uses a simple +closed-form computation to directly compute a least-squares decomposed +approximation to the value function for any weights. We then use this +value determination algorithm as a subroutine in a policy iteration process. We +show that, under reasonable restrictions, the policies induced by a factored +value function are compactly represented, and can be manipulated efficiently in +a policy iteration process. We also present a method for computing error bounds +for decomposed value functions using a variable-elimination algorithm for +function optimization. The complexity of all of our algorithms depends on the +factorization of system dynamics and of the approximate value function. +",Policy Iteration for Factored MDPs +" We propose a framework for building graphical causal model that is based on +the concept of causal mechanisms. Causal models are intuitive for human users +and, more importantly, support the prediction of the effect of manipulation. We +describe an implementation of the proposed framework as an interactive model +construction module, ImaGeNIe, in SMILE (Structural Modeling, Inference, and +Learning Engine) and in GeNIe (SMILE's Windows user interface). +",Causal Mechanism-based Model Construction +" We apply the principle of maximum entropy to select a unique joint +probability distribution from the set of all joint probability distributions +specified by a credal network. In detail, we start by showing that the unique +joint distribution of a Bayesian tree coincides with the maximum entropy model +of its conditional distributions. This result, however, does not hold anymore +for general Bayesian networks. We thus present a new kind of maximum entropy +models, which are computed sequentially. We then show that for all general +Bayesian networks, the sequential maximum entropy model coincides with the +unique joint distribution. Moreover, we apply the new principle of sequential +maximum entropy to interval Bayesian networks and more generally to credal +networks. We especially show that this application is equivalent to a number of +small local entropy maximizations. +",Credal Networks under Maximum Entropy +" We propose a formal framework for intelligent systems which can reason about +scientific domains, in particular about the carcinogenicity of chemicals, and +we study its properties. Our framework is grounded in a philosophy of +scientific enquiry and discourse, and uses a model of dialectical +argumentation. The formalism enables representation of scientific uncertainty +and conflict in a manner suitable for qualitative reasoning about the domain. +",Risk Agoras: Dialectical Argumentation for Scientific Reasoning +" Many applications of intelligent systems require reasoning about the mental +states of agents in the domain. We may want to reason about an agent's beliefs, +including beliefs about other agents; we may also want to reason about an +agent's preferences, and how his beliefs and preferences relate to his +behavior. We define a probabilistic epistemic logic (PEL) in which belief +statements are given a formal semantics, and provide an algorithm for asserting +and querying PEL formulas in Bayesian networks. We then show how to reason +about an agent's behavior by modeling his decision process as an influence +diagram and assuming that he behaves rationally. PEL can then be used for +reasoning from an agent's observed actions to conclusions about other aspects +of the domain, including unobserved domain variables and the agent's mental +states. +",Probabilistic Models for Agents' Beliefs and Decisions +" This paper deals with the representation and solution of asymmetric Bayesian +decision problems. We present a formal framework, termed asymmetric influence +diagrams, that is based on the influence diagram and allows an efficient +representation of asymmetric decision problems. As opposed to existing +frameworks, the asymmetric influece diagram primarily encodes asymmetry at the +qualitative level and it can therefore be read directly from the model. We give +an algorithm for solving asymmetric influence diagrams. The algorithm initially +decomposes the asymmetric decision problem into a structure of symmetric +subproblems organized as a tree. A solution to the decision problem can then be +found by propagating from the leaves toward the root using existing evaluation +methods to solve the sub-problems. +",Representing and Solving Asymmetric Bayesian Decision Problems +" When using Bayesian networks for modelling the behavior of man-made +machinery, it usually happens that a large part of the model is deterministic. +For such Bayesian networks deterministic part of the model can be represented +as a Boolean function, and a central part of belief updating reduces to the +task of calculating the number of satisfying configurations in a Boolean +function. In this paper we explore how advances in the calculation of Boolean +functions can be adopted for belief updating, in particular within the context +of troubleshooting. We present experimental results indicating a substantial +speed-up compared to traditional junction tree propagation. +","Using ROBDDs for Inference in Bayesian Networks with Troubleshooting as + an Example" +" We present a new approach to the solution of decision problems formulated as +influence diagrams. The approach converts the influence diagram into a simpler +structure, the LImited Memory Influence Diagram (LIMID), where only the +requisite information for the computation of optimal policies is depicted. +Because the requisite information is explicitly represented in the diagram, the +evaluation procedure can take advantage of it. In this paper we show how to +convert an influence diagram to a LIMID and describe the procedure for finding +an optimal strategy. Our approach can yield significant savings of memory and +computational time when compared to traditional methods. +",Evaluating Influence Diagrams using LIMIDs +" Conversations abound with uncetainties of various kinds. Treating +conversation as inference and decision making under uncertainty, we propose a +task independent, multimodal architecture for supporting robust continuous +spoken dialog called Quartet. We introduce four interdependent levels of +analysis, and describe representations, inference procedures, and decision +strategies for managing uncertainties within and between the levels. We +highlight the approach by reviewing interactions between a user and two spoken +dialog systems developed using the Quartet architecture: Prsenter, a prototype +system for navigating Microsoft PowerPoint presentations, and the Bayesian +Receptionist, a prototype system for dealing with tasks typically handled by +front desk receptionists at the Microsoft corporate campus. +",Conversation as Action Under Uncertainty +" We consider the problem belief-state monitoring for the purposes of +implementing a policy for a partially-observable Markov decision process +(POMDP), specifically how one might approximate the belief state. Other schemes +for belief-state approximation (e.g., based on minimixing a measures such as +KL-diveregence between the true and estimated state) are not necessarily +appropriate for POMDPs. Instead we propose a framework for analyzing +value-directed approximation schemes, where approximation quality is determined +by the expected error in utility rather than by the error in the belief state +itself. We propose heuristic methods for finding good projection schemes for +belief state estimation - exhibiting anytime characteristics - given a POMDP +value fucntion. We also describe several algorithms for constructing bounds on +the error in decision quality (expected utility) associated with acting in +accordance with a given belief state approximation. +",Value-Directed Belief State Approximation for POMDPs +" Techniques for plan recognition under uncertainty require a stochastic model +of the plan-generation process. We introduce Probabilistic State-Dependent +Grammars (PSDGs) to represent an agent's plan-generation process. The PSDG +language model extends probabilistic context-free grammars (PCFGs) by allowing +production probabilities to depend on an explicit model of the planning agent's +internal and external state. Given a PSDG description of the plan-generation +process, we can then use inference algorithms that exploit the particular +independence properties of the PSDG language to efficiently answer +plan-recognition queries. The combination of the PSDG language model and +inference algorithms extends the range of plan-recognition domains for which +practical probabilistic inference is possible, as illustrated by applications +in traffic monitoring and air combat. +",Probabilistic State-Dependent Grammars for Plan Recognition +" Qualitative probabilistic networks have been designed for probabilistic +reasoning in a qualitative way. Due to their coarse level of representation +detail, qualitative probabilistic networks do not provide for resolving +trade-offs and typically yield ambiguous results upon inference. We present an +algorithm for computing more insightful results for unresolved trade-offs. The +algorithm builds upon the idea of using pivots to zoom in on the trade-offs and +identifying the information that would serve to resolve them. +",Pivotal Pruning of Trade-offs in QPNs +" This paper describes a domain-specific knowledge acquisition tool for +intelligent automated troubleshooters based on Bayesian networks. No Bayesian +network knowledge is required to use the tool, and troubleshooting information +can be specified as natural and intuitive as possible. Probabilities can be +specified in the direction that is most natural to the domain expert. Thus, the +knowledge acquisition efficiently removes the traditional knowledge acquisition +bottleneck of Bayesian networks. +",A Knowledge Acquisition Tool for Bayesian-Network Troubleshooters +" In this paper, we present a heuristic operator which aims at simultaneously +optimizing the orientations of all the edges in an intermediate Bayesian +network structure during the search process. This is done by alternating +between the space of directed acyclic graphs (DAGs) and the space of skeletons. +The found orientations of the edges are based on a scoring function rather than +on induced conditional independences. This operator can be used as an extension +to commonly employed search strategies. It is evaluated in experiments with +artificial and real-world data. +",On the Use of Skeletons when Learning in Bayesian Networks +" This paper deals with the problem of estimating the probability that one +event was a cause of another in a given scenario. Using structural-semantical +definitions of the probabilities of necessary or sufficient causation (or +both), we show how to optimally bound these quantities from data obtained in +experimental and observational studies, making minimal assumptions concerning +the data-generating process. In particular, we strengthen the results of Pearl +(1999) by weakening the data-generation assumptions and deriving theoretically +sharp bounds on the probabilities of causation. These results delineate +precisely how empirical data can be used both in settling questions of +attribution and in solving attribution-related problems of decision making. +",Probabilities of Causation: Bounds and Identification +" Conditional independence and Markov properties are powerful tools allowing +expression of multidimensional probability distributions by means of +low-dimensional ones. As multidimensional possibilistic models have been +studied for several years, the demand for analogous tools in possibility theory +seems to be quite natural. This paper is intended to be a promotion of de +Cooman's measure-theoretic approcah to possibility theory, as this approach +allows us to find analogies to many important results obtained in probabilistic +framework. First, we recall semi-graphoid properties of conditional +possibilistic independence, parameterized by a continuous t-norm, and find +sufficient conditions for a class of Archimedean t-norms to have the graphoid +property. Then we introduce Markov properties and factorization of possibility +distrubtions (again parameterized by a continuous t-norm) and find the +relationships between them. These results are accompanied by a number of +conterexamples, which show that the assumptions of specific theorems are +substantial. +",Conditional Independence and Markov Properties in Possibility Theory +" Algorithms for learning the conditional probabilities of Bayesian networks +with hidden variables typically operate within a high-dimensional search space +and yield only locally optimal solutions. One way of limiting the search space +and avoiding local optima is to impose qualitative constraints that are based +on background knowledge concerning the domain. We present a method for +integrating formal statements of qualitative constraints into two learning +algorithms, APN and EM. In our experiments with synthetic data, this method +yielded networks that satisfied the constraints almost perfectly. The accuracy +of the learned networks was consistently superior to that of corresponding +networks learned without constraints. The exploitation of qualitative +constraints therefore appears to be a promising way to increase both the +interpretability and the accuracy of learned Bayesian networks with known +structure. +","Exploiting Qualitative Knowledge in the Learning of Conditional + Probabilities of Bayesian Networks" +" Constraints that may be obtained by composition from simpler constraints are +present, in some way or another, in almost every constraint program. The +decomposition of such constraints is a standard technique for obtaining an +adequate propagation algorithm from a combination of propagators designed for +simpler constraints. The decomposition approach is appealing in several ways. +Firstly because creating a specific propagator for every constraint is clearly +infeasible since the number of constraints is infinite. Secondly, because +designing a propagation algorithm for complex constraints can be very +challenging. Finally, reusing existing propagators allows to reduce the size of +code to be developed and maintained. Traditionally, constraint solvers +automatically decompose constraints into simpler ones using additional +auxiliary variables and propagators, or expect the users to perform such +decomposition themselves, eventually leading to the same propagation model. In +this paper we explore views, an alternative way to create efficient propagators +for such constraints in a modular, simple and correct way, which avoids the +introduction of auxiliary variables and propagators. +",View-based propagation of decomposable constraints +" Elicitation of probabilities is one of the most laborious tasks in building +decision-theoretic models, and one that has so far received only moderate +attention in decision-theoretic systems. We propose a set of user interface +tools for graphical probabilistic models, focusing on two aspects of +probability elicitation: (1) navigation through conditional probability tables +and (2) interactive graphical assessment of discrete probability distributions. +We propose two new graphical views that aid navigation in very large +conditional probability tables: the CPTree (Conditional Probability Tree) and +the SCPT (shrinkable Conditional Probability Table). Based on what is known +about graphical presentation of quantitative data to humans, we offer several +useful enhancements to probability wheel and bar graph, including different +chart styles and options that can be adapted to user preferences and needs. We +present the results of a simple usability study that proves the value of the +proposed tools. +","User Interface Tools for Navigation in Conditional Probability Tables + and Elicitation of Probabilities in Bayesian Networks" +" This is the Proceedings of the Twenty-Eighth Conference on Uncertainty in +Artificial Intelligence, which was held on Catalina Island, CA August 14-18 +2012. +","Proceedings of the Twenty-Eighth Conference on Uncertainty in Artificial + Intelligence (2012)" +" This is the Proceedings of the Nineteenth Conference on Uncertainty in +Artificial Intelligence, which was held in Acapulco, Mexico, August 7-10 2003 +","Proceedings of the Nineteenth Conference on Uncertainty in Artificial + Intelligence (2003)" +" This is the Proceedings of the Seventeenth Conference on Uncertainty in +Artificial Intelligence, which was held in Seattle, WA, August 2-5 2001 +","Proceedings of the Seventeenth Conference on Uncertainty in Artificial + Intelligence (2001)" +" This is the Proceedings of the Eighteenth Conference on Uncertainty in +Artificial Intelligence, which was held in Alberta, Canada, August 1-4 2002 +","Proceedings of the Eighteenth Conference on Uncertainty in Artificial + Intelligence (2002)" +" Handwriting is one of the most important means of daily communication. +Although the problem of handwriting recognition has been considered for more +than 60 years there are still many open issues, especially in the task of +unconstrained handwritten sentence recognition. This paper focuses on the +automatic system that recognizes continuous English sentence through a +mouse-based gestures in real-time based on Artificial Neural Network. The +proposed Artificial Neural Network is trained using the traditional +backpropagation algorithm for self supervised neural network which provides the +system with great learning ability and thus has proven highly successful in +training for feed-forward Artificial Neural Network. The designed algorithm is +not only capable of translating discrete gesture moves, but also continuous +gestures through the mouse. In this paper we are using the efficient neural +network approach for recognizing English sentence drawn by mouse. This approach +shows an efficient way of extracting the boundary of the English Sentence and +specifies the area of the recognition English sentence where it has been drawn +in an image and then used Artificial Neural Network to recognize the English +sentence. The proposed approach English sentence recognition (ESR) system is +designed and tested successfully. Experimental results show that the higher +speed and accuracy were examined. +","English Sentence Recognition using Artificial Neural Network through + Mouse-based Gestures" +" This paper presents a knowledge-based detection of objects approach using the +OWL ontology language, the Semantic Web Rule Language, and 3D processing +built-ins aiming at combining geometrical analysis of 3D point clouds and +specialist's knowledge. Here, we share our experience regarding the creation of +3D semantic facility model out of unorganized 3D point clouds. Thus, a +knowledge-based detection approach of objects using the OWL ontology language +is presented. This knowledge is used to define SWRL detection rules. In +addition, the combination of 3D processing built-ins and topological Built-Ins +in SWRL rules allows a more flexible and intelligent detection, and the +annotation of objects contained in 3D point clouds. The created WiDOP prototype +takes a set of 3D point clouds as input, and produces as output a populated +ontology corresponding to an indexed scene visualized within VRML language. The +context of the study is the detection of railway objects materialized within +the Deutsche Bahn scene such as signals, technical cupboards, electric poles, +etc. Thus, the resulting enriched and populated ontology, that contains the +annotations of objects in the point clouds, is used to feed a GIS system or an +IFC file for architecture purposes. +","Knowledge Base Approach for 3D Objects Detection in Point Clouds Using + 3D Processing and Specialists Knowledge" +" This paper presents a method to compute automatically topological relations +using SWRL rules. The calculation of these rules is based on the definition of +a Selective Nef Complexes Nef Polyhedra structure generated from standard +Polyhedron. The Selective Nef Complexes is a data model providing a set of +binary Boolean operators such as Union, Difference, Intersection and Symmetric +difference, and unary operators such as Interior, Closure and Boundary. In this +work, these operators are used to compute topological relations between objects +defined by the constraints of the 9 Intersection Model (9-IM) from Egenhofer. +With the help of these constraints, we defined a procedure to compute the +topological relations on Nef polyhedra. These topological relationships are +Disjoint, Meets, Contains, Inside, Covers, CoveredBy, Equals and Overlaps, and +defined in a top-level ontology with a specific semantic definition on relation +such as Transitive, Symmetric, Asymmetric, Functional, Reflexive, and +Irreflexive. The results of the computation of topological relationships are +stored in an OWL-DL ontology allowing after what to infer on these new +relationships between objects. In addition, logic rules based on the Semantic +Web Rule Language allows the definition of logic programs that define which +topological relationships have to be computed on which kind of objects with +specific attributes. For instance, a ""Building"" that overlaps a ""Railway"" is a +""RailStation"". +","From 9-IM Topological Operators to Qualitative Spatial Relations using + 3D Selective Nef Complexes and Logic Rules for bodies" +" Computer Poker's unique characteristics present a well-suited challenge for +research in artificial intelligence. For that reason, and due to the Poker's +market increase in popularity in Portugal since 2008, several members of LIACC +have researched in this field. Several works were published as papers and +master theses and more recently a member of LIACC engaged on a research in this +area as a Ph.D. thesis in order to develop a more extensive and in-depth work. +This paper describes the existing research in LIACC about Computer Poker, with +special emphasis on the completed master's theses and plans for future work. +This paper means to present a summary of the lab's work to the research +community in order to encourage the exchange of ideas with other labs / +individuals. LIACC hopes this will improve research in this area so as to reach +the goal of creating an agent that surpasses the best human players. +",Computer Poker Research at LIACC +" Medical diagnosis process vary in the degree to which they attempt to deal +with different complicating aspects of diagnosis such as relative importance of +symptoms, varied symptom pattern and the relation between diseases them selves. +Based on decision theory, in the past many mathematical models such as crisp +set, probability distribution, fuzzy set, intuitionistic fuzzy set were +developed to deal with complicating aspects of diagnosis. But, many such models +are failed to include important aspects of the expert decisions. Therefore, an +effort has been made to process inconsistencies in data being considered by +Pawlak with the introduction of rough set theory. Though rough set has major +advantages over the other methods, but it generates too many rules that create +many difficulties while taking decisions. Therefore, it is essential to +minimize the decision rules. In this paper, we use two processes such as pre +process and post process to mine suitable rules and to explore the relationship +among the attributes. In pre process we use rough set theory to mine suitable +rules, whereas in post process we use formal concept analysis from these +suitable rules to explore better knowledge and most important factors affecting +the decision making. +","A Framework for Intelligent Medical Diagnosis using Rough Set with + Formal Concept Analysis" +" The dependency graph is a data architecture that models all the dependencies +between the different types of assets in the game. It depicts the +dependency-based relationships between the assets of a game. For example, a +player must construct an arsenal before he can build weapons. It is vital that +the dependency graph of a game is designed logically to ensure a logical +sequence of game play. However, a mere logical dependency graph is not +sufficient in sustaining the players' enduring interests in a game, which +brings the problem of game balancing into picture. The issue of game balancing +arises when the players do not feel the chances of winning the game over their +AI opponents who are more skillful in the game play. At the current state of +research, the architecture of dependency graph is monolithic for the players. +The sequence of asset possession is always foreseeable because there is only a +single dependency graph. Game balancing is impossible when the assets of AI +players are overwhelmingly outnumbering that of human players. This paper +proposes a parallel architecture of dependency graph for the AI players and +human players. Instead of having a single dependency graph, a parallel +architecture is proposed where the dependency graph of AI player is adjustable +with that of human player using a support dependency as a game balancing +mechanism. This paper exhibits that the parallel dependency graph helps to +improve game balancing. +",Developing Parallel Dependency Graph In Improving Game Balancing +" The main prospective aim of modern research related to Artificial +Intelligence is the creation of technical systems that implement the idea of +Strong Intelligence. According our point of view the path to the development of +such systems comes through the research in the field related to perceptions. +Here we formulate the model of the perception of external world which may be +used for the description of perceptual activity of intelligent beings. We +consider a number of issues related to the development of the set of patterns +which will be used by the intelligent system when interacting with environment. +The key idea of the presented perception model is the idea of subjective +reality. The principle of the relativity of perceived world is formulated. It +is shown that this principle is the immediate consequence of the idea of +subjective reality. In this paper we show how the methodology of subjective +reality may be used for the creation of different types of Strong AI systems. +",Subjective Reality and Strong Artificial Intelligence +" Diagnosis and prediction in some domains, like medical and industrial +diagnosis, require a representation that combines uncertainty management and +temporal reasoning. Based on the fact that in many cases there are few state +changes in the temporal range of interest, we propose a novel representation +called Temporal Nodes Bayesian Networks (TNBN). In a TNBN each node represents +an event or state change of a variable, and an arc corresponds to a +causal-temporal relationship. The temporal intervals can differ in number and +size for each temporal node, so this allows multiple granularity. Our approach +is contrasted with a dynamic Bayesian network for a simple medical example. An +empirical evaluation is presented for a more complex problem, a subsystem of a +fossil power plant, in which this approach is used for fault diagnosis and +prediction with good results. +",A Temporal Bayesian Network for Diagnosis and Prediction +" Possibilistic logic bases and possibilistic graphs are two different +frameworks of interest for representing knowledge. The former stratifies the +pieces of knowledge (expressed by logical formulas) according to their level of +certainty, while the latter exhibits relationships between variables. The two +types of representations are semantically equivalent when they lead to the same +possibility distribution (which rank-orders the possible interpretations). A +possibility distribution can be decomposed using a chain rule which may be +based on two different kinds of conditioning which exist in possibility theory +(one based on product in a numerical setting, one based on minimum operation in +a qualitative setting). These two types of conditioning induce two kinds of +possibilistic graphs. In both cases, a translation of these graphs into +possibilistic bases is provided. The converse translation from a possibilistic +knowledge base into a min-based graph is also described. +",Possibilistic logic bases and possibilistic graphs +" Our hypothesis is that by equipping certain agents in a multi-agent system +controlling an intelligent building with automated decision support, two +important factors will be increased. The first is energy saving in the +building. The second is customer value---how the people in the building +experience the effects of the actions of the agents. We give evidence for the +truth of this hypothesis through experimental findings related to tools for +artificial decision making. A number of assumptions related to agent control, +through monitoring and delegation of tasks to other kinds of agents, of rooms +at a test site are relaxed. Each assumption controls at least one uncertainty +that complicates considerably the procedures for selecting actions part of each +such agent. We show that in realistic decision situations, room-controlling +agents can make bounded rational decisions even under dynamic real-time +constraints. This result can be, and has been, generalized to other domains +with even harsher time constraints. +",Artificial Decision Making Under Uncertainty in Intelligent Buildings +" In many domains it is desirable to assess the preferences of users in a +qualitative rather than quantitative way. Such representations of qualitative +preference orderings form an importnat component of automated decision tools. +We propose a graphical representation of preferences that reflects conditional +dependence and independence of preference statements under a ceteris paribus +(all else being equal) interpretation. Such a representation is ofetn compact +and arguably natural. We describe several search algorithms for dominance +testing based on this representation; these algorithms are quite effective, +especially in specific network topologies, such as chain-and tree- structured +networks, as well as polytrees. +",Reasoning With Conditional Ceteris Paribus Preference Statem +" This paper describes a Bayesian method for combining an arbitrary mixture of +observational and experimental data in order to learn causal Bayesian networks. +Observational data are passively observed. Experimental data, such as that +produced by randomized controlled trials, result from the experimenter +manipulating one or more variables (typically randomly) and observing the +states of other variables. The paper presents a Bayesian method for learning +the causal structure and parameters of the underlying causal process that is +generating the data, given that (1) the data contains a mixture of +observational and experimental case records, and (2) the causal process is +modeled as a causal Bayesian network. This learning method was applied using as +input various mixtures of experimental and observational data that were +generated from the ALARM causal Bayesian network. In these experiments, the +absolute and relative quantities of experimental and observational data were +varied systematically. For each of these training datasets, the learning method +was applied to predict the causal structure and to estimate the causal +parameters that exist among randomly selected pairs of nodes in ALARM that are +not confounded. The paper reports how these structure predictions and parameter +estimates compare with the true causal structures and parameters as given by +the ALARM network. +",Causal Discovery from a Mixture of Experimental and Observational Data +" Recent work on loglinear models in probabilistic constraint logic programming +is applied to first-order probabilistic reasoning. Probabilities are defined +directly on the proofs of atomic formulae, and by marginalisation on the atomic +formulae themselves. We use Stochastic Logic Programs (SLPs) composed of +labelled and unlabelled definite clauses to define the proof probabilities. We +have a conservative extension of first-order reasoning, so that, for example, +there is a one-one mapping between logical and random variables. We show how, +in this framework, Inductive Logic Programming (ILP) can be used to induce the +features of a loglinear model from data. We also compare the presented +framework with other approaches to first-order probabilistic reasoning. +",Loglinear models for first-order probabilistic reasoning +" We present a hybrid constraint-based/Bayesian algorithm for learning causal +networks in the presence of sparse data. The algorithm searches the space of +equivalence classes of models (essential graphs) using a heuristic based on +conventional constraint-based techniques. Each essential graph is then +converted into a directed acyclic graph and scored using a Bayesian scoring +metric. Two variants of the algorithm are developed and tested using data from +randomly generated networks of sizes from 15 to 45 nodes with data sizes +ranging from 250 to 2000 records. Both variations are compared to, and found to +consistently outperform two variations of greedy search with restarts. +","A Hybrid Anytime Algorithm for the Constructiion of Causal Models From + Sparse Data" +" Hybrid Probabilistic Programs (HPPs) are logic programs that allow the +programmer to explicitly encode his knowledge of the dependencies between +events being described in the program. In this paper, we classify HPPs into +three classes called HPP_1,HPP_2 and HPP_r,r>= 3. For these classes, we provide +three types of results for HPPs. First, we develop algorithms to compute the +set of all ground consequences of an HPP. Then we provide algorithms and +complexity results for the problems of entailment (""Given an HPP P and a query +Q as input, is Q a logical consequence of P?"") and consistency (""Given an HPP P +as input, is P consistent?""). Our results provide a fine characterization of +when polynomial algorithms exist for the above problems, and when these +problems become intractable. +",Hybrid Probabilistic Programs: Algorithms and Complexity +" The problem of assessing the value of a candidate is viewed here as a +multiple combination problem. On the one hand a candidate can be evaluated +according to different criteria, and on the other hand several experts are +supposed to assess the value of candidates according to each criterion. +Criteria are not equally important, experts are not equally competent or +reliable. Moreover levels of satisfaction of criteria, or levels of confidence +are only assumed to take their values in qualitative scales which are just +linearly ordered. The problem is discussed within two frameworks, the +transferable belief model and the qualitative possibility theory. They +respectively offer a quantitative and a qualitative setting for handling the +problem, providing thus a way to compare the nature of the underlying +assumptions. +","Assessing the value of a candidate. Comparing belief function and + possibility theories" +" This paper investigates a purely qualitative version of Savage's theory for +decision making under uncertainty. Until now, most representation theorems for +preference over acts rely on a numerical representation of utility and +uncertainty where utility and uncertainty are commensurate. Disrupting the +tradition, we relax this assumption and introduce a purely ordinal axiom +requiring that the Decision Maker (DM) preference between two acts only depends +on the relative position of their consequences for each state. Within this +qualitative framework, we determine the only possible form of the decision rule +and investigate some instances compatible with the transitivity of the strict +preference. Finally we propose a mild relaxation of our ordinality axiom, +leaving room for a new family of qualitative decision rules compatible with +transitivity. +","Qualitative Models for Decision Under Uncertainty without the + Commensurability Assumption" +" In this paper, we analyze the relationship between probability and Spohn's +theory for representation of uncertain beliefs. Using the intuitive idea that +the more probable a proposition is, the more believable it is, we study +transformations from probability to Sphonian disbelief and vice-versa. The +transformations described in this paper are different from those described in +the literature. In particular, the former satisfies the principles of ordinal +congruence while the latter does not. Such transformations between probability +and Spohn's calculi can contribute to (1) a clarification of the semantics of +nonprobabilistic degree of uncertain belief, and (2) to a construction of a +decision theory for such calculi. In practice, the transformations will allow a +meaningful combination of more than one calculus in different stages of using +an expert system such as knowledge acquisition, inference, and interpretation +of results. +",On Transformations between Probability and Spohnian Disbelief Functions +" We present a new abductive, probabilistic theory of plan recognition. This +model differs from previous plan recognition theories in being centered around +a model of plan execution: most previous methods have been based on plans as +formal objects or on rules describing the recognition process. We show that our +new model accounts for phenomena omitted from most previous plan recognition +theories: notably the cumulative effect of a sequence of observations of +partially-ordered, interleaved plans and the effect of context on plan +adoption. The model also supports inferences about the evolution of plan +execution in situations where another agent intervenes in plan execution. This +facility provides support for using plan recognition to build systems that will +intelligently assist a user. +",A New Model of Plan Recognition +" Classical Decision Theory provides a normative framework for representing and +reasoning about complex preferences. Straightforward application of this theory +to automate decision making is difficult due to high elicitation cost. In +response to this problem, researchers have recently developed a number of +qualitative, logic-oriented approaches for representing and reasoning about +references. While effectively addressing some expressiveness issues, these +logics have not proven powerful enough for building practical automated +decision making systems. In this paper we present a hybrid approach to +preference elicitation and decision making that is grounded in classical +multi-attribute utility theory, but can make effective use of the expressive +power of qualitative approaches. Specifically, assuming a partially specified +multilinear utility function, we show how comparative statements about classes +of decision alternatives can be used to further constrain the utility function +and thus identify sup-optimal alternatives. This work demonstrates that +quantitative and qualitative approaches can be synergistically integrated to +provide effective and flexible decision support. +",A Hybrid Approach to Reasoning with Partially Elicited Preference Models +" A conceptual foundation for approximation of belief functions is proposed and +investigated. It is based on the requirements of consistency and closeness. An +optimal approximation is studied. Unfortunately, the computation of the optimal +approximation turns out to be intractable. Hence, various heuristic methods are +proposed and experimantally evaluated both in terms of their accuracy and in +terms of the speed of computation. These methods are compared to the earlier +proposed approximations of belief functions. +",Faithful Approximations of Belief Functions +" Markov decisions processes (MDPs) are becoming increasing popular as models +of decision theoretic planning. While traditional dynamic programming methods +perform well for problems with small state spaces, structured methods are +needed for large problems. We propose and examine a value iteration algorithm +for MDPs that uses algebraic decision diagrams(ADDs) to represent value +functions and policies. An MDP is represented using Bayesian networks and ADDs +and dynamic programming is applied directly to these ADDs. We demonstrate our +method on large MDPs (up to 63 million states) and show that significant gains +can be had when compared to tree-structured representations (with up to a +thirty-fold reduction in the number of nodes required to represent optimal +value functions). +",SPUDD: Stochastic Planning using Decision Diagrams +" We outline a method to estimate the value of computation for a flexible +algorithm using empirical data. To determine a reasonable trade-off between +cost and value, we build an empirical model of the value obtained through +computation, and apply this model to estimate the value of computation for +quite different problems. In particular, we investigate this trade-off for the +problem of constructing policies for decision problems represented as influence +diagrams. We show how two features of our anytime algorithm provide reasonable +estimates of the value of computation in this domain. +",Estimating the Value of Computation in Flexible Information Refinement +" The paper is a second in a series of two papers evaluating the power of a new +scheme that generates search heuristics mechanically. The heuristics are +extracted from an approximation scheme called mini-bucket elimination that was +recently introduced. The first paper introduced the idea and evaluated it +within Branch-and-Bound search. In the current paper the idea is further +extended and evaluated within Best-First search. The resulting algorithms are +compared on coding and medical diagnosis problems, using varying strength of +the mini-bucket heuristics. + Our results demonstrate an effective search scheme that permits controlled +tradeoff between preprocessing (for heuristic generation) and search. +Best-first search is shown to outperform Branch-and-Bound, when supplied with +good heuristics, and sufficient memory space. +",Mini-Bucket Heuristics for Improved Search +" The clique tree algorithm is the standard method for doing inference in +Bayesian networks. It works by manipulating clique potentials - distributions +over the variables in a clique. While this approach works well for many +networks, it is limited by the need to maintain an exact representation of the +clique potentials. This paper presents a new unified approach that combines +approximate inference and the clique tree algorithm, thereby circumventing this +limitation. Many known approximate inference algorithms can be viewed as +instances of this approach. The algorithm essentially does clique tree +propagation, using approximate inference to estimate the densities in each +clique. In many settings, the computation of the approximate clique potential +can be done easily using statistical importance sampling. Iterations are used +to gradually improve the quality of the estimation. +","A General Algorithm for Approximate Inference and its Application to + Hybrid Bayes Nets" +" Most fuzzy systems including fuzzy decision support and fuzzy control systems +provide out-puts in the form of fuzzy sets that represent the inferred +conclusions. Linguistic interpretation of such outputs often involves the use +of linguistic approximation that assigns a linguistic label to a fuzzy set +based on the predefined primary terms, linguistic modifiers and linguistic +connectives. More generally, linguistic approximation can be formalized in the +terms of the re-translation rules that correspond to the translation rules in +ex-plicitation (e.g. simple, modifier, composite, quantification and +qualification rules) in com-puting with words [Zadeh 1996]. However most +existing methods of linguistic approximation use the simple, modifier and +composite re-translation rules only. Although these methods can provide a +sufficient approximation of simple fuzzy sets the approximation of more complex +ones that are typical in many practical applications of fuzzy systems may be +less satisfactory. Therefore the question arises why not use in linguistic +ap-proximation also other re-translation rules corre-sponding to the +translation rules in explicitation to advantage. In particular linguistic +quantifica-tion may be desirable in situations where the conclusions +interpreted as quantified linguistic propositions can be more informative and +natu-ral. This paper presents some aspects of linguis-tic approximation in the +context of the re-translation rules and proposes an approach to linguistic +approximation with the use of quantifi-cation rules, i.e. quantified linguistic +approxima-tion. Two methods of the quantified linguistic approximation are +considered with the use of lin-guistic quantifiers based on the concepts of the +non-fuzzy and fuzzy cardinalities of fuzzy sets. A number of examples are +provided to illustrate the proposed approach. +",On Quantified Linguistic Approximation +" There is available an ever-increasing variety of procedures for managing +uncertainty. These methods are discussed in the literature of artificial +intelligence, as well as in the literature of philosophy of science. Heretofore +these methods have been evaluated by intuition, discussion, and the general +philosophical method of argument and counterexample. Almost any method of +uncertainty management will have the property that in the long run it will +deliver numbers approaching the relative frequency of the kinds of events at +issue. To find a measure that will provide a meaningful evaluation of these +treatments of uncertainty, we must look, not at the long run, but at the short +or intermediate run. Our project attempts to develop such a measure in terms of +short or intermediate length performance. We represent the effects of practical +choices by the outcomes of bets offered to agents characterized by two +uncertainty management approaches: the subjective Bayesian approach and the +Classical confidence interval approach. Experimental evaluation suggests that +the confidence interval approach can outperform the subjective approach in the +relatively short run. +",Choosing Among Interpretations of Probability +" We consider the problem of finding good finite-horizon policies for POMDPs +under the expected reward metric. The policies considered are {em free +finite-memory policies with limited memory}; a policy is a mapping from the +space of observation-memory pairs to the space of action-memeory pairs (the +policy updates the memory as it goes), and the number of possible memory states +is a parameter of the input to the policy-finding algorithms. The algorithms +considered here are preliminary implementations of three search heuristics: +local search, simulated annealing, and genetic algorithms. We compare their +outcomes to each other and to the optimal policies for each instance. We +compare run times of each policy and of a dynamic programming algorithm for +POMDPs developed by Hansen that iteratively improves a finite-state controller +--- the previous state of the art for finite memory policies. The value of the +best policy can only improve as the amount of memory increases, up to the +amount needed for an optimal finite-memory policy. Our most surprising finding +is that more memory helps in another way: given more memory than is needed for +an optimal policy, the algorithms are more likely to converge to optimal-valued +policies. +",My Brain is Full: When More Memory Helps +" Solving symmetric Bayesian decision problems is a computationally intensive +task to perform regardless of the algorithm used. In this paper we propose a +method for improving the efficiency of algorithms for solving Bayesian decision +problems. The method is based on the principle of lazy evaluation - a principle +recently shown to improve the efficiency of inference in Bayesian networks. The +basic idea is to maintain decompositions of potentials and to postpone +computations for as long as possible. The efficiency improvements obtained with +the lazy evaluation based method is emphasized through examples. Finally, the +lazy evaluation based method is compared with the hugin and valuation-based +systems architectures for solving symmetric Bayesian decision problems. +",Lazy Evaluation of Symmetric Bayesian Decision Problems +" This paper extends previous work with network fragments and +situation-specific network construction. We formally define the asymmetry +network, an alternative representation for a conditional probability table. We +also present an object-oriented representation for partially specified +asymmetry networks. We show that the representation is parsimonious. We define +an algebra for the elements of the representation that allows us to 'factor' +any CPT and to soundly combine the partially specified asymmetry networks. +",Representing and Combining Partially Specified CPTs +" Decision-making problems in uncertain or stochastic domains are often +formulated as Markov decision processes (MDPs). Policy iteration (PI) is a +popular algorithm for searching over policy-space, the size of which is +exponential in the number of states. We are interested in bounds on the +complexity of PI that do not depend on the value of the discount factor. In +this paper we prove the first such non-trivial, worst-case, upper bounds on the +number of iterations required by PI to converge to the optimal policy. Our +analysis also sheds new light on the manner in which PI progresses through the +space of policies. +",On the Complexity of Policy Iteration +" We are interested in the problem of planning for factored POMDPs. Building on +the recent results of Kearns, Mansour and Ng, we provide a planning algorithm +for factored POMDPs that exploits the accuracy-efficiency tradeoff in the +belief state simplification introduced by Boyen and Koller. +","Approximate Planning for Factored POMDPs using Belief State + Simplification" +" Solving partially observable Markov decision processes (POMDPs) is highly +intractable in general, at least in part because the optimal policy may be +infinitely large. In this paper, we explore the problem of finding the optimal +policy from a restricted set of policies, represented as finite state automata +of a given size. This problem is also intractable, but we show that the +complexity can be greatly reduced when the POMDP and/or policy are further +constrained. We demonstrate good empirical results with a branch-and-bound +method for finding globally optimal deterministic policies, and a +gradient-ascent method for finding locally optimal stochastic policies. +",Solving POMDPs by Searching the Space of Finite Policies +" Influence diagrams serve as a powerful tool for modelling symmetric decision +problems. When solving an influence diagram we determine a set of strategies +for the decisions involved. A strategy for a decision variable is in principle +a function over its past. However, some of the past may be irrelevant for the +decision, and for computational reasons it is important not to deal with +redundant variables in the strategies. We show that current methods (e.g. the +""Decision Bayes-ball"" algorithm by Shachter UAI98) do not determine the +relevant past, and we present a complete algorithm. + Actually, this paper takes a more general outset: When formulating a decision +scenario as an influence diagram, a linear temporal ordering of the decisions +variables is required. This constraint ensures that the decision scenario is +welldefined. However, the structure of a decision scenario often yields certain +decisions conditionally independent, and it is therefore unnecessary to impose +a linear temporal ordering on the decisions. In this paper we deal with partial +influence diagrams i.e. influence diagrams with only a partial temporal +ordering specified. We present a set of conditions which are necessary and +sufficient to ensure that a partial influence diagram is welldefined. These +conditions are used as a basis for the construction of an algorithm for +determining whether or not a partial influence diagram is welldefined. +",Welldefined Decision Scenarios +" Graphical models based on conditional independence support concise encodings +of the subjective belief of a single agent. A natural question is whether the +consensus belief of a group of agents can be represented with equal parsimony. +We prove, under relatively mild assumptions, that even if everyone agrees on a +common graph topology, no method of combining beliefs can maintain that +structure. Even weaker conditions rule out local aggregation within conditional +probability tables. On a more positive note, we show that if probabilities are +combined with the logarithmic opinion pool (LogOP), then commonly held Markov +independencies are maintained. This suggests a straightforward procedure for +constructing a consensus Markov network. We describe an algorithm for computing +the LogOP with time complexity comparable to that of exact Bayesian inference. +",Graphical Representations of Consensus Belief +" In previous work, we pointed out the limitations of standard Bayesian +networks as a modeling framework for large, complex domains. We proposed a new, +richly structured modeling language, {em Object-oriented Bayesian Netorks}, +that we argued would be able to deal with such domains. However, it turns out +that OOBNs are not expressive enough to model many interesting aspects of +complex domains: the existence of specific named objects, arbitrary relations +between objects, and uncertainty over domain structure. These aspects are +crucial in real-world domains such as battlefield awareness. In this paper, we +present SPOOK, an implemented system that addresses these limitations. SPOOK +implements a more expressive language that allows it to represent the +battlespace domain naturally and compactly. We present a new inference +algorithm that utilizes the model structure in a fundamental way, and show +empirically that it achieves orders of magnitude speedup over existing +approaches. +","SPOOK: A System for Probabilistic Object-Oriented Knowledge + Representation" +" Bayesian Networks (BN) provide robust probabilistic methods of reasoning +under uncertainty, but despite their formal grounds are strictly based on the +notion of conditional dependence, not much attention has been paid so far to +their use in dependability analysis. The aim of this paper is to propose BN as +a suitable tool for dependability analysis, by challenging the formalism with +basic issues arising in dependability tasks. We will discuss how both modeling +and analysis issues can be naturally dealt with by BN. Moreover, we will show +how some limitations intrinsic to combinatorial dependability methods such as +Fault Trees can be overcome using BN. This will be pursued through the study of +a real-world example concerning the reliability analysis of a redundant digital +Programmable Logic Controller (PLC) with majority voting 2:3 +","Bayesian Networks for Dependability Analysis: an Application to Digital + Control Reliability" +" Qualitative probabilistic networks have been introduced as qualitative +abstractions of Bayesian belief networks. One of the major drawbacks of these +qualitative networks is their coarse level of detail, which may lead to +unresolved trade-offs during inference. We present an enhanced formalism for +qualitative networks with a finer level of detail. An enhanced qualitative +probabilistic network differs from a regular qualitative network in that it +distinguishes between strong and weak influences. Enhanced qualitative +probabilistic networks are purely qualitative in nature, as regular qualitative +networks are, yet allow for efficiently resolving trade-offs during inference. +",Enhancing QPNs for Trade-off Resolution +" In this article we propose a qualitative (ordinal) counterpart for the +Partially Observable Markov Decision Processes model (POMDP) in which the +uncertainty, as well as the preferences of the agent, are modeled by +possibility distributions. This qualitative counterpart of the POMDP model +relies on a possibilistic theory of decision under uncertainty, recently +developed. One advantage of such a qualitative framework is its ability to +escape from the classical obstacle of stochastic POMDPs, in which even with a +finite state space, the obtained belief state space of the POMDP is infinite. +Instead, in the possibilistic framework even if exponentially larger than the +state space, the belief state space remains finite. +","A Possibilistic Model for Qualitative Sequential Decision Problems under + Uncertainty in Partially Observable Environments" +" One of the most useful sensitivity analysis techniques of decision analysis +is the computation of value of information (or clairvoyance), the difference in +value obtained by changing the decisions by which some of the uncertainties are +observed. In this paper, some simple but powerful extensions to previous +algorithms are introduced which allow an efficient value of information +calculation on the rooted cluster tree (or strong junction tree) used to solve +the original decision problem. +",Efficient Value of Information Computation +" Hidden Markov models (HMMs) and partially observable Markov decision +processes (POMDPs) form a useful tool for modeling dynamical systems. They are +particularly useful for representing environments such as road networks and +office buildings, which are typical for robot navigation and planning. The work +presented here is concerned with acquiring such models. We demonstrate how +domain-specific information and constraints can be incorporated into the +statistical estimation process, greatly improving the learned models in terms +of the model quality, the number of iterations required for convergence and +robustness to reduction in the amount of available data. We present new +initialization heuristics which can be used even when the data suffers from +cumulative rotational error, new update rules for the model parameters, as an +instance of generalized EM, and a strategy for enforcing complete geometrical +consistency in the model. Experimental results demonstrate the effectiveness of +our approach for both simulated and real robot data, in traditionally +hard-to-learn environments. +",Learning Hidden Markov Models with Geometrical Constraints +" We present examples where the use of belief functions provided sound and +elegant solutions to real life problems. These are essentially characterized by +?missing' information. The examples deal with 1) discriminant analysis using a +learning set where classes are only partially known; 2) an information +retrieval systems handling inter-documents relationships; 3) the combination of +data from sensors competent on partially overlapping frames; 4) the +determination of the number of sources in a multi-sensor environment by +studying the inter-sensors contradiction. The purpose of the paper is to report +on such applications where the use of belief functions provides a convenient +tool to handle ?messy' data problems. +",Practical Uses of Belief Functions +" The noisy-or and its generalization noisy-max have been utilized to reduce +the complexity of knowledge acquisition. In this paper, we present a new +representation of noisy-max that allows for efficient inference in general +Bayesian networks. Empirical studies show that our method is capable of +computing queries in well-known large medical networks, QMR-DT and CPCS, for +which no previous exact inference method has been shown to perform well. +",Multiplicative Factorization of Noisy-Max +" Structure and parameters in a Bayesian network uniquely specify the +probability distribution of the modeled domain. The locality of both structure +and probabilistic information are the great benefits of Bayesian networks and +require the modeler to only specify local information. On the other hand this +locality of information might prevent the modeler - and even more any other +person - from obtaining a general overview of the important relationships +within the domain. The goal of the work presented in this paper is to provide +an ""alternative"" view on the knowledge encoded in a Bayesian network which +might sometimes be very helpful for providing insights into the underlying +domain. The basic idea is to calculate a mixture approximation to the +probability distribution represented by the Bayesian network. The mixture +component densities can be thought of as representing typical scenarios implied +by the Bayesian model, providing intuition about the basic relationships. As an +additional benefit, performing inference in the approximate model is very +simple and intuitive and can provide additional insights. The computational +complexity for the calculation of the mixture approximations criticaly depends +on the measure which defines the distance between the probability distribution +represented by the Bayesian network and the approximate distribution. Both the +KL-divergence and the backward KL-divergence lead to inefficient algorithms. +Incidentally, the latter is used in recent work on mixtures of mean field +solutions to which the work presented here is closely related. We show, +however, that using a mean squared error cost function leads to update +equations which can be solved using the junction tree algorithm. We conclude +that the mean squared error cost function can be used for Bayesian networks in +which inference based on the junction tree is tractable. For large networks, +however, one may have to rely on mean field approximations. +",Mixture Approximations to Bayesian Networks +" In building Bayesian belief networks, the elicitation of all probabilities +required can be a major obstacle. We learned the extent of this often-cited +observation in the construction of the probabilistic part of a complex +influence diagram in the field of cancer treatment. Based upon our negative +experiences with existing methods, we designed a new method for probability +elicitation from domain experts. The method combines various ideas, among which +are the ideas of transcribing probabilities and of using a scale with both +numerical and verbal anchors for marking assessments. In the construction of +the probabilistic part of our influence diagram, the method proved to allow for +the elicitation of many probabilities in little time. +",How to Elicit Many Probabilities +" The AGM theory of belief revision has become an important paradigm for +investigating rational belief changes. Unfortunately, researchers working in +this paradigm have restricted much of their attention to rather simple +representations of belief states, namely logically closed sets of propositional +sentences. In our opinion, this has resulted in a too abstract categorisation +of belief change operations: expansion, revision, or contraction. Occasionally, +in the AGM paradigm, also probabilistic belief changes have been considered, +and it is widely accepted that the probabilistic version of expansion is +conditioning. However, we argue that it may be more correct to view +conditioning and expansion as two essentially different kinds of belief change, +and that what we call constraining is a better candidate for being considered +probabilistic expansion. +","Probabilistic Belief Change: Expansion, Conditioning and Constraining" +" It is well-known that the notion of (strong) conditional independence (CI) is +too restrictive to capture independencies that only hold in certain contexts. +This kind of contextual independency, called context-strong independence (CSI), +can be used to facilitate the acquisition, representation, and inference of +probabilistic knowledge. In this paper, we suggest the use of contextual weak +independence (CWI) in Bayesian networks. It should be emphasized that the +notion of CWI is a more general form of contextual independence than CSI. +Furthermore, if the contextual strong independence holds for all contexts, then +the notion of CSI becomes strong CI. On the other hand, if the weak contextual +independence holds for all contexts, then the notion of CWI becomes weak +independence (WI) nwhich is a more general noncontextual independency than +strong CI. More importantly, complete axiomatizations are studied for both the +class of WI and the class of CI and WI together. Finally, the interesting +property of WI being a necessary and sufficient condition for ensuring +consistency in granular probabilistic networks is shown. +",Contextual Weak Independence in Bayesian Networks +" As Bayesian networks are applied to larger and more complex problem domains, +search for flexible modeling and more efficient inference methods is an ongoing +effort. Multiply sectioned Bayesian networks (MSBNs) extend the HUGIN inference +for Bayesian networks into a coherent framework for flexible modeling and +distributed inference.Lazy propagation extends the Shafer-Shenoy and HUGIN +inference methods with reduced space complexity. We apply the Shafer-Shenoy and +lazy propagation to inference in MSBNs. The combination of the MSBN framework +and lazy propagation provides a better framework for modeling and inference in +very large domains. It retains the modeling flexibility of MSBNs and reduces +the runtime space complexity, allowing exact inference in much larger domains +given the same computational resources. +","Inference in Multiply Sectioned Bayesian Networks with Extended + Shafer-Shenoy and Lazy Propagation" +" Recent interests in dynamic decision modeling have led to the development of +several representation and inference methods. These methods however, have +limited application under time critical conditions where a trade-off between +model quality and computational tractability is essential. This paper presents +an approach to time-critical dynamic decision modeling. A knowledge +representation and modeling method called the time-critical dynamic influence +diagram is proposed. The formalism has two forms. The condensed form is used +for modeling and model abstraction, while the deployed form which can be +converted from the condensed form is used for inference purposes. The proposed +approach has the ability to represent space-temporal abstraction within the +model. A knowledge-based meta-reasoning approach is proposed for the purpose of +selecting the best abstracted model that provide the optimal trade-off between +model quality and model tractability. An outline of the knowledge-based model +construction algorithm is also provided. +",Time-Critical Dynamic Decision Making +" We present a technique for speeding up the convergence of value iteration for +partially observable Markov decisions processes (POMDPs). The underlying idea +is similar to that behind modified policy iteration for fully observable Markov +decision processes (MDPs). The technique can be easily incorporated into any +existing POMDP value iteration algorithms. Experiments have been conducted on +several test problems with one POMDP value iteration algorithm called +incremental pruning. We find that the technique can make incremental pruning +run several orders of magnitude faster. +","A Method for Speeding Up Value Iteration in Partially Observable Markov + Decision Processes" +" The notion of rough set captures indiscernibility of elements in a set. But, +in many real life situations, an information system establishes the relation +between different universes. This gave the extension of rough set on single +universal set to rough set on two universal sets. In this paper, we introduce +approximation of classifications and measures of uncertainty basing upon rough +set on two universal sets employing the knowledge due to binary relations. +","Approximation of Classification and Measures of Uncertainty in Rough Set + on Two Universal Sets" +" Possibilistic logic is a well-known graded logic of uncertainty suitable to +reason under incomplete information and partially inconsistent knowledge, which +is built upon classical first order logic. There exists for Possibilistic logic +a proof procedure based on a refutation complete resolution-style calculus. +Recently, a syntactical extension of first order Possibilistic logic (called +PLFC) dealing with fuzzy constants and fuzzily restricted quantifiers has been +proposed. Our aim is to present steps towards both the formalization of PLFC +itself and an automated deduction system for it by (i) providing a formal +semantics; (ii) defining a sound resolution-style calculus by refutation; and +(iii) describing a first-order proof procedure for PLFC clauses based on (ii) +and on a novel notion of most general substitution of two literals in a +resolution step. In contrast to standard Possibilistic logic semantics, +truth-evaluation of formulas with fuzzy constants are many-valued instead of +boolean, and consequently an extended notion of possibilistic uncertainty is +also needed. +","On the Semantics and Automated Deduction for PLFC, a Logic of + Possibilistic Uncertainty and Fuzziness" +" Argumentation is a promising model for reasoning with uncertain knowledge. +The key concept of acceptability enables to differentiate arguments and +counterarguments: The certainty of a proposition can then be evaluated through +the most acceptable arguments for that proposition. In this paper, we +investigate different complementary points of view: - an acceptability based on +the existence of direct counterarguments, - an acceptability based on the +existence of defenders. Pursuing previous work on preference-based +argumentation principles, we enforce both points of view by taking into account +preference orderings for comparing arguments. Our approach is illustrated in +the context of reasoning with stratified knowldge bases. +",On the Acceptability of Arguments in Preference-Based Argumentation +" This paper addresses the problem of merging uncertain information in the +framework of possibilistic logic. It presents several syntactic combination +rules to merge possibilistic knowledge bases, provided by different sources, +into a new possibilistic knowledge base. These combination rules are first +described at the meta-level outside the language of possibilistic logic. Next, +an extension of possibilistic logic, where the combination rules are inside the +language, is proposed. A proof system in a sequent form, which is sound and +complete with respect to the possibilistic logic semantics, is given. +",Merging Uncertain Knowledge Bases in a Possibilistic Logic Framework +" There exist two general forms of exact algorithms for updating probabilities +in Bayesian Networks. The first approach involves using a structure, usually a +clique tree, and performing local message based calculation to extract the +belief in each variable. The second general class of algorithm involves the use +of non-serial dynamic programming techniques to extract the belief in some +desired group of variables. In this paper we present a hybrid algorithm based +on the latter approach yet possessing the ability to retrieve the belief in all +single variables. The technique is advantageous in that it saves a NP-hard +computation step over using one algorithm of each type. Furthermore, this +technique re-enforces a conjecture of Jensen and Jensen [JJ94] in that it still +requires a single NP-hard step to set up the structure on which inference is +performed, as we show by confirming Li and D'Ambrosio's [LD94] conjectured +NP-hardness of OFP. +","A Hybrid Algorithm to Compute Marginal and Joint Beliefs in Bayesian + Networks and Its Complexity" +" Recent research in decision theoretic planning has focussed on making the +solution of Markov decision processes (MDPs) more feasible. We develop a family +of algorithms for structured reachability analysis of MDPs that are suitable +when an initial state (or set of states) is known. Using compact, structured +representations of MDPs (e.g., Bayesian networks), our methods, which vary in +the tradeoff between complexity and accuracy, produce structured descriptions +of (estimated) reachable states that can be used to eliminate variables or +variable values from the problem description, reducing the size of the MDP and +making it easier to solve. One contribution of our work is the extension of +ideas from GRAPHPLAN to deal with the distributed nature of action +representations typically embodied within Bayes nets and the problem of +correlated action effects. We also demonstrate that our algorithm can be made +more complete by using k-ary constraints instead of binary constraints. Another +contribution is the illustration of how the compact representation of +reachability constraints can be exploited by several existing (exact and +approximate) abstraction algorithms for MDPs. +",Structured Reachability Analysis for Markov Decision Processes +" The monitoring and control of any dynamic system depends crucially on the +ability to reason about its current status and its future trajectory. In the +case of a stochastic system, these tasks typically involve the use of a belief +state- a probability distribution over the state of the process at a given +point in time. Unfortunately, the state spaces of complex processes are very +large, making an explicit representation of a belief state intractable. Even in +dynamic Bayesian networks (DBNs), where the process itself can be represented +compactly, the representation of the belief state is intractable. We +investigate the idea of maintaining a compact approximation to the true belief +state, and analyze the conditions under which the errors due to the +approximations taken over the lifetime of the process do not accumulate to make +our answers completely irrelevant. We show that the error in a belief state +contracts exponentially as the process evolves. Thus, even with multiple +approximations, the error in our process remains bounded indefinitely. We show +how the additional structure of a DBN can be used to design our approximation +scheme, improving its performance significantly. We demonstrate the +applicability of our ideas in the context of a monitoring task, showing that +orders of magnitude faster inference can be achieved with only a small +degradation in accuracy. +",Tractable Inference for Complex Stochastic Processes +" The situation assessment problem is considered, in terms of object, +condition, activity, and plan recognition, based on data coming from the +real-word {em via} various sensors. It is shown that uncertainty issues are +linked both to the models and to the matching algorithm. Three different types +of uncertainties are identified, and within each one, the numerical and the +symbolic cases are distinguished. The emphasis is then put on purely symbolic +uncertainties: it is shown that they can be dealt with within a purely symbolic +framework resulting from a transposition of classical numerical estimation +tools. +","Dealing with Uncertainty in Situation Assessment: towards a Symbolic + Approach" +" Given an undirected graph G or hypergraph X model for a given set of +variables V, we introduce two marginalization operators for obtaining the +undirected graph GA or hypergraph HA associated with a given subset A c V such +that the marginal distribution of A factorizes according to GA or HA, +respectively. Finally, we illustrate the method by its application to some +practical examples. With them we show that hypergraph models allow defining a +finer factorization or performing a more precise conditional independence +analysis than undirected graph models. +",Marginalizing in Undirected Graph and Hypergraph Models +" We investigate the application of classification techniques to utility +elicitation. In a decision problem, two sets of parameters must generally be +elicited: the probabilities and the utilities. While the prior and conditional +probabilities in the model do not change from user to user, the utility models +do. Thus it is necessary to elicit a utility model separately for each new +user. Elicitation is long and tedious, particularly if the outcome space is +large and not decomposable. There are two common approaches to utility function +elicitation. The first is to base the determination of the users utility +function solely ON elicitation OF qualitative preferences.The second makes +assumptions about the form AND decomposability OF the utility function.Here we +take a different approach: we attempt TO identify the new USERs utility +function based on classification relative to a database of previously collected +utility functions. We do this by identifying clusters of utility functions that +minimize an appropriate distance measure. Having identified the clusters, we +develop a classification scheme that requires many fewer and simpler +assessments than full utility elicitation and is more robust than utility +elicitation based solely on preferences. We have tested our algorithm on a +small database of utility functions in a prenatal diagnosis domain and the +results are quite promising. +",Utility Elicitation as a Classification Problem +" This paper analyzes irrelevance and independence relations in graphical +models associated with convex sets of probability distributions (called +Quasi-Bayesian networks). The basic question in Quasi-Bayesian networks is, How +can irrelevance/independence relations in Quasi-Bayesian networks be detected, +enforced and exploited? This paper addresses these questions through Walley's +definitions of irrelevance and independence. Novel algorithms and results are +presented for inferences with the so-called natural extensions using fractional +linear programming, and the properties of the so-called type-1 extensions are +clarified through a new generalization of d-separation. +",Irrelevance and Independence Relations in Quasi-Bayesian Networks +" It is well known that one can ignore parts of a belief network when computing +answers to certain probabilistic queries. It is also well known that the +ignorable parts (if any) depend on the specific query of interest and, +therefore, may change as the query changes. Algorithms based on jointrees, +however, do not seem to take computational advantage of these facts given that +they typically construct jointrees for worst-case queries; that is, queries for +which every part of the belief network is considered relevant. To address this +limitation, we propose in this paper a method for reconfiguring jointrees +dynamically as the query changes. The reconfiguration process aims at +maintaining a jointree which corresponds to the underlying belief network after +it has been pruned given the current query. Our reconfiguration method is +marked by three characteristics: (a) it is based on a non-classical definition +of jointrees; (b) it is relatively efficient; and (c) it can reuse some of the +computations performed before a jointree is reconfigured. We present +preliminary experimental results which demonstrate significant savings over +using static jointrees when query changes are considerable. +",Dynamic Jointrees +" The variability of structure in a finite Markov equivalence class of causally +sufficient models represented by directed acyclic graphs has been fully +characterized. Without causal sufficiency, an infinite semi-Markov equivalence +class of models has only been characterized by the fact that each model in the +equivalence class entails the same marginal statistical dependencies. In this +paper, we study the variability of structure of causal models within a +semi-Markov equivalence class and propose a systematic approach to construct +models entailing any specific marginal statistical dependencies. +",On the Semi-Markov Equivalence of Causal Models +" This paper relates comparative belief structures and a general view of belief +management in the setting of deductively closed logical representations of +accepted beliefs. We show that the range of compatibility between the classical +deductive closure and uncertain reasoning covers precisely the nonmonotonic +'preferential' inference system of Kraus, Lehmann and Magidor and nothing else. +In terms of uncertain reasoning any possibility or necessity measure gives +birth to a structure of accepted beliefs. The classes of probability functions +and of Shafer's belief functions which yield belief sets prove to be very +special ones. +","Comparative Uncertainty, Belief Functions and Accepted Beliefs" +" This paper presents an axiomatic framework for qualitative decision under +uncertainty in a finite setting. The corresponding utility is expressed by a +sup-min expression, called Sugeno (or fuzzy) integral. Technically speaking, +Sugeno integral is a median, which is indeed a qualitative counterpart to the +averaging operation underlying expected utility. The axiomatic justification of +Sugeno integral-based utility is expressed in terms of preference between acts +as in Savage decision theory. Pessimistic and optimistic qualitative utilities, +based on necessity and possibility measures, previously introduced by two of +the authors, can be retrieved in this setting by adding appropriate axioms. +",Qualitative Decision Theory with Sugeno Integrals +" While decision theory provides an appealing normative framework for +representing rich preference structures, eliciting utility or value functions +typically incurs a large cost. For many applications involving interactive +systems this overhead precludes the use of formal decision-theoretic models of +preference. Instead of performing elicitation in a vacuum, it would be useful +if we could augment directly elicited preferences with some appropriate default +information. In this paper we propose a case-based approach to alleviating the +preference elicitation bottleneck. Assuming the existence of a population of +users from whom we have elicited complete or incomplete preference structures, +we propose eliciting the preferences of a new user interactively and +incrementally, using the closest existing preference structures as potential +defaults. Since a notion of closeness demands a measure of distance among +preference structures, this paper takes the first step of studying various +distance measures over fully and partially specified preference structures. We +explore the use of Euclidean distance, Spearmans footrule, and define a new +measure, the probabilistic distance. We provide computational techniques for +all three measures. +","Towards Case-Based Preference Elicitation: Similarity Measures on + Preference Structures" +" Most algorithms for solving POMDPs iteratively improve a value function that +implicitly represents a policy and are said to search in value function space. +This paper presents an approach to solving POMDPs that represents a policy +explicitly as a finite-state controller and iteratively improves the controller +by search in policy space. Two related algorithms illustrate this approach. The +first is a policy iteration algorithm that can outperform value iteration in +solving infinitehorizon POMDPs. It provides the foundation for a new heuristic +search algorithm that promises further speedup by focusing computational effort +on regions of the problem space that are reachable, or likely to be reached, +from a start state. +",Solving POMDPs by Searching in Policy Space +" We investigate the use of temporally abstract actions, or macro-actions, in +the solution of Markov decision processes. Unlike current models that combine +both primitive actions and macro-actions and leave the state space unchanged, +we propose a hierarchical model (using an abstract MDP) that works with +macro-actions only, and that significantly reduces the size of the state space. +This is achieved by treating macroactions as local policies that act in certain +regions of state space, and by restricting states in the abstract MDP to those +at the boundaries of regions. The abstract MDP approximates the original and +can be solved more efficiently. We discuss several ways in which macro-actions +can be generated to ensure good solution quality. Finally, we consider ways in +which macro-actions can be reused to solve multiple, related MDPs; and we show +that this can justify the computational overhead of macro-action generation. +",Hierarchical Solution of Markov Decision Processes using Macro-actions +" Stochastic search algorithms are among the most sucessful approaches for +solving hard combinatorial problems. A large class of stochastic search +approaches can be cast into the framework of Las Vegas Algorithms (LVAs). As +the run-time behavior of LVAs is characterized by random variables, the +detailed knowledge of run-time distributions provides important information for +the analysis of these algorithms. In this paper we propose a novel methodology +for evaluating the performance of LVAs, based on the identification of +empirical run-time distributions. We exemplify our approach by applying it to +Stochastic Local Search (SLS) algorithms for the satisfiability problem (SAT) +in propositional logic. We point out pitfalls arising from the use of improper +empirical methods and discuss the benefits of the proposed methodology for +evaluating and comparing LVAs. +",Evaluating Las Vegas Algorithms - Pitfalls and Remedies +" We present an anytime algorithm which computes policies for decision problems +represented as multi-stage influence diagrams. Our algorithm constructs +policies incrementally, starting from a policy which makes no use of the +available information. The incremental process constructs policies which +includes more of the information available to the decision maker at each step. +While the process converges to the optimal policy, our approach is designed for +situations in which computing the optimal policy is infeasible. We provide +examples of the process on several large decision problems, showing that, for +these examples, the process constructs valuable (but sub-optimal) policies +before the optimal policy would be available by traditional methods. +",An Anytime Algorithm for Decision Making under Uncertainty +" For many real time applications, it is important to validate the information +received from the sensors before entering higher levels of reasoning. This +paper presents an any time probabilistic algorithm for validating the +information provided by sensors. The system consists of two Bayesian network +models. The first one is a model of the dependencies between sensors and it is +used to validate each sensor. It provides a list of potentially faulty sensors. +To isolate the real faults, a second Bayesian network is used, which relates +the potential faults with the real faults. This second model is also used to +make the validation algorithm any time, by validating first the sensors that +provide more information. To select the next sensor to validate, and measure +the quality of the results at each stage, an entropy function is used. This +function captures in a single quantity both the certainty and specificity +measures of any time algorithms. Together, both models constitute a mechanism +for validating sensors in an any time fashion, providing at each step the +probability of correct/faulty for each sensor, and the total quality of the +results. The algorithm has been tested in the validation of temperature sensors +of a power plant. +",Any Time Probabilistic Reasoning for Sensor Validation +" We take another look at the general problem of selecting a preferred +probability measure among those that comply with some given constraints. The +dominant role that entropy maximization has obtained in this context is +questioned by arguing that the minimum information principle on which it is +based could be supplanted by an at least as plausible ""likelihood of evidence"" +principle. We then review a method for turning given selection functions into +representation independent variants, and discuss the tradeoffs involved in this +transformation. +","Measure Selection: Notions of Rationality and Representation + Independence" +" In the literature on graphical models, there has been increased attention +paid to the problems of learning hidden structure (see Heckerman [H96] for +survey) and causal mechanisms from sample data [H96, P88, S93, P95, F98]. In +most settings we should expect the former to be difficult, and the latter +potentially impossible without experimental intervention. In this work, we +examine some restricted settings in which perfectly reconstruct the hidden +structure solely on the basis of observed sample data. +","Exact Inference of Hidden Structure from Sample Data in Noisy-OR + Networks" +" In the last decade, several architectures have been proposed for exact +computation of marginals using local computation. In this paper, we compare +three architectures - Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer - from +the perspective of graphical structure for message propagation, message-passing +scheme, computational efficiency, and storage efficiency. +","A Comparison of Lauritzen-Spiegelhalter, Hugin, and Shenoy-Shafer + Architectures for Computing Marginals of Probability Distributions" +" Qualitative probabilistic reasoning in a Bayesian network often reveals +tradeoffs: relationships that are ambiguous due to competing qualitative +influences. We present two techniques that combine qualitative and numeric +probabilistic reasoning to resolve such tradeoffs, inferring the qualitative +relationship between nodes in a Bayesian network. The first approach +incrementally marginalizes nodes that contribute to the ambiguous qualitative +relationships. The second approach evaluates approximate Bayesian networks for +bounds of probability distributions, and uses these bounds to determinate +qualitative relationships in question. This approach is also incremental in +that the algorithm refines the state spaces of random variables for tighter +bounds until the qualitative relationships are resolved. Both approaches +provide systematic methods for tradeoff resolution at potentially lower +computational cost than application of purely numeric methods. +",Incremental Tradeoff Resolution in Qualitative Probabilistic Networks +" We exploit qualitative probabilistic relationships among variables for +computing bounds of conditional probability distributions of interest in +Bayesian networks. Using the signs of qualitative relationships, we can +implement abstraction operations that are guaranteed to bound the distributions +of interest in the desired direction. By evaluating incrementally improved +approximate networks, our algorithm obtains monotonically tightening bounds +that converge to exact distributions. For supermodular utility functions, the +tightening bounds monotonically reduce the set of admissible decision +alternatives as well. +",Using Qualitative Relationships for Bounding Probability Distributions +" We present locally complete inference rules for probabilistic deduction from +taxonomic and probabilistic knowledge-bases over conjunctive events. Crucially, +in contrast to similar inference rules in the literature, our inference rules +are locally complete for conjunctive events and under additional taxonomic +knowledge. We discover that our inference rules are extremely complex and that +it is at first glance not clear at all where the deduced tightest bounds come +from. Moreover, analyzing the global completeness of our inference rules, we +find examples of globally very incomplete probabilistic deductions. More +generally, we even show that all systems of inference rules for taxonomic and +probabilistic knowledge-bases over conjunctive events are globally incomplete. +We conclude that probabilistic deduction by the iterative application of +inference rules on interval restrictions for conditional probabilities, even +though considered very promising in the literature so far, seems very limited +in its field of application. +","Magic Inference Rules for Probabilistic Deduction under Taxonomic + Knowledge" +" The efficiency of algorithms using secondary structures for probabilistic +inference in Bayesian networks can be improved by exploiting independence +relations induced by evidence and the direction of the links in the original +network. In this paper we present an algorithm that on-line exploits +independence relations induced by evidence and the direction of the links in +the original network to reduce both time and space costs. Instead of +multiplying the conditional probability distributions for the various cliques, +we determine on-line which potentials to multiply when a message is to be +produced. The performance improvement of the algorithm is emphasized through +empirical evaluations involving large real world Bayesian networks, and we +compare the method with the HUGIN and Shafer-Shenoy inference algorithms. +",Lazy Propagation in Junction Trees +" This paper describes a process for constructing situation-specific belief +networks from a knowledge base of network fragments. A situation-specific +network is a minimal query complete network constructed from a knowledge base +in response to a query for the probability distribution on a set of target +variables given evidence and context variables. We present definitions of query +completeness and situation-specific networks. We describe conditions on the +knowledge base that guarantee query completeness. The relationship of our work +to earlier work on KBMC is also discussed. +",Constructing Situation Specific Belief Networks +" Several authors have explained that the likelihood ratio measures the +strength of the evidence represented by observations in statistical problems. +This idea works fine when the goal is to evaluate the strength of the available +evidence for a simple hypothesis versus another simple hypothesis. However, the +applicability of this idea is limited to simple hypotheses because the +likelihood function is primarily defined on points (simple hypotheses) of the +parameter space. In this paper we define a general weight of evidence that is +applicable to both simple and composite hypotheses. It is based on the +Dempster-Shafer concept of plausibility and is shown to be a generalization of +the likelihood ratio. Functional models are of a fundamental importance for the +general weight of evidence proposed in this paper. The relevant concepts and +ideas are explained by means of a familiar urn problem and the general analysis +of a real-world medical problem is presented. +",From Likelihood to Plausibility +" Distributed knowledge based applications in open domain rely on common sense +information which is bound to be uncertain and incomplete. To draw the useful +conclusions from ambiguous data, one must address uncertainties and conflicts +incurred in a holistic view. No integrated frameworks are viable without an +in-depth analysis of conflicts incurred by uncertainties. In this paper, we +give such an analysis and based on the result, propose an integrated framework. +Our framework extends definite argumentation theory to model uncertainty. It +supports three views over conflicting and uncertain knowledge. Thus, knowledge +engineers can draw different conclusions depending on the application context +(i.e. view). We also give an illustrative example on strategical decision +support to show the practical usefulness of our framework. +",Resolving Conflicting Arguments under Uncertainties +" This paper presents two new approaches to decomposing and solving large +Markov decision problems (MDPs), a partial decoupling method and a complete +decoupling method. In these approaches, a large, stochastic decision problem is +divided into smaller pieces. The first approach builds a cache of policies for +each part of the problem independently, and then combines the pieces in a +separate, light-weight step. A second approach also divides the problem into +smaller pieces, but information is communicated between the different problem +pieces, allowing intelligent decisions to be made about which piece requires +the most attention. Both approaches can be used to find optimal policies or +approximately optimal policies with provable bounds. These algorithms also +provide a framework for the efficient transfer of knowledge across problems +that share similar structure. +","Flexible Decomposition Algorithms for Weakly Coupled Markov Decision + Problems" +" I present a parallel algorithm for exact probabilistic inference in Bayesian +networks. For polytree networks with n variables, the worst-case time +complexity is O(log n) on a CREW PRAM (concurrent-read, exclusive-write +parallel random-access machine) with n processors, for any constant number of +evidence variables. For arbitrary networks, the time complexity is O(r^{3w}*log +n) for n processors, or O(w*log n) for r^{3w}*n processors, where r is the +maximum range of any variable, and w is the induced width (the maximum clique +size), after moralizing and triangulating the network. +",Logarithmic Time Parallel Bayesian Inference +" The process of diagnosis involves learning about the state of a system from +various observations of symptoms or findings about the system. Sophisticated +Bayesian (and other) algorithms have been developed to revise and maintain +beliefs about the system as observations are made. Nonetheless, diagnostic +models have tended to ignore some common sense reasoning exploited by human +diagnosticians; In particular, one can learn from which observations have not +been made, in the spirit of conversational implicature. There are two concepts +that we describe to extract information from the observations not made. First, +some symptoms, if present, are more likely to be reported before others. +Second, most human diagnosticians and expert systems are economical in their +data-gathering, searching first where they are more likely to find symptoms +present. Thus, there is a desirable bias toward reporting symptoms that are +present. We develop a simple model for these concepts that can significantly +improve diagnostic inference. +",Learning From What You Don't Observe +" There is evidence that the numbers in probabilistic inference don't really +matter. This paper considers the idea that we can make a probabilistic model +simpler by making fewer distinctions. Unfortunately, the level of a Bayesian +network seems too coarse; it is unlikely that a parent will make little +difference for all values of the other parents. In this paper we consider an +approximation scheme where distinctions can be ignored in some contexts, but +not in other contexts. We elaborate on a notion of a parent context that allows +a structured context-specific decomposition of a probability distribution and +the associated probabilistic inference scheme called probabilistic partial +evaluation (Poole 1997). This paper shows a way to simplify a probabilistic +model by ignoring distinctions which have similar probabilities, a method to +exploit the simpler model, a bound on the resulting errors, and some +preliminary empirical results on simple networks. +",Context-Specific Approximation in Probabilistic Inference +" It was recently shown that the problem of decoding messages transmitted +through a noisy channel can be formulated as a belief updating task over a +probabilistic network [McEliece]. Moreover, it was observed that iterative +application of the (linear time) Pearl's belief propagation algorithm designed +for polytrees outperformed state of the art decoding algorithms, even though +the corresponding networks may have many cycles. This paper demonstrates +empirically that an approximation algorithm approx-mpe for solving the most +probable explanation (MPE) problem, developed within the recently proposed +mini-bucket elimination framework [Dechter96], outperforms iterative belief +propagation on classes of coding networks that have bounded induced width. Our +experiments suggest that approximate MPE decoders can be good competitors to +the approximate belief updating decoders. +","Empirical Evaluation of Approximation Algorithms for Probabilistic + Decoding" +" This paper describes a decision theoretic formulation of learning the +graphical structure of a Bayesian Belief Network from data. This framework +subsumes the standard Bayesian approach of choosing the model with the largest +posterior probability as the solution of a decision problem with a 0-1 loss +function and allows the use of more general loss functions able to trade-off +the complexity of the selected model and the error of choosing an +oversimplified model. A new class of loss functions, called disintegrable, is +introduced, to allow the decision problem to match the decomposability of the +graphical model. With this class of loss functions, the optimal solution to the +decision problem can be found using an efficient bottom-up search strategy. +",Decision Theoretic Foundations of Graphical Model Selection +" One of the benefits of belief networks and influence diagrams is that so much +knowledge is captured in the graphical structure. In particular, statements of +conditional irrelevance (or independence) can be verified in time linear in the +size of the graph. To resolve a particular inference query or decision problem, +only some of the possible states and probability distributions must be +specified, the ""requisite information."" + This paper presents a new, simple, and efficient ""Bayes-ball"" algorithm which +is well-suited to both new students of belief networks and state of the art +implementations. The Bayes-ball algorithm determines irrelevant sets and +requisite information more efficiently than existing methods, and is linear in +the size of the graph for belief networks and influence diagrams. +","Bayes-Ball: The Rational Pastime (for Determining Irrelevance and + Requisite Information in Belief Networks and Influence Diagrams)" +" AThe paper gives a few arguments in favour of the use of chain graphs for +description of probabilistic conditional independence structures. Every +Bayesian network model can be equivalently introduced by means of a +factorization formula with respect to a chain graph which is Markov equivalent +to the Bayesian network. A graphical characterization of such graphs is given. +The class of equivalent graphs can be represented by a distinguished graph +which is called the largest chain graph. The factorization formula with respect +to the largest chain graph is a basis of a proposal of how to represent the +corresponding (discrete) probability distribution in a computer (i.e. +parametrize it). This way does not depend on the choice of a particular +Bayesian network from the class of equivalent networks and seems to be the most +efficient way from the point of view of memory demands. A separation criterion +for reading independency statements from a chain graph is formulated in a +simpler way. It resembles the well-known d-separation criterion for Bayesian +networks and can be implemented locally. +",Bayesian Networks from the Point of View of Chain Graphs +" This paper is about reducing influence diagram (ID) evaluation into Bayesian +network (BN) inference problems. Such reduction is interesting because it +enables one to readily use one's favorite BN inference algorithm to efficiently +evaluate IDs. Two such reduction methods have been proposed previously (Cooper +1988, Shachter and Peot 1992). This paper proposes a new method. The BN +inference problems induced by the mew method are much easier to solve than +those induced by the two previous methods. +",Probabilistic Inference in Influence Diagrams +" There is much interest in using partially observable Markov decision +processes (POMDPs) as a formal model for planning in stochastic domains. This +paper is concerned with finding optimal policies for POMDPs. We propose several +improvements to incremental pruning, presently the most efficient exact +algorithm for solving POMDPs. +","Planning with Partially Observable Markov Decision Processes: Advances + in Exact Solution Method" +" In the real world, insufficient information, limited computation resources, +and complex problem structures often force an autonomous agent to make a +decision in time less than that required to solve the problem at hand +completely. Flexible and approximate computations are two approaches to +decision making under limited computation resources. Flexible computation helps +an agent to flexibly allocate limited computation resources so that the overall +system utility is maximized. Approximate computation enables an agent to find +the best satisfactory solution within a deadline. In this paper, we present two +state-space reduction methods for flexible and approximate computation: +quantitative reduction to deal with inaccurate heuristic information, and +structural reduction to handle complex problem structures. These two methods +can be applied successively to continuously improve solution quality if more +computation is available. Our results show that these reduction methods are +effective and efficient, finding better solutions with less computation than +some existing well-known methods. +",Flexible and Approximate Computation through State-Space Reduction +" Two different definitions of the Artificial Intelligence concept have been +proposed in papers [1] and [2]. The first definition is informal. It says that +any program that is cleverer than a human being, is acknowledged as Artificial +Intelligence. The second definition is formal because it avoids reference to +the concept of human being. The readers of papers [1] and [2] might be left +with the impression that both definitions are equivalent and the definition in +[2] is simply a formal version of that in [1]. This paper will compare both +definitions of Artificial Intelligence and, hopefully, will bring a better +understanding of the concept. +",Comparison between the two definitions of AI +" Class algebra provides a natural framework for sharing of ISA hierarchies +between users that may be unaware of each other's definitions. This permits +data from relational databases, object-oriented databases, and tagged XML +documents to be unioned into one distributed ontology, sharable by all users +without the need for prior negotiation or the development of a ""standard"" +ontology for each field. Moreover, class algebra produces a functional +correspondence between a class's class algebraic definition (i.e. its ""intent"") +and the set of all instances which satisfy the expression (i.e. its ""extent""). +The framework thus provides assistance in quickly locating examples and +counterexamples of various definitions. This kind of information is very +valuable when developing models of the real world, and serves as an invaluable +tool assisting in the proof of theorems concerning these class algebra +expressions. Finally, the relative frequencies of objects in the ISA hierarchy +can produce a useful Boolean algebra of probabilities. The probabilities can be +used by traditional information-theoretic classification methodologies to +obtain optimal ways of classifying objects in the database. +",Class Algebra for Ontology Reasoning +" We give an effective procedure that produces a natural number in its output +from any natural number in its input, that is, it computes a total function. +The elementary operations of the procedure are Turing-computable. The procedure +has a second input which can contain the Goedel number of any Turing-computable +total function whose range is a subset of the set of the Goedel numbers of all +Turing-computable total functions. We prove that the second input cannot be set +to the Goedel number of any Turing-computable function that computes the output +from any natural number in its first input. In this sense, there is no Turing +program that computes the output from its first input. The procedure is used to +define creative procedures which compute functions that are not +Turing-computable. We argue that creative procedures model an aspect of +reasoning that cannot be modeled by Turing machines. +","An Effective Procedure for Computing ""Uncomputable"" Functions" +" The paper considers the class of information systems capable of solving +heuristic problems on basis of formal theory that was termed modal and vector +theory of formal intelligent systems (FIS). The paper justifies the +construction of FIS resolution algorithm, defines the main features of these +systems and proves theorems that underlie the theory. The principle of +representation diversity of FIS construction is formulated. The paper deals +with the main principles of constructing and functioning formal intelligent +system (FIS) on basis of FIS modal and vector theory. The following phenomena +are considered: modular architecture of FIS presentation sub-system, algorithms +of data processing at every step of the stage of creating presentations. +Besides the paper suggests the structure of neural elements, i.e. zone +detectors and processors that are the basis for FIS construction. +",Principles of modal and vector theory of formal intelligence systems +" Wide-angle sonar mapping of the environment by mobile robot is nontrivial due +to several sources of uncertainty: dropouts due to ""specular"" reflections, +obstacle location uncertainty due to the wide beam, and distance measurement +error. Earlier papers address the latter problems, but dropouts remain a +problem in many environments. We present an approach that lifts the +overoptimistic independence assumption used in earlier work, and use Bayes nets +to represent the dependencies between objects of the model. Objects of the +model consist of readings, and of regions in which ""quasi location invariance"" +of the (possible) obstacles exists, with respect to the readings. Simulation +supports the method's feasibility. The model is readily extensible to allow for +prior distributions, as well as other types of sensing operations. +",Bayes Networks for Sonar Sensor Fusion +" A modelling language is described which is suitable for the correlation of +information when the underlying functional model of the system is incomplete or +uncertain and the temporal dependencies are imprecise. An efficient and +incremental implementation is outlined which depends on cost functions +satisfying certain criteria. Possibilistic logic and probability theory (as it +is used in the applications targetted) satisfy these criteria. +",Exploiting Uncertain and Temporal Information in Correlation +" Much recent research in decision theoretic planning has adopted Markov +decision processes (MDPs) as the model of choice, and has attempted to make +their solution more tractable by exploiting problem structure. One particular +algorithm, structured policy construction achieves this by means of a decision +theoretic analog of goal regression using action descriptions based on Bayesian +networks with tree-structured conditional probability tables. The algorithm as +presented is not able to deal with actions with correlated effects. We describe +a new decision theoretic regression operator that corrects this weakness. While +conceptually straightforward, this extension requires a somewhat more +complicated technical approach. +",Correlated Action Effects in Decision Theoretic Regression +" Performance prediction or forecasting sporting outcomes involves a great deal +of insight into the particular area one is dealing with, and a considerable +amount of intuition about the factors that bear on such outcomes and +performances. The mathematical Theory of Evidence offers representation +formalisms which grant experts a high degree of freedom when expressing their +subjective beliefs in the context of decision-making situations like +performance prediction. Furthermore, this reasoning framework incorporates a +powerful mechanism to systematically pool the decisions made by individual +subject matter experts. The idea behind such a combination of knowledge is to +improve the competence (quality) of the overall decision-making process. This +paper reports on a performance prediction experiment carried out during the +European Football Championship in 1996. Relying on the knowledge of four +predictors, Evidence Theory was used to forecast the final scores of all 31 +matches. The results of this empirical study are very encouraging. +",Corporate Evidential Decision Making in Performance Prediction Domains +" Decomposable dependency models and their graphical counterparts, i.e., +chordal graphs, possess a number of interesting and useful properties. On the +basis of two characterizations of decomposable models in terms of independence +relationships, we develop an exact algorithm for recovering the chordal +graphical representation of any given decomposable model. We also propose an +algorithm for learning chordal approximations of dependency models isomorphic +to general undirected graphs. +",Algorithms for Learning Decomposable Models and Chordal Graphs +" Most exact algorithms for general partially observable Markov decision +processes (POMDPs) use a form of dynamic programming in which a +piecewise-linear and convex representation of one value function is transformed +into another. We examine variations of the ""incremental pruning"" method for +solving this problem and compare them to earlier algorithms from theoretical +and empirical perspectives. We find that incremental pruning is presently the +most efficient exact method for solving POMDPs. +","Incremental Pruning: A Simple, Fast, Exact Method for Partially + Observable Markov Decision Processes" +" As probabilistic systems gain popularity and are coming into wider use, the +need for a mechanism that explains the system's findings and recommendations +becomes more critical. The system will also need a mechanism for ordering +competing explanations. We examine two representative approaches to explanation +in the literature - one due to G\""ardenfors and one due to Pearl - and show +that both suffer from significant problems. We propose an approach to defining +a notion of ""better explanation"" that combines some of the features of both +together with more recent work by Pearl and others on causality. +",Defining Explanation in Probabilistic Systems +" We present an algorithm for arc reversal in Bayesian networks with +tree-structured conditional probability tables, and consider some of its +advantages, especially for the simulation of dynamic probabilistic networks. In +particular, the method allows one to produce CPTs for nodes involved in the +reversal that exploit regularities in the conditional distributions. We argue +that this approach alleviates some of the overhead associated with arc +reversal, plays an important role in evidence integration and can be used to +restrict sampling of variables in DPNs. We also provide an algorithm that +detects the dynamic irrelevance of state variables in forward simulation. This +algorithm exploits the structured CPTs in a reversed network to determine, in a +time-independent fashion, the conditions under which a variable does or does +not need to be sampled. +",Structured Arc Reversal and Simulation of Dynamic Probabilistic Networks +" Robust Bayesian inference is the calculation of posterior probability bounds +given perturbations in a probabilistic model. This paper focuses on +perturbations that can be expressed locally in Bayesian networks through convex +sets of distributions. Two approaches for combination of local models are +considered. The first approach takes the largest set of joint distributions +that is compatible with the local sets of distributions; we show how to reduce +this type of robust inference to a linear programming problem. The second +approach takes the convex hull of joint distributions generated from the local +sets of distributions; we demonstrate how to apply interior-point optimization +methods to generate posterior bounds and how to generate approximations that +are guaranteed to converge to correct posterior bounds. We also discuss +calculation of bounds for expected utilities and variances, and global +perturbation models. +","Robustness Analysis of Bayesian Networks with Local Convex Sets of + Distributions" +" This paper proposes a novel, algorithm-independent approach to optimizing +belief network inference. rather than designing optimizations on an algorithm +by algorithm basis, we argue that one should use an unoptimized algorithm to +generate a Q-DAG, a compiled graphical representation of the belief network, +and then optimize the Q-DAG and its evaluator instead. We present a set of +Q-DAG optimizations that supplant optimizations designed for traditional +inference algorithms, including zero compression, network pruning and caching. +We show that our Q-DAG optimizations require time linear in the Q-DAG size, and +significantly simplify the process of designing algorithms for optimizing +belief network inference. +","A Standard Approach for Optimizing Belief Network Inference using Query + DAGs" +" We present a method for solving implicit (factored) Markov decision processes +(MDPs) with very large state spaces. We introduce a property of state space +partitions which we call epsilon-homogeneity. Intuitively, an +epsilon-homogeneous partition groups together states that behave approximately +the same under all or some subset of policies. Borrowing from recent work on +model minimization in computer-aided software verification, we present an +algorithm that takes a factored representation of an MDP and an 0<=epsilon<=1 +and computes a factored epsilon-homogeneous partition of the state space. This +partition defines a family of related MDPs - those MDPs with state space equal +to the blocks of the partition, and transition probabilities ""approximately"" +like those of any (original MDP) state in the source block. To formally study +such families of MDPs, we introduce the new notion of a ""bounded parameter MDP"" +(BMDP), which is a family of (traditional) MDPs defined by specifying upper and +lower bounds on the transition probabilities and rewards. We describe +algorithms that operate on BMDPs to find policies that are approximately +optimal with respect to the original MDP. In combination, our method for +reducing a large implicit MDP to a possibly much smaller BMDP using an +epsilon-homogeneous partition, and our methods for selecting actions in BMDPs +constitute a new approach for analyzing large implicit MDPs. Among its +advantages, this new approach provides insight into existing algorithms to +solving implicit MDPs, provides useful connections to work in automata theory +and model minimization, and suggests methods, which involve varying epsilon, to +trade time and space (specifically in terms of the size of the corresponding +state space) for solution quality. +","Model Reduction Techniques for Computing Approximately Optimal Solutions + for Markov Decision Processes" +" This paper describes a class of probabilistic approximation algorithms based +on bucket elimination which offer adjustable levels of accuracy and efficiency. +We analyze the approximation for several tasks: finding the most probable +explanation, belief updating and finding the maximum a posteriori hypothesis. +We identify regions of completeness and provide preliminary empirical +evaluation on randomly generated networks. +",A Scheme for Approximating Probabilistic Inference +" We present a method for calculation of myopic value of information in +influence diagrams (Howard & Matheson, 1981) based on the strong junction tree +framework (Jensen, Jensen & Dittmer, 1994). The difference in instantiation +order in the influence diagrams is reflected in the corresponding junction +trees by the order in which the chance nodes are marginalized. This order of +marginalization can be changed by table expansion and in effect the same +junction tree with expanded tables may be used for calculating the expected +utility for scenarios with different instantiation order. We also compare our +method to the classic method of modeling different instantiation orders in the +same influence diagram. +",Myopic Value of Information in Influence Diagrams +" Poole has shown that nonmonotonic logics do not handle the lottery paradox +correctly. In this paper we will show that Pollock's theory of defeasible +reasoning fails for the same reason: defeasible reasoning is incompatible with +the skeptical notion of derivability. +",Limitations of Skeptical Default Reasoning +" This paper investigates the problem of finding a preference relation on a set +of acts from the knowledge of an ordering on events (subsets of states of the +world) describing the decision-maker (DM)s uncertainty and an ordering of +consequences of acts, describing the DMs preferences. However, contrary to +classical approaches to decision theory, we try to do it without resorting to +any numerical representation of utility nor uncertainty, and without even using +any qualitative scale on which both uncertainty and preference could be mapped. +It is shown that although many axioms of Savage theory can be preserved and +despite the intuitive appeal of the method for constructing a preference over +acts, the approach is inconsistent with a probabilistic representation of +uncertainty, but leads to the kind of uncertainty theory encountered in +non-monotonic reasoning (especially preferential and rational inference), +closely related to possibility theory. Moreover the method turns out to be +either very little decisive or to lead to very risky decisions, although its +basic principles look sound. This paper raises the question of the very +possibility of purely symbolic approaches to Savage-like decision-making under +uncertainty and obtains preliminary negative results. +",Decision-making Under Ordinal Preferences and Comparative Uncertainty +" We examine the computational complexity of testing and finding small plans in +probabilistic planning domains with succinct representations. We find that many +problems of interest are complete for a variety of complexity classes: NP, +co-NP, PP, NP^PP, co-NP^PP, and PSPACE. Of these, the probabilistic classes PP +and NP^PP are likely to be of special interest in the field of uncertainty in +artificial intelligence and are deserving of additional study. These results +suggest a fruitful direction of future algorithmic development. +",The Complexity of Plan Existence and Evaluation in Probabilistic Domains +" Stochastic algorithms are among the best for solving computationally hard +search and reasoning problems. The runtime of such procedures is characterized +by a random variable. Different algorithms give rise to different probability +distributions. One can take advantage of such differences by combining several +algorithms into a portfolio, and running them in parallel or interleaving them +on a single processor. We provide a detailed evaluation of the portfolio +approach on distributions of hard combinatorial search problems. We show under +what conditions the protfolio approach can have a dramatic computational +advantage over the best traditional methods. +",Algorithm Portfolio Design: Theory vs. Practice +" Conditioning is the generally agreed-upon method for updating probability +distributions when one learns that an event is certainly true. But it has been +argued that we need other rules, in particular the rule of cross-entropy +minimization, to handle updates that involve uncertain information. In this +paper we re-examine such a case: van Fraassen's Judy Benjamin problem, which in +essence asks how one might update given the value of a conditional probability. +We argue that -- contrary to the suggestions in the literature -- it is +possible to use simple conditionalization in this case, and thereby obtain +answers that agree fully with intuition. This contrasts with proposals such as +cross-entropy, which are easier to apply but can give unsatisfactory answers. +Based on the lessons from this example, we speculate on some general +philosophical issues concerning probability update. +",Probability Update: Conditioning vs. Cross-Entropy +" Valuation based systems verifying an idempotent property are studied. A +partial order is defined between the valuations giving them a lattice +structure. Then, two different strategies are introduced to represent +valuations: as infimum of the most informative valuations or as supremum of the +least informative ones. It is studied how to carry out computations with both +representations in an efficient way. The particular cases of finite sets and +convex polytopes are considered. +",Inference with Idempotent Valuations +" We review the problem of time-critical action and discuss a reformulation +that shifts knowledge acquisition from the assessment of complex temporal +probabilistic dependencies to the direct assessment of time-dependent utilities +over key outcomes of interest. We dwell on a class of decision problems +characterized by the centrality of diagnosing and reacting in a timely manner +to pathological processes. We motivate key ideas in the context of trauma-care +triage and transportation decisions. +",Time-Critical Reasoning: Representations and Application +" A new method is developed to represent probabilistic relations on multiple +random events. Where previously knowledge bases containing probabilistic rules +were used for this purpose, here a probability distribution over the relations +is directly represented by a Bayesian network. By using a powerful way of +specifying conditional probability distributions in these networks, the +resulting formalism is more expressive than the previous ones. Particularly, it +provides for constraints on equalities of events, and it allows to define +complex, nested combination functions. +",Relational Bayesian Networks +" Decomposable models and Bayesian networks can be defined as sequences of +oligo-dimensional probability measures connected with operators of composition. +The preliminary results suggest that the probabilistic models allowing for +effective computational procedures are represented by sequences possessing a +special property; we shall call them perfect sequences. The paper lays down the +elementary foundation necessary for further study of iterative application of +operators of composition. We believe to develop a technique describing several +graph models in a unifying way. We are convinced that practically all +theoretical results and procedures connected with decomposable models and +Bayesian networks can be translated into the terminology introduced in this +paper. For example, complexity of computational procedures in these models is +closely dependent on possibility to change the ordering of oligo-dimensional +measures defining the model. Therefore, in this paper, lot of attention is paid +to possibility to change ordering of the operators of composition. +",Composition of Probability Measures on Finite Spaces +" The efficiency of inference in both the Hugin and, most notably, the +Shafer-Shenoy architectures can be improved by exploiting the independence +relations induced by the incoming messages of a clique. That is, the message to +be sent from a clique can be computed via a factorization of the clique +potential in the form of a junction tree. In this paper we show that by +exploiting such nested junction trees in the computation of messages both space +and time costs of the conventional propagation methods may be reduced. The +paper presents a structured way of exploiting the nested junction trees +technique to achieve such reductions. The usefulness of the method is +emphasized through a thorough empirical evaluation involving ten large +real-world Bayesian networks and the Hugin inference algorithm. +",Nested Junction Trees +" Bayesian networks provide a modeling language and associated inference +algorithm for stochastic domains. They have been successfully applied in a +variety of medium-scale applications. However, when faced with a large complex +domain, the task of modeling using Bayesian networks begins to resemble the +task of programming using logical circuits. In this paper, we describe an +object-oriented Bayesian network (OOBN) language, which allows complex domains +to be described in terms of inter-related objects. We use a Bayesian network +fragment to describe the probabilistic relations between the attributes of an +object. These attributes can themselves be objects, providing a natural +framework for encoding part-of hierarchies. Classes are used to provide a +reusable probabilistic model which can be applied to multiple similar objects. +Classes also support inheritance of model fragments from a class to a subclass, +allowing the common aspects of related classes to be defined only once. Our +language has clear declarative semantics: an OOBN can be interpreted as a +stochastic functional program, so that it uniquely specifies a probabilistic +model. We provide an inference algorithm for OOBNs, and show that much of the +structural information encoded by an OOBN--particularly the encapsulation of +variables within an object and the reuse of model fragments in different +contexts--can also be used to speed up the inference process. +",Object-Oriented Bayesian Networks +" We consider probabilistic inference in general hybrid networks, which include +continuous and discrete variables in an arbitrary topology. We reexamine the +question of variable discretization in a hybrid network aiming at minimizing +the information loss induced by the discretization. We show that a nonuniform +partition across all variables as opposed to uniform partition of each variable +separately reduces the size of the data structures needed to represent a +continuous function. We also provide a simple but efficient procedure for +nonuniform partition. To represent a nonuniform discretization in the computer +memory, we introduce a new data structure, which we call a Binary Split +Partition (BSP) tree. We show that BSP trees can be an exponential factor +smaller than the data structures in the standard uniform discretization in +multiple dimensions and show how the BSP trees can be used in the standard join +tree algorithm. We show that the accuracy of the inference process can be +significantly improved by adjusting discretization with evidence. We construct +an iterative anytime algorithm that gradually improves the quality of the +discretization and the accuracy of the answer on a query. We provide empirical +evidence that the algorithm converges. +",Nonuniform Dynamic Discretization in Hybrid Networks +" The idea of fully accepting statements when the evidence has rendered them +probable enough faces a number of difficulties. We leave the interpretation of +probability largely open, but attempt to suggest a contextual approach to full +belief. We show that the difficulties of probabilistic acceptance are not as +severe as they are sometimes painted, and that though there are oddities +associated with probabilistic acceptance they are in some instances less +awkward than the difficulties associated with other nonmonotonic formalisms. We +show that the structure at which we arrive provides a natural home for +statistical inference. +",Probabilistic Acceptance +" In most current applications of belief networks, domain knowledge is +represented by a single belief network that applies to all problem instances in +the domain. In more complex domains, problem-specific models must be +constructed from a knowledge base encoding probabilistic relationships in the +domain. Most work in knowledge-based model construction takes the rule as the +basic unit of knowledge. We present a knowledge representation framework that +permits the knowledge base designer to specify knowledge in larger semantically +meaningful units which we call network fragments. Our framework provides for +representation of asymmetric independence and canonical intercausal +interaction. We discuss the combination of network fragments to form +problem-specific models to reason about particular problem instances. The +framework is illustrated using examples from the domain of military situation +awareness. +","Network Fragments: Representing Knowledge for Constructing Probabilistic + Models" +" This paper introduces a computational framework for reasoning in Bayesian +belief networks that derives significant advantages from focused inference and +relevance reasoning. This framework is based on d -separation and other simple +and computationally efficient techniques for pruning irrelevant parts of a +network. Our main contribution is a technique that we call relevance-based +decomposition. Relevance-based decomposition approaches belief updating in +large networks by focusing on their parts and decomposing them into partially +overlapping subnetworks. This makes reasoning in some intractable networks +possible and, in addition, often results in significant speedup, as the total +time taken to update all subnetworks is in practice often considerably less +than the time taken to update the network as a whole. We report results of +empirical tests that demonstrate practical significance of our approach. +","Computational Advantages of Relevance Reasoning in Bayesian Belief + Networks" +" A submarine's sonar team is responsible for detecting, localising and +classifying targets using information provided by the platform's sensor suite. +The information used to make these assessments is typically uncertain and/or +incomplete and is likely to require a measure of confidence in its reliability. +Moreover, improvements in sensor and communication technology are resulting in +increased amounts of on-platform and off-platform information available for +evaluation. This proliferation of imprecise information increases the risk of +overwhelming the operator. To assist the task of localisation and +classification a concept demonstration decision aid (Horizon), based on +evidential reasoning, has been developed. Horizon is an information fusion +software package for representing and fusing imprecise information about the +state of the world, expressed across suitable frames of reference. The Horizon +software is currently at prototype stage. +",A Target Classification Decision Aid +" By discussing several examples, the theory of generalized functional models +is shown to be very natural for modeling some situations of reasoning under +uncertainty. A generalized functional model is a pair (f, P) where f is a +function describing the interactions between a parameter variable, an +observation variable and a random source, and P is a probability distribution +for the random source. Unlike traditional functional models, generalized +functional models do not require that there is only one value of the parameter +variable that is compatible with an observation and a realization of the random +source. As a consequence, the results of the analysis of a generalized +functional model are not expressed in terms of probability distributions but +rather by support and plausibility functions. The analysis of a generalized +functional model is very logical and is inspired from ideas already put forward +by R.A. Fisher in his theory of fiducial probability. +",Support and Plausibility Degrees in Generalized Functional Models +" There is a brief description of the probabilistic causal graph model for +representing, reasoning with, and learning causal structure using Bayesian +networks. It is then argued that this model is closely related to how humans +reason with and learn causal structure. It is shown that studies in psychology +on discounting (reasoning concerning how the presence of one cause of an effect +makes another cause less probable) support the hypothesis that humans reach the +same judgments as algorithms for doing inference in Bayesian networks. Next, it +is shown how studies by Piaget indicate that humans learn causal structure by +observing the same independencies and dependencies as those used by certain +algorithms for learning the structure of a Bayesian network. Based on this +indication, a subjective definition of causality is forwarded. Finally, methods +for further testing the accuracy of these claims are discussed. +",The Cognitive Processing of Causal Knowledge +" Bayesian knowledge bases (BKBs) are a generalization of Bayes networks and +weighted proof graphs (WAODAGs), that allow cycles in the causal graph. +Reasoning in BKBs requires finding the most probable inferences consistent with +the evidence. The cost-sharing heuristic for finding least-cost explanations in +WAODAGs was presented and shown to be effective by Charniak and Husain. +However, the cycles in BKBs would make the definition of cost-sharing cyclic as +well, if applied directly to BKBs. By treating the defining equations of +cost-sharing as a system of equations, one can properly define an admissible +cost-sharing heuristic for BKBs. Empirical evaluation shows that cost-sharing +improves performance significantly when applied to BKBs. +",Cost-Sharing in Bayesian Knowledge Bases +" Default logic encounters some conceptual difficulties in representing common +sense reasoning tasks. We argue that we should not try to formulate modular +default rules that are presumed to work in all or most circumstances. We need +to take into account the importance of the context which is continuously +evolving during the reasoning process. Sequential thresholding is a +quantitative counterpart of default logic which makes explicit the role context +plays in the construction of a non-monotonic extension. We present a semantic +characterization of generic non-monotonic reasoning, as well as the +instantiations pertaining to default logic and sequential thresholding. This +provides a link between the two mechanisms as well as a way to integrate the +two that can be beneficial to both. +",Sequential Thresholds: Context Sensitive Default Extensions +" A stable joint plan should guarantee the achievement of a designer's goal in +a multi-agent environment, while ensuring that deviations from the prescribed +plan would be detected. We present a computational framework where stable joint +plans can be studied, as well as several basic results about the +representation, verification and synthesis of stable joint plans. +",On Stable Multi-Agent Behavior in Face of Uncertainty +" This paper is concerned with planning in stochastic domains by means of +partially observable Markov decision processes (POMDPs). POMDPs are difficult +to solve. This paper identifies a subclass of POMDPs called region observable +POMDPs, which are easier to solve and can be used to approximate general POMDPs +to arbitrary accuracy. +",Region-Based Approximations for Planning in Stochastic Domains +" This paper explores the role of independence of causal influence (ICI) in +Bayesian network inference. ICI allows one to factorize a conditional +probability table into smaller pieces. We describe a method for exploiting the +factorization in clique tree propagation (CTP) - the state-of-the-art exact +inference algorithm for Bayesian networks. We also present empirical results +showing that the resulting algorithm is significantly more efficient than the +combination of CTP and previous techniques for exploiting ICI. +",Independence of Causal Influence and Clique Tree Propagation +" Planning problems where effects of actions are non-deterministic can be +modeled as Markov decision processes. Planning problems are usually +goal-directed. This paper proposes several techniques for exploiting the +goal-directedness to accelerate value iteration, a standard algorithm for +solving Markov decision processes. Empirical studies have shown that the +techniques can bring about significant speedups. +",Fast Value Iteration for Goal-Directed Markov Decision Processes +" We analyse the complexity of environments according to the policies that need +to be used to achieve high performance. The performance results for a +population of policies leads to a distribution that is examined in terms of +policy complexity and analysed through several diagrams and indicators. The +notion of environment response curve is also introduced, by inverting the +performance results into an ability scale. We apply all these concepts, +diagrams and indicators to a minimalistic environment class, agent-populated +elementary cellular automata, showing how the difficulty, discriminating power +and ranges (previous to normalisation) may vary for several environments. +",Complexity distribution of agent policies +" The best currently known interactive debugging systems rely upon some +meta-information in terms of fault probabilities in order to improve their +efficiency. However, misleading meta information might result in a dramatic +decrease of the performance and its assessment is only possible a-posteriori. +Consequently, as long as the actual fault is unknown, there is always some risk +of suboptimal interactions. In this work we present a reinforcement learning +strategy that continuously adapts its behavior depending on the performance +achieved and minimizes the risk of using low-quality meta information. +Therefore, this method is suitable for application scenarios where reliable +prior fault estimates are difficult to obtain. Using diverse real-world +knowledge bases, we show that the proposed interactive query strategy is +scalable, features decent reaction time, and outperforms both entropy-based and +no-risk strategies on average w.r.t. required amount of user interaction. +",RIO: Minimizing User Interaction in Debugging of Knowledge Bases +" The criterion commonly used in directed acyclic graphs (dags) for testing +graphical independence is the well-known d-separation criterion. It allows us +to build graphical representations of dependency models (usually probabilistic +dependency models) in the form of belief networks, which make easy +interpretation and management of independence relationships possible, without +reference to numerical parameters (conditional probabilities). In this paper, +we study the following combinatorial problem: finding the minimum d-separating +set for two nodes in a dag. This set would represent the minimum information +(in the sense of minimum number of variables) necessary to prevent these two +nodes from influencing each other. The solution to this basic problem and some +of its extensions can be useful in several ways, as we shall see later. Our +solution is based on a two-step process: first, we reduce the original problem +to the simpler one of finding a minimum separating set in an undirected graph, +and second, we develop an algorithm for solving it. +",An Algorithm for Finding Minimum d-Separating Sets in Belief Networks +" This paper works through the optimization of a real world planning problem, +with a combination of a generative planning tool and an influence diagram +solver. The problem is taken from an existing application in the domain of oil +spill emergency response. The planning agent manages constraints that order +sets of feasible equipment employment actions. This is mapped at an +intermediate level of abstraction onto an influence diagram. In addition, the +planner can apply a surveillance operator that determines observability of the +state---the unknown trajectory of the oil. The uncertain world state and the +objective function properties are part of the influence diagram structure, but +not represented in the planning agent domain. By exploiting this structure +under the constraints generated by the planning agent, the influence diagram +solution complexity simplifies considerably, and an optimum solution to the +employment problem based on the objective function is found. Finding this +optimum is equivalent to the simultaneous evaluation of a range of plans. This +result is an example of bounded optimality, within the limitations of this +hybrid generative planner and influence diagram architecture. +","Constraining Influence Diagram Structure by Generative Planning: An + Application to the Optimization of Oil Spill Response" +" We extend Gaussian networks - directed acyclic graphs that encode +probabilistic relationships between variables - to its vector form. Vector +Gaussian continuous networks consist of composite nodes representing +multivariates, that take continuous values. These vector or composite nodes can +represent correlations between parents, as opposed to conventional univariate +nodes. We derive rules for inference in these networks based on two methods: +message propagation and topology transformation. These two approaches lead to +the development of algorithms, that can be implemented in either a centralized +or a decentralized manner. The domain of application of these networks are +monitoring and estimation problems. This new representation along with the +rules for inference developed here can be used to derive current Bayesian +algorithms such as the Kalman filter, and provide a rich foundation to develop +new algorithms. We illustrate this process by deriving the decentralized form +of the Kalman filter. This work unifies concepts from artificial intelligence +and modern control theory. +","Inference Using Message Propagation and Topology Transformation in + Vector Gaussian Continuous Networks" +" We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a +structural and temporal extension of Bayesian Belief Networks (BNs) to +facilitate normative temporal and causal modeling under uncertainty. In this +paper we present definitions of the model, its components, and its fundamental +properties. We also discuss how to represent various types of temporal +knowledge, with an emphasis on hybrid temporal-explicit time modeling, dynamic +structures, avoiding causal temporal inconsistencies, and dealing with models +that involve simultaneously actions (decisions) and causal and non-causal +associations. We examine the relationships among BNs, Modifiable Belief +Networks, and MTBNs with a single temporal granularity, and suggest areas of +application suitable to each one of them. +","A Structurally and Temporally Extended Bayesian Belief Network Model: + Definitions, Properties, and Modeling Techniques" +" Graphical Markov models use graphs, either undirected, directed, or mixed, to +represent possible dependences among statistical variables. Applications of +undirected graphs (UDGs) include models for spatial dependence and image +analysis, while acyclic directed graphs (ADGs), which are especially convenient +for statistical analysis, arise in such fields as genetics and psychometrics +and as models for expert systems and Bayesian belief networks. Lauritzen, +Wermuth and Frydenberg (LWF) introduced a Markov property for chain graphs, +which are mixed graphs that can be used to represent simultaneously both causal +and associative dependencies and which include both UDGs and ADGs as special +cases. In this paper an alternative Markov property (AMP) for chain graphs is +introduced, which in some ways is a more direct extension of the ADG Markov +property than is the LWF property for chain graph. +",An Alternative Markov Property for Chain Graphs +" Approximate models of world state transitions are necessary when building +plans for complex systems operating in dynamic environments. External event +probabilities can depend on state feature values as well as time spent in that +particular state. We assign temporally -dependent probability functions to +state transitions. These functions are used to locally compute state +probabilities, which are then used to select highly probable goal paths and +eliminate improbable states. This probabilistic model has been implemented in +the Cooperative Intelligent Real-time Control Architecture (CIRCA), which +combines an AI planner with a separate real-time system such that plans are +developed, scheduled, and executed with real-time guarantees. We present flight +simulation tests that demonstrate how our probabilistic model may improve CIRCA +performance. +",Plan Development using Local Probabilistic Models +" A nonmonotonic logic of thresholded generalizations is presented. Given +propositions A and B from a language L and a positive integer k, the +thresholded generalization A=>B{k} means that the conditional probability +P(B|A) falls short of one by no more than c*d^k. A two-level probability +structure is defined. At the lower level, a model is defined to be a +probability function on L. At the upper level, there is a probability +distribution over models. A definition is given of what it means for a +collection of thresholded generalizations to entail another thresholded +generalization. This nonmonotonic entailment relation, called ""entailment in +probability"", has the feature that its conclusions are ""probabilistically +trustworthy"" meaning that, given true premises, it is improbable that an +entailed conclusion would be false. A procedure is presented for ascertaining +whether any given collection of premises entails any given conclusion. It is +shown that entailment in probability is closely related to Goldszmidt and +Pearl's System-Z^+, thereby demonstrating that the conclusions of System-Z^+ +are probabilistically trustworthy. +",Entailment in Probability of Thresholded Generalizations +" The computational complexity of reasoning within the Dempster-Shafer theory +of evidence is one of the main points of criticism this formalism has to face. +To overcome this difficulty various approximation algorithms have been +suggested that aim at reducing the number of focal elements in the belief +functions involved. Besides introducing a new algorithm using this method, this +paper describes an empirical study that examines the appropriateness of these +approximation procedures in decision making situations. It presents the +empirical findings and discusses the various tradeoffs that have to be taken +into account when actually applying one of these methods. +","Approximations for Decision Making in the Dempster-Shafer Theory of + Evidence" +" Possibility theory offers a framework where both Lehmann's ""preferential +inference"" and the more productive (but less cautious) ""rational closure +inference"" can be represented. However, there are situations where the second +inference does not provide expected results either because it cannot produce +them, or even provide counter-intuitive conclusions. This state of facts is not +due to the principle of selecting a unique ordering of interpretations (which +can be encoded by one possibility distribution), but rather to the absence of +constraints expressing pieces of knowledge we have implicitly in mind. It is +advocated in this paper that constraints induced by independence information +can help finding the right ordering of interpretations. In particular, +independence constraints can be systematically assumed with respect to formulas +composed of literals which do not appear in the conditional knowledge base, or +for default rules with respect to situations which are ""normal"" according to +the other default rules in the base. The notion of independence which is used +can be easily expressed in the qualitative setting of possibility theory. +Moreover, when a counter-intuitive plausible conclusion of a set of defaults, +is in its rational closure, but not in its preferential closure, it is always +possible to repair the set of defaults so as to produce the desired conclusion. +","Coping with the Limitations of Rational Inference in the Framework of + Possibility Theory" +" We develop a qualitative model of decision making with two aims: to describe +how people make simple decisions and to enable computer programs to do the +same. Current approaches based on Planning or Decisions Theory either ignore +uncertainty and tradeoffs, or provide languages and algorithms that are too +complex for this task. The proposed model provides a language based on rules, a +semantics based on high probabilities and lexicographical preferences, and a +transparent decision procedure where reasons for and against decisions +interact. The model is no substitude for Decision Theory, yet for decisions +that people find easy to explain it may provide an appealing alternative. +",Arguing for Decisions: A Qualitative Model of Decision Making +" Bayesian networks provide a language for qualitatively representing the +conditional independence properties of a distribution. This allows a natural +and compact representation of the distribution, eases knowledge acquisition, +and supports effective inference algorithms. It is well-known, however, that +there are certain independencies that we cannot capture qualitatively within +the Bayesian network structure: independencies that hold only in certain +contexts, i.e., given a specific assignment of values to certain variables. In +this paper, we propose a formal notion of context-specific independence (CSI), +based on regularities in the conditional probability tables (CPTs) at a node. +We present a technique, analogous to (and based on) d-separation, for +determining when such independence holds in a given network. We then focus on a +particular qualitative representation scheme - tree-structured CPTs - for +capturing CSI. We suggest ways in which this representation can be used to +support effective inference algorithms. In particular, we present a structural +decomposition of the resulting network which can improve the performance of +clustering algorithms, and an alternative algorithm based on cutset +conditioning. +",Context-Specific Independence in Bayesian Networks +" We develop and extend existing decision-theoretic methods for troubleshooting +a nonfunctioning device. Traditionally, diagnosis with Bayesian networks has +focused on belief updating---determining the probabilities of various faults +given current observations. In this paper, we extend this paradigm to include +taking actions. In particular, we consider three classes of actions: (1) we can +make observations regarding the behavior of a device and infer likely faults as +in traditional diagnosis, (2) we can repair a component and then observe the +behavior of the device to infer likely faults, and (3) we can change the +configuration of the device, observe its new behavior, and infer the likelihood +of faults. Analysis of latter two classes of troubleshooting actions requires +incorporating notions of persistence into the belief-network formalism used for +probabilistic inference. +","Decision-Theoretic Troubleshooting: A Framework for Repair and + Experiment" +" It is shown that the ability of the interval probability representation to +capture epistemological independence is severely limited. Two events are +epistemologically independent if knowledge of the first event does not alter +belief (i.e., probability bounds) about the second. However, independence in +this form can only exist in a 2-monotone probability function in degenerate +cases i.e., if the prior bounds are either point probabilities or entirely +vacuous. Additional limitations are characterized for other classes of lower +probabilities as well. It is argued that these phenomena are simply a matter of +interpretation. They appear to be limitations when one interprets probability +bounds as a measure of epistemological indeterminacy (i.e., uncertainty arising +from a lack of knowledge), but are exactly as one would expect when probability +intervals are interpreted as representations of ontological indeterminacy +(indeterminacy introduced by structural approximations). The ontological +interpretation is introduced and discussed. +",Independence with Lower and Upper Probabilities +" Lower and upper probabilities, also known as Choquet capacities, are widely +used as a convenient representation for sets of probability distributions. This +paper presents a graphical decomposition and exact propagation algorithm for +computing marginal posteriors of 2-monotone lower probabilities (equivalently, +2-alternating upper probabilities). +",Propagation of 2-Monotone Lower Probabilities on an Undirected Graph +" Quasi-Bayesian theory uses convex sets of probability distributions and +expected loss to represent preferences about plans. The theory focuses on +decision robustness, i.e., the extent to which plans are affected by deviations +in subjective assessments of probability. The present work presents solutions +for plan generation when robustness of probability assessments must be +included: plans contain information about the robustness of certain actions. +The surprising result is that some problems can be solved faster in the +Quasi-Bayesian framework than within usual Bayesian theory. We investigate this +on the planning to observe problem, i.e., an agent must decide whether to take +new observations or not. The fundamental question is: How, and how much, to +search for a ""best"" plan, based on the robustness of probability assessments? +Plan generation algorithms are derived in the context of material +classification with an acoustic robotic probe. A package that constructs +Quasi-Bayesian plans is available through anonymous ftp. +","Quasi-Bayesian Strategies for Efficient Plan Generation: Application to + the Planning to Observe Problem" +" Real-time Decision algorithms are a class of incremental resource-bounded +[Horvitz, 89] or anytime [Dean, 93] algorithms for evaluating influence +diagrams. We present a test domain for real-time decision algorithms, and the +results of experiments with several Real-time Decision Algorithms in this +domain. The results demonstrate high performance for two algorithms, a +decision-evaluation variant of Incremental Probabilisitic Inference [D'Ambrosio +93] and a variant of an algorithm suggested by Goldszmidt, [Goldszmidt, 95], +PK-reduced. We discuss the implications of these experimental results and +explore the broader applicability of these algorithms. +",Some Experiments with Real-Time Decision Algorithms +" Probabilistic inference algorithms for finding the most probable explanation, +the maximum aposteriori hypothesis, and the maximum expected utility and for +updating belief are reformulated as an elimination--type algorithm called +bucket elimination. This emphasizes the principle common to many of the +algorithms appearing in that literature and clarifies their relationship to +nonserial dynamic programming algorithms. We also present a general way of +combining conditioning and elimination within this framework. Bounds on +complexity are given for all the algorithms as a function of the problem's +structure. +","Bucket Elimination: A Unifying Framework for Several Probabilistic + Inference" +" In this paper we propose a family of algorithms combining tree-clustering +with conditioning that trade space for time. Such algorithms are useful for +reasoning in probabilistic and deterministic networks as well as for +accomplishing optimization tasks. By analyzing the problem structure it will be +possible to select from a spectrum the algorithm that best meets a given +time-space specification. +",Topological Parameters for Time-Space Tradeoff +" This paper provides a formal and practical framework for sound abstraction of +probabilistic actions. We start by precisely defining the concept of sound +abstraction within the context of finite-horizon planning (where each plan is a +finite sequence of actions). Next we show that such abstraction cannot be +performed within the traditional probabilistic action representation, which +models a world with a single probability distribution over the state space. We +then present the constraint mass assignment representation, which models the +world with a set of probability distributions and is a generalization of mass +assignment representations. Within this framework, we present sound abstraction +procedures for three types of action abstraction. We end the paper with +discussions and related work on sound and approximate abstraction. We give +pointers to papers in which we discuss other sound abstraction-related issues, +including applications, estimating loss due to abstraction, and automatically +generating abstraction hierarchies. +","Sound Abstraction of Probabilistic Actions in The Constraint Mass + Assignment Framework" +" This paper discusses belief revision under uncertain inputs in the framework +of possibility theory. Revision can be based on two possible definitions of the +conditioning operation, one based on min operator which requires a purely +ordinal scale only, and another based on product, for which a richer structure +is needed, and which is a particular case of Dempster's rule of conditioning. +Besides, revision under uncertain inputs can be understood in two different +ways depending on whether the input is viewed, or not, as a constraint to +enforce. Moreover, it is shown that M.A. Williams' transmutations, originally +defined in the setting of Spohn's functions, can be captured in this framework, +as well as Boutilier's natural revision. +",Belief Revision with Uncertain Inputs in the Possibilistic Setting +" Many algorithms for processing probabilistic networks are dependent on the +topological properties of the problem's structure. Such algorithms (e.g., +clustering, conditioning) are effective only if the problem has a sparse graph +captured by parameters such as tree width and cycle-cut set size. In this paper +we initiate a study to determine the potential of structure-based algorithms in +real-life applications. We analyze empirically the structural properties of +problems coming from the circuit diagnosis domain. Specifically, we locate +those properties that capture the effectiveness of clustering and conditioning +as well as of a family of conditioning+clustering algorithms designed to +gradually trade space for time. We perform our analysis on 11 benchmark +circuits widely used in the testing community. We also report on the effect of +ordering heuristics on tree-clustering and show that, on our benchmarks, the +well-known max-cardinality ordering is substantially inferior to an ordering +called min-degree. +","An Evaluation of Structural Parameters for Probabilistic Reasoning: + Results on Benchmark Circuits" +" The study of belief change has been an active area in philosophy and AI. In +recent years two special cases of belief change, belief revision and belief +update, have been studied in detail. Roughly, revision treats a surprising +observation as a sign that previous beliefs were wrong, while update treats a +surprising observation as an indication that the world has changed. In general, +we would expect that an agent making an observation may both want to revise +some earlier beliefs and assume that some change has occurred in the world. We +define a novel approach to belief change that allows us to do this, by applying +ideas from probability theory in a qualitative setting. The key idea is to use +a qualitative Markov assumption, which says that state transitions are +independent. We show that a recent approach to modeling qualitative uncertainty +using plausibility measures allows us to make such a qualitative Markov +assumption in a relatively straightforward way, and show how the Markov +assumption can be used to provide an attractive belief-change model. +",A Qualitative Markov Assumption and its Implications for Belief Change +" Modeling worlds and actions under uncertainty is one of the central problems +in the framework of decision-theoretic planning. The representation must be +general enough to capture real-world problems but at the same time it must +provide a basis upon which theoretical results can be derived. The central +notion in the framework we propose here is that of the affine-operator, which +serves as a tool for constructing (convex) sets of probability distributions, +and which can be considered as a generalization of belief functions and +interval mass assignments. Uncertainty in the state of the worlds is modeled +with sets of probability distributions, represented by affine-trees while +actions are defined as tree-manipulators. A small set of key properties of the +affine-operator is presented, forming the basis for most existing +operator-based definitions of probabilistic action projection and action +abstraction. We derive and prove correct three projection rules, which vividly +illustrate the precision-complexity tradeoff in plan projection. Finally, we +show how the three types of action abstraction identified by Haddawy and Doan +are manifested in the present framework. +",Theoretical Foundations for Abstraction-Based Probabilistic Planning +" Recent research has found that diagnostic performance with Bayesian belief +networks is often surprisingly insensitive to imprecision in the numerical +probabilities. For example, the authors have recently completed an extensive +study in which they applied random noise to the numerical probabilities in a +set of belief networks for medical diagnosis, subsets of the CPCS network, a +subset of the QMR (Quick Medical Reference) focused on liver and bile diseases. +The diagnostic performance in terms of the average probabilities assigned to +the actual diseases showed small sensitivity even to large amounts of noise. In +this paper, we summarize the findings of this study and discuss possible +explanations of this low sensitivity. One reason is that the criterion for +performance is average probability of the true hypotheses, rather than average +error in probability, which is insensitive to symmetric noise distributions. +But, we show that even asymmetric, logodds-normal noise has modest effects. A +second reason is that the gold-standard posterior probabilities are often near +zero or one, and are little disturbed by noise. +","Why Is Diagnosis Using Belief Networks Insensitive to Imprecision In + Probabilities?" +" We report on work towards flexible algorithms for solving decision problems +represented as influence diagrams. An algorithm is given to construct a tree +structure for each decision node in an influence diagram. Each tree represents +a decision function and is constructed incrementally. The improvements to the +tree converge to the optimal decision function (neglecting computational costs) +and the asymptotic behaviour is only a constant factor worse than dynamic +programming techniques, counting the number of Bayesian network queries. +Empirical results show how expected utility increases with the size of the tree +and the number of Bayesian net calculations. +",Flexible Policy Construction by Information Refinement +" Inference algorithms for arbitrary belief networks are impractical for large, +complex belief networks. Inference algorithms for specialized classes of belief +networks have been shown to be more efficient. In this paper, we present a +search-based algorithm for approximate inference on arbitrary, noisy-OR belief +networks, generalizing earlier work on search-based inference for two-level, +noisy-OR belief networks. Initial experimental results appear promising. +","Efficient Search-Based Inference for Noisy-OR Belief Networks: + TopEpsilon" +" The validation of data from sensors has become an important issue in the +operation and control of modern industrial plants. One approach is to use +knowledge based techniques to detect inconsistencies in measured data. This +article presents a probabilistic model for the detection of such +inconsistencies. Based on probability propagation, this method is able to find +the existence of a possible fault among the set of sensors. That is, if an +error exists, many sensors present an apparent fault due to the propagation +from the sensor(s) with a real fault. So the fault detection mechanism can only +tell if a sensor has a potential fault, but it can not tell if the fault is +real or apparent. So the central problem is to develop a theory, and then an +algorithm, for distinguishing real and apparent faults, given that one or more +sensors can fail at the same time. This article then, presents an approach +based on two levels: (i) probabilistic reasoning, to detect a potential fault, +and (ii) constraint management, to distinguish the real fault from the apparent +ones. The proposed approach is exemplified by applying it to a power plant +model. +",A Probabilistic Model For Sensor Validation +" We present deterministic techniques for computing upper and lower bounds on +marginal probabilities in sigmoid and noisy-OR networks. These techniques +become useful when the size of the network (or clique size) precludes exact +computations. We illustrate the tightness of the bounds by numerical +experiments. +",Computing Upper and Lower Bounds on Likelihoods in Intractable Networks +" We present a prototype of a decision support system for management of the +fungal disease mildew in winter wheat. The prototype is based on an influence +diagram which is used to determine the optimal time and dose of mildew +treatments. This involves multiple decision opportunities over time, +stochasticity, inaccurate information and incomplete knowledge. The paper +describes the practical and theoretical problems encountered during the +construction of the influence diagram, and also the experience with the +prototype. +",MIDAS - An Influence Diagram for Management of Mildew in Winter Wheat +" Although probabilistic inference in a general Bayesian belief network is an +NP-hard problem, computation time for inference can be reduced in most +practical cases by exploiting domain knowledge and by making approximations in +the knowledge representation. In this paper we introduce the property of +similarity of states and a new method for approximate knowledge representation +and inference which is based on this property. We define two or more states of +a node to be similar when the ratio of their probabilities, the likelihood +ratio, does not depend on the instantiations of the other nodes in the network. +We show that the similarity of states exposes redundancies in the joint +probability distribution which can be exploited to reduce the computation time +of probabilistic inference in networks with multiple similar states, and that +the computational complexity in the networks with exponentially many similar +states might be polynomial. We demonstrate our ideas on the example of a BN2O +network -- a two layer network often used in diagnostic problems -- by reducing +it to a very close network with multiple similar states. We show that the +answers to practical queries converge very fast to the answers obtained with +the original network. The maximum error is as low as 5% for models that require +only 10% of the computation time needed by the original BN2O model. +","Computational Complexity Reduction for BN2O Networks Using Similarity of + States" +" Uncertainty may be taken to characterize inferences, their conclusions, their +premises or all three. Under some treatments of uncertainty, the inferences +itself is never characterized by uncertainty. We explore both the significance +of uncertainty in the premises and in the conclusion of an argument that +involves uncertainty. We argue that for uncertainty to characterize the +conclusion of an inference is natural, but that there is an interplay between +uncertainty in the premises and uncertainty in the procedure of argument +itself. We show that it is possible in principle to incorporate all uncertainty +in the premises, rendering uncertainty arguments deductively valid. But we then +argue (1) that this does not reflect human argument, (2) that it is +computationally costly, and (3) that the gain in simplicity obtained by +allowing uncertainty inference can sometimes outweigh the loss of flexibility +it entails. +",Uncertain Inferences and Uncertain Conclusions +" Like any large system development effort, the construction of a complex +belief network model requires systems engineering to manage the design and +construction process. We propose a rapid prototyping approach to network +engineering. We describe criteria for identifying network modules and the use +of ""stubs"" to represent not-yet-constructed modules. We propose an object +oriented representation for belief networks which captures the semantics of the +problem in addition to conditional independencies and probabilities. Methods +for evaluating complex belief network models are discussed. The ideas are +illustrated with examples from a large belief network construction problem in +the military intelligence domain. +",Network Engineering for Complex Belief Networks +" In this paper we propose a framework for combining Disjunctive Logic +Programming and Poole's Probabilistic Horn Abduction. We use the concept of +hypothesis to specify the probability structure. We consider the case in which +probabilistic information is not available. Instead of using probability +intervals, we allow for the specification of the probabilities of disjunctions. +Because minimal models are used as characteristic models in disjunctive logic +programming, we apply the principle of indifference on the set of minimal +models to derive default probability values. We define the concepts of +explanation and partial explanation of a formula, and use them to determine the +default probability distribution(s) induced by a program. An algorithm for +calculating the default probability of a goal is presented. +",Probabilistic Disjunctive Logic Programming +" A naive (or Idiot) Bayes network is a network with a single hypothesis node +and several observations that are conditionally independent given the +hypothesis. We recently surveyed a number of members of the UAI community and +discovered a general lack of understanding of the implications of the Naive +Bayes assumption on the kinds of problems that can be solved by these networks. +It has long been recognized [Minsky 61] that if observations are binary, the +decision surfaces in these networks are hyperplanes. We extend this result +(hyperplane separability) to Naive Bayes networks with m-ary observations. In +addition, we illustrate the effect of observation-observation dependencies on +decision surfaces. Finally, we discuss the implications of these results on +knowledge acquisition and research in learning. +",Geometric Implications of the Naive Bayes Assumption +" We show that the d -separation criterion constitutes a valid test for +conditional independence relationships that are induced by feedback systems +involving discrete variables. +",Identifying Independencies in Causal Graphs with Feedback +" We derive qualitative relationships about the informational relevance of +variables in graphical decision models based on a consideration of the topology +of the models. Specifically, we identify dominance relations for the expected +value of information on chance variables in terms of their position and +relationships in influence diagrams. The qualitative relationships can be +harnessed to generate nonnumerical procedures for ordering uncertain variables +in a decision model by their informational relevance. +",A Graph-Theoretic Analysis of Information Value +" This paper shows how we can combine logical representations of actions and +decision theory in such a manner that seems natural for both. In particular we +assume an axiomatization of the domain in terms of situation calculus, using +what is essentially Reiter's solution to the frame problem, in terms of the +completion of the axioms defining the state change. Uncertainty is handled in +terms of the independent choice logic, which allows for independent choices and +a logic program that gives the consequences of the choices. As part of the +consequences are a specification of the utility of (final) states. The robot +adopts robot plans, similar to the GOLOG programming language. Within this +logic, we can define the expected utility of a conditional plan, based on the +axiomatization of the actions, the uncertainty and the utility. The ?planning' +problem is to find the plan with the highest expected utility. This is related +to recent structured representations for POMDPs; here we use stochastic +situation calculus rules to specify the state transition function and the +reward/value function. Finally we show that with stochastic frame axioms, +actions representations in probabilistic STRIPS are exponentially larger than +using the representation proposed here. +","A Framework for Decision-Theoretic Planning I: Combining the Situation + Calculus, Conditional Plans, Probability and Utility" +" We present two Monte Carlo sampling algorithms for probabilistic inference +that guarantee polynomial-time convergence for a larger class of network than +current sampling algorithms provide. These new methods are variants of the +known likelihood weighting algorithm. We use of recent advances in the theory +of optimal stopping rules for Monte Carlo simulation to obtain an inference +approximation with relative error epsilon and a small failure probability +delta. We present an empirical evaluation of the algorithms which demonstrates +their improved performance. +",Optimal Monte Carlo Estimation of Belief Network Inference +" Directed acyclic graphs have been used fruitfully to represent causal +strucures (Pearl 1988). However, in the social sciences and elsewhere models +are often used which correspond both causally and statistically to directed +graphs with directed cycles (Spirtes 1995). Pearl (1993) discussed predicting +the effects of intervention in models of this kind, so-called linear +non-recursive structural equation models. This raises the question of whether +it is possible to make inferences about causal structure with cycles, form +sample data. In particular do there exist general, informative, feasible and +reliable precedures for inferring causal structure from conditional +independence relations among variables in a sample generated by an unknown +causal structure? In this paper I present a discovery algorithm that is correct +in the large sample limit, given commonly (but often implicitly) made plausible +assumptions, and which provides information about the existence or +non-existence of causal pathways from one variable to another. The algorithm is +polynomial on sparse graphs. +",A Discovery Algorithm for Directed Cyclic Graphs +" Although the concept of d-separation was originally defined for directed +acyclic graphs (see Pearl 1988), there is a natural extension of he concept to +directed cyclic graphs. When exactly the same set of d-separation relations +hold in two directed graphs, no matter whether respectively cyclic or acyclic, +we say that they are Markov equivalent. In other words, when two directed +cyclic graphs are Markov equivalent, the set of distributions that satisfy a +natural extension of the Global Directed Markov condition (Lauritzen et al. +1990) is exactly the same for each graph. There is an obvious exponential (in +the number of vertices) time algorithm for deciding Markov equivalence of two +directed cyclic graphs; simply chech all of the d-separation relations in each +graph. In this paper I state a theorem that gives necessary and sufficient +conditions for the Markov equivalence of two directed cyclic graphs, where each +of the conditions can be checked in polynomial time. Hence, the theorem can be +easily adapted into a polynomial time algorithm for deciding the Markov +equivalence of two directed cyclic graphs. Although space prohibits inclusion +of correctness proofs, they are fully described in Richardson (1994b). +","A Polynomial-Time Algorithm for Deciding Markov Equivalence of Directed + Cyclic Graphical Models" +" SPIRIT is an expert system shell for probabilistic knowledge bases. Knowledge +acquisition is performed by processing facts and rules on discrete variables in +a rich syntax. The shell generates a probability distribution which respects +all acquired facts and rules and which maximizes entropy. The user-friendly +devices of SPIRIT to define variables, formulate rules and create the knowledge +base are revealed in detail. Inductive learning is possible. Medium sized +applications show the power of the system. +",Coherent Knowledge Processing at Maximum Entropy by SPIRIT +" Belief updating in Bayes nets, a well known computationally hard problem, has +recently been approximated by several deterministic algorithms, and by various +randomized approximation algorithms. Deterministic algorithms usually provide +probability bounds, but have an exponential runtime. Some randomized schemes +have a polynomial runtime, but provide only probability estimates. We present +randomized algorithms that enumerate high-probability partial instantiations, +resulting in probability bounds. Some of these algorithms are also sampling +algorithms. Specifically, we introduce and evaluate a variant of backward +sampling, both as a sampling algorithm and as a randomized enumeration +algorithm. We also relax the implicit assumption used by both sampling and +accumulation algorithms, that query nodes must be instantiated in all the +samples. +",Sample-and-Accumulate Algorithms for Belief Updating in Bayes Networks +" We propose a decision-analytical approach to comparing the flexibility of +decision situations from the perspective of a decision-maker who exhibits +constant risk-aversion over a monetary value model. Our approach is simple yet +seems to be consistent with a variety of flexibility concepts, including robust +and adaptive alternatives. We try to compensate within the model for +uncertainty that was not anticipated or not modeled. This approach not only +allows one to compare the flexibility of plans, but also guides the search for +new, more flexible alternatives. +",A Measure of Decision Flexibility +" The main goal of this paper is to describe a data structure called binary +join trees that are useful in computing multiple marginals efficiently using +the Shenoy-Shafer architecture. We define binary join trees, describe their +utility, and sketch a procedure for constructing them. +",Binary Join Trees +" Over the past several years Bayesian networks have been applied to a wide +variety of problems. A central problem in applying Bayesian networks is that of +finding one or more of the most probable instantiations of a network. In this +paper we develop an efficient algorithm that incrementally enumerates the +instantiations of a Bayesian network in decreasing order of probability. Such +enumeration algorithms are applicable in a variety of applications ranging from +medical expert systems to model-based diagnosis. Fundamentally, our algorithm +is simply performing a lazy enumeration of the sorted list of all +instantiations of the network. This insight leads to a very concise algorithm +statement which is both easily understood and implemented. We show that for +singly connected networks, our algorithm generates the next instantiation in +time polynomial in the size of the network. The algorithm extends to arbitrary +Bayesian networks using standard conditioning techniques. We empirically +evaluate the enumeration algorithm and demonstrate its practicality. +",Efficient Enumeration of Instantiations in Bayesian Networks +" Chain graphs give a natural unifying point of view on Markov and Bayesian +networks and enlarge the potential of graphical models for description of +conditional independence structures. In the paper a direct graphical separation +criterion for chain graphs, called c-separation, which generalizes the +d-separation criterion for Bayesian networks is introduced (recalled). It is +equivalent to the classic moralization criterion for chain graphs and complete +in sense that for every chain graph there exists a probability distribution +satisfying exactly conditional independencies derivable from the chain graph by +the c-separation criterion. Every class of Markov equivalent chain graphs can +be uniquely described by a natural representative, called the largest chain +graph. A recovery algorithm, which on basis of the (conditional) dependency +model induced by an unknown chain graph finds the corresponding largest chain +graph, is presented. +",On Separation Criterion and Recovery Algorithm for Chain Graphs +" When we work with information from multiple sources, the formalism each +employs to handle uncertainty may not be uniform. In order to be able to +combine these knowledge bases of different formats, we need to first establish +a common basis for characterizing and evaluating the different formalisms, and +provide a semantics for the combined mechanism. A common framework can provide +an infrastructure for building an integrated system, and is essential if we are +to understand its behavior. We present a unifying framework based on an ordered +partition of possible worlds called partition sequences, which corresponds to +our intuitive notion of biasing towards certain possible scenarios when we are +uncertain of the actual situation. We show that some of the existing +formalisms, namely, default logic, autoepistemic logic, probabilistic +conditioning and thresholding (generalized conditioning), and possibility +theory can be incorporated into this general framework. +","Possible World Partition Sequences: A Unifying Framework for Uncertain + Reasoning" +" Integrating diagnosis and repair is particularly crucial when gaining +sufficient information to discriminate between several candidate diagnoses +requires carrying out some repair actions. A typical case is supply restoration +in a faulty power distribution system. This problem, which is a major concern +for electricity distributors, features partial observability, and stochastic +repair actions which are more elaborate than simple replacement of components. +This paper analyses the difficulties in applying existing work on integrating +model-based diagnosis and repair and on planning in partially observable +stochastic domains to this real-world problem, and describes the pragmatic +approach we have retained so far. +","Supply Restoration in Power Distribution Systems - A Case Study in + Integrating Model-Based Diagnosis and Repair Planning" +" For real time evaluation of a Bayesian network when there is not sufficient +time to obtain an exact solution, a guaranteed response time, approximate +solution is required. It is shown that nontraditional methods utilizing +estimators based on an archive of trial solutions and genetic search can +provide an approximate solution that is considerably superior to the +traditional Monte Carlo simulation methods. +",Real Time Estimation of Bayesian Networks +" Axiomatization has been widely used for testing logical implications. This +paper suggests a non-axiomatic method, the chase, to test if a new dependency +follows from a given set of probabilistic dependencies. Although the chase +computation may require exponential time in some cases, this technique is a +powerful tool for establishing nontrivial theoretical results. More +importantly, this approach provides valuable insight into the intriguing +connection between relational databases and probabilistic reasoning systems. +",Testing Implication of Probabilistic Dependencies +" We examine a standard factory scheduling problem with stochastic processing +and setup times, minimizing the expectation of the weighted number of tardy +jobs. Because the costs of operators in the schedule are stochastic and +sequence dependent, standard dynamic programming algorithms such as A* may fail +to find the optimal schedule. The SDA* (Stochastic Dominance A*) algorithm +remedies this difficulty by relaxing the pruning condition. We present an +improved state-space search formulation for these problems and discuss the +conditions under which stochastic scheduling problems can be solved optimally +using SDA*. In empirical testing on randomly generated problems, we found that +in 70%, the expected cost of the optimal stochastic solution is lower than that +of the solution derived using a deterministic approximation, with comparable +search effort. +",Optimal Factory Scheduling using Stochastic Dominance A* +" In learning belief networks, the single link lookahead search is widely +adopted to reduce the search space. We show that there exists a class of +probabilistic domain models which displays a special pattern of dependency. We +analyze the behavior of several learning algorithms using different scoring +metrics such as the entropy, conditional independence, minimal description +length and Bayesian metrics. We demonstrate that single link lookahead search +procedures (employed in these algorithms) cannot learn these models correctly. +Thus, when the underlying domain model actually belongs to this class, the use +of a single link search procedure will result in learning of an incorrect +model. This may lead to inference errors when the model is used. Our analysis +suggests that if the prior knowledge about a domain does not rule out the +possible existence of these models, a multi-link lookahead search or other +heuristics should be used for the learning process. +",Critical Remarks on Single Link Search in Learning Belief Networks +" We extend the theory of d-separation to cases in which data instances are not +independent and identically distributed. We show that applying the rules of +d-separation directly to the structure of probabilistic models of relational +data inaccurately infers conditional independence. We introduce relational +d-separation, a theory for deriving conditional independence facts from +relational models. We provide a new representation, the abstract ground graph, +that enables a sound, complete, and computationally efficient method for +answering d-separation queries about relational models, and we present +empirical results that demonstrate effectiveness. +",Reasoning about Independence in Probabilistic Models of Relational Data +" Probabilistic independence can dramatically simplify the task of eliciting, +representing, and computing with probabilities in large domains. A key +technique in achieving these benefits is the idea of graphical modeling. We +survey existing notions of independence for utility functions in a +multi-attribute space, and suggest that these can be used to achieve similar +advantages. Our new results concern conditional additive independence, which we +show always has a perfect representation as separation in an undirected graph +(a Markov network). Conditional additive independencies entail a particular +functional for the utility function that is analogous to a product +decomposition of a probability function, and confers analogous benefits. This +functional form has been utilized in the Bayesian network and influence diagram +literature, but generally without an explanation in terms of independence. The +functional form yields a decomposition of the utility function that can greatly +speed up expected utility calculations, particularly when the utility graph has +a similar topology to the probabilistic network being used. +",Graphical Models for Preference and Utility +" Evaluation of counterfactual queries (e.g., ""If A were true, would C have +been true?"") is important to fault diagnosis, planning, determination of +liability, and policy analysis. We present a method of revaluating +counterfactuals when the underlying causal model is represented by structural +models - a nonlinear generalization of the simultaneous equations models +commonly used in econometrics and social sciences. This new method provides a +coherent means for evaluating policies involving the control of variables +which, prior to enacting the policy were influenced by other variables in the +system. +",Counterfactuals and Policy Analysis in Structural Models +" We present a new approach to dealing with default information based on the +theory of belief functions. Our semantic structures, inspired by Adams' +epsilon-semantics, are epsilon-belief assignments, where values committed to +focal elements are either close to 0 or close to 1. We define two systems based +on these structures, and relate them to other non-monotonic systems presented +in the literature. We show that our second system correctly addresses the +well-known problems of specificity, irrelevance, blocking of inheritance, +ambiguity, and redundancy. +",Belief Functions and Default Reasoning +" We describe an application of belief networks to the diagnosis of bottlenecks +in computer systems. The technique relies on a high-level functional model of +the interaction between application workloads, the Windows NT operating system, +and system hardware. Given a workload description, the model predicts the +values of observable system counters available from the Windows NT performance +monitoring tool. Uncertainty in workloads, predictions, and counter values are +characterized with Gaussian distributions. During diagnostic inference, we use +observed performance monitor values to find the most probable assignment to the +workload parameters. In this paper we provide some background on automated +bottleneck detection, describe the structure of the system model, and discuss +empirical procedures for model calibration and verification. Part of the +calibration process includes generating a dataset to estimate a multivariate +Gaussian error model. Initial results in diagnosing bottlenecks are presented. +",Automating Computer Bottleneck Detection with Belief Nets +" Chain graphs combine directed and undirected graphs and their underlying +mathematics combines properties of the two. This paper gives a simplified +definition of chain graphs based on a hierarchical combination of Bayesian +(directed) and Markov (undirected) networks. Examples of a chain graph are +multivariate feed-forward networks, clustering with conditional interaction +between variables, and forms of Bayes classifiers. Chain graphs are then +extended using the notation of plates so that samples and data analysis +problems can be represented in a graphical model as well. Implications for +learning are discussed in the conclusion. +",Chain Graphs for Learning +" We can perform inference in Bayesian belief networks by enumerating +instantiations with high probability thus approximating the marginals. In this +paper, we present a method for determining the fraction of instantiations that +has to be considered such that the absolute error in the marginals does not +exceed a predefined value. The method is based on extreme value theory. +Essentially, the proposed method uses the reversed generalized Pareto +distribution to model probabilities of instantiations below a given threshold. +Based on this distribution, an estimate of the maximal absolute error if +instantiations with probability smaller than u are disregarded can be made. +",Error Estimation in Approximate Bayesian Belief Network Inference +" The aim of this paper is to present a method for identifying the structure of +a rule in a fuzzy model. For this purpose, an ATMS shall be used (Zurita 1994). +An algorithm obtaining the identification of the structure will be suggested +(Castro 1995). The minimal structure of the rule (with respect to the number of +variables that must appear in the rule) will be found by this algorithm. +Furthermore, the identification parameters shall be obtained simultaneously. +The proposed method shall be applied for classification to an example. The {em +Iris Plant Database} shall be learnt for all three kinds of plants. +",Generating the Structure of a Fuzzy Rule under Uncertainty +" An approach to fault isolation that exploits vastly incomplete models is +presented. It relies on separate descriptions of each component behavior, +together with the links between them, which enables focusing of the reasoning +to the relevant part of the system. As normal observations do not need +explanation, the behavior of the components is limited to anomaly propagation. +Diagnostic solutions are disorders (fault modes or abnormal signatures) that +are consistent with the observations, as well as abductive explanations. An +ordinal representation of uncertainty based on possibility theory provides a +simple exception-tolerant description of the component behaviors. We can for +instance distinguish between effects that are more or less certainly present +(or absent) and effects that are more or less certainly present (or absent) +when a given anomaly is present. A realistic example illustrates the benefits +of this approach. +","Practical Model-Based Diagnosis with Qualitative Possibilistic + Uncertainty" +" The development of new methods and representations for temporal +decision-making requires a principled basis for characterizing and measuring +the flexibility of decision strategies in the face of uncertainty. Our goal in +this paper is to provide a framework - not a theory - for observing how +decision policies behave in the face of informational perturbations, to gain +clues as to how they might behave in the face of unanticipated, possibly +unarticulated uncertainties. To this end, we find it beneficial to distinguish +between two types of uncertainty: ""Small World"" and ""Large World"" uncertainty. +The first type can be resolved by posing an unambiguous question to a +""clairvoyant,"" and is anchored on some well-defined aspect of a decision frame. +The second type is more troublesome, yet it is often of greater interest when +we address the issue of flexibility; this type of uncertainty can be resolved +only by consulting a ""psychic."" We next observe that one approach to +flexibility used in the economics literature is already implicitly accounted +for in the Maximum Expected Utility (MEU) principle from decision theory. +Though simple, the observation establishes the context for a more illuminating +notion of flexibility, what we term flexibility with respect to information +revelation. We show how to perform flexibility analysis of a static (i.e., +single period) decision problem using a simple example, and we observe that the +most flexible alternative thus identified is not necessarily the MEU +alternative. We extend our analysis for a dynamic (i.e., multi-period) model, +and we demonstrate how to calculate the value of flexibility for decision +strategies that allow downstream revision of an upstream commitment decision. +",Decision Flexibility +" We present a simple characterization of equivalent Bayesian network +structures based on local transformations. The significance of the +characterization is twofold. First, we are able to easily prove several new +invariant properties of theoretical interest for equivalent structures. Second, +we use the characterization to derive an efficient algorithm that identifies +all of the compelled edges in a structure. Compelled edge identification is of +particular importance for learning Bayesian network structures from data +because these edges indicate causal relationships when certain assumptions +hold. +","A Transformational Characterization of Equivalent Bayesian Network + Structures" +" We present two algorithms for exact and approximate inference in causal +networks. The first algorithm, dynamic conditioning, is a refinement of cutset +conditioning that has linear complexity on some networks for which cutset +conditioning is exponential. The second algorithm, B-conditioning, is an +algorithm for approximate inference that allows one to trade-off the quality of +approximations with the computation time. We also present some experimental +results illustrating the properties of the proposed algorithms. +","Conditioning Methods for Exact and Approximate Inference in Causal + Networks" +" In this paper we study different concepts of independence for convex sets of +probabilities. There will be two basic ideas for independence. The first is +irrelevance. Two variables are independent when a change on the knowledge about +one variable does not affect the other. The second one is factorization. Two +variables are independent when the joint convex set of probabilities can be +decomposed on the product of marginal convex sets. In the case of the Theory of +Probability, these two starting points give rise to the same definition. In the +case of convex sets of probabilities, the resulting concepts will be strongly +related, but they will not be equivalent. As application of the concept of +independence, we shall consider the problem of building a global convex set +from marginal convex sets of probabilities. +",Independence Concepts for Convex Sets of Probabilities +" The undirected technique for evaluating belief networks [Jensen, et.al., +1990, Lauritzen and Spiegelhalter, 1988] requires clustering the nodes in the +network into a junction tree. In the traditional view, the junction tree is +constructed from the cliques of the moralized and triangulated belief network: +triangulation is taken to be the primitive concept, the goal towards which any +clustering algorithm (e.g. node elimination) is directed. In this paper, we +present an alternative conception of clustering, in which clusters and the +junction tree property play the role of primitives: given a graph (not a tree) +of clusters which obey (a modified version of) the junction tree property, we +transform this graph until we have obtained a tree. There are several +advantages to this approach: it is much clearer and easier to understand, which +is important for humans who are constructing belief networks; it admits a wider +range of heuristics which may enable more efficient or superior clustering +algorithms; and it serves as the natural basis for an incremental clustering +scheme, which we describe. +",Clustering Without (Thinking About) Triangulation +" Bayesian networks provide a method of representing conditional independence +between random variables and computing the probability distributions associated +with these random variables. In this paper, we extend Bayesian network +structures to compute probability density functions for continuous random +variables. We make this extension by approximating prior and conditional +densities using sums of weighted Gaussian distributions and then finding the +propagation rules for updating the densities in terms of these weights. We +present a simple example that illustrates the Bayesian network for continuous +variables; this example shows the effect of the network structure and +approximation errors on the computation of densities for variables in the +network. +","Implementation of Continuous Bayesian Networks Using Sums of Weighted + Gaussians" +" Although the usefulness of belief networks for reasoning under uncertainty is +widely accepted, obtaining numerical probabilities that they require is still +perceived a major obstacle. Often not enough statistical data is available to +allow for reliable probability estimation. Available information may not be +directly amenable for encoding in the network. Finally, domain experts may be +reluctant to provide numerical probabilities. In this paper, we propose a +method for elicitation of probabilities from a domain expert that is +non-invasive and accommodates whatever probabilistic information the expert is +willing to state. We express all available information, whether qualitative or +quantitative in nature, in a canonical form consisting of (in) equalities +expressing constraints on the hyperspace of possible joint probability +distributions. We then use this canonical form to derive second-order +probability distributions over the desired probabilities. +","Elicitation of Probabilities for Belief Networks: Combining Qualitative + and Quantitative Information" +" Accepting a proposition means that our confidence in this proposition is +strictly greater than the confidence in its negation. This paper investigates +the subclass of uncertainty measures, expressing confidence, that capture the +idea of acceptance, what we call acceptance functions. Due to the monotonicity +property of confidence measures, the acceptance of a proposition entails the +acceptance of any of its logical consequences. In agreement with the idea that +a belief set (in the sense of Gardenfors) must be closed under logical +consequence, it is also required that the separate acceptance o two +propositions entail the acceptance of their conjunction. Necessity (and +possibility) measures agree with this view of acceptance while probability and +belief functions generally do not. General properties of acceptance functions +are estabilished. The motivation behind this work is the investigation of a +setting for belief revision more general than the one proposed by Alchourron, +Gardenfors and Makinson, in connection with the notion of conditioning. +",Numerical Representations of Acceptance +" The fraud/uncollectible debt problem in the telecommunications industry +presents two technical challenges: the detection and the treatment of the +account given the detection. In this paper, we focus on the first problem of +detection using Bayesian network models, and we briefly discuss the application +of a normative expert system for the treatment at the end. We apply Bayesian +network models to the problem of fraud/uncollectible debt detection for +telecommunication services. In addition to being quite successful at predicting +rare event outcomes, it is able to handle a mixture of categorical and +continuous data. We present a performance comparison using linear and +non-linear discriminant analysis, classification and regression trees, and +Bayesian network models +","Fraud/Uncollectible Debt Detection Using a Bayesian Network Based + Learning System: A Rare Binary Outcome with Mixed Data Structures" +" The Constraint Satisfaction Problem (CSP) framework offers a simple and sound +basis for representing and solving simple decision problems, without +uncertainty. This paper is devoted to an extension of the CSP framework +enabling us to deal with some decisions problems under uncertainty. This +extension relies on a differentiation between the agent-controllable decision +variables and the uncontrollable parameters whose values depend on the +occurrence of uncertain events. The uncertainty on the values of the parameters +is assumed to be given under the form of a probability distribution. Two +algorithms are given, for computing respectively decisions solving the problem +with a maximal probability, and conditional decisions mapping the largest +possible amount of possible cases to actual decisions. +",A Constraint Satisfaction Approach to Decision under Uncertainty +" We examine a new approach to modeling uncertainty based on plausibility +measures, where a plausibility measure just associates with an event its +plausibility, an element is some partially ordered set. This approach is easily +seen to generalize other approaches to modeling uncertainty, such as +probability measures, belief functions, and possibility measures. The lack of +structure in a plausibility measure makes it easy for us to add structure on an +""as needed"" basis, letting us examine what is required to ensure that a +plausibility measure has certain properties of interest. This gives us insight +into the essential features of the properties in question, while allowing us to +prove general results that apply to many approaches to reasoning about +uncertainty. Plausibility measures have already proved useful in analyzing +default reasoning. In this paper, we examine their ""algebraic properties,"" +analogues to the use of + and * in probability theory. An understanding of such +properties will be essential if plausibility measures are to be used in +practice as a representation tool. +",Plausibility Measures: A User's Guide +" This paper concerns the probabilistic evaluation of the effects of actions in +the presence of unmeasured variables. We show that the identification of causal +effect between a singleton variable X and a set of variables Y can be +accomplished systematically, in time polynomial in the number of variables in +the graph. When the causal effect is identifiable, a closed-form expression can +be obtained for the probability that the action will achieve a specified goal, +or a set of goals. +",Testing Identifiability of Causal Effects +" We present an algorithm, called Predict, for updating beliefs in causal +networks quantified with order-of-magnitude probabilities. The algorithm takes +advantage of both the structure and the quantification of the network and +presents a polynomial asymptotic complexity. Predict exhibits a conservative +behavior in that it is always sound but not always complete. We provide +sufficient conditions for completeness and present algorithms for testing these +conditions and for computing a complete set of plausible values. We propose +Predict as an efficient method to estimate probabilistic values and illustrate +its use in conjunction with two known algorithms for probabilistic inference. +Finally, we describe an application of Predict to plan evaluation, present +experimental results, and discuss issues regarding its use with conditional +logics of belief, and in the characterization of irrelevance. +",Fast Belief Update Using Order-of-Magnitude Probabilities +" We show how to transform any set of prioritized propositional defaults into +an equivalent set of parallel (i.e., unprioritized) defaults, in +circumscription. We give an algorithm to implement the transform. We show how +to use the transform algorithm as a generator of a whole family of inferencing +algorithms for circumscription. The method is to employ the transform algorithm +as a front end to any inferencing algorithm, e.g., one of the previously +available, that handles the parallel (empty) case of prioritization. Our +algorithms provide not just coverage of a new expressive class, but also +alternatives to previous algorithms for implementing the previously covered +class (?layered?) of prioritization. In particular, we give a new +query-answering algorithm for prioritized cirumscription which is sound and +complete for the full expressive class of unrestricted finite prioritization +partial orders, for propositional defaults (or minimized predicates). By +contrast, previous algorithms required that the prioritization partial order be +layered, i.e., structured similar to the system of rank in the military. Our +algorithm enables, for the first time, the implementation of the most useful +class of prioritization: non-layered prioritization partial orders. Default +inheritance, for example, typically requires non-layered prioritization to +represent specificity adequately. Our algorithm enables not only the +implementation of default inheritance (and specificity) within prioritized +circumscription, but also the extension and combination of default inheritance +with other kinds of prioritized default reasoning, e.g.: with stratified logic +programs with negation-as-failure. Such logic programs are previously known to +be representable equivalently as layered-priority predicate circumscriptions. +Worst-case, the transform increases the number of defaults exponentially. We +discuss how inferencing is practically implementable nevertheless in two kinds +of situations: general expressiveness but small numbers of defaults, or +expressive special cases with larger numbers of defaults. One such expressive +special case is non-?top-heaviness? of the prioritization partial order. In +addition to its direct implementation, the transform can also be exploited +analytically to generate special case algorithms, e.g., a tractable transform +for a class within default inheritance (detailed in another, forthcoming +paper). We discuss other aspects of the significance of the fundamental result. +One can view the transform as reducing n degrees of partially ordered belief +confidence to just 2 degrees of confidence: for-sure and (unprioritized) +default. Ordinary, parallel default reasoning, e.g., in parallel +circumscription or Poole's Theorist, can be viewed in these terms as reducing 2 +degrees of confidence to just 1 degree of confidence: that of the non-monotonic +theory's conclusions. The expressive reduction's computational complexity +suggests that prioritization is valuable for its expressive conciseness, just +as defaults are for theirs. For Reiter's Default Logic and Poole's Theorist, +the transform implies how to extend those formalisms so as to equip them with a +concept of prioritization that is exactly equivalent to that in +circumscription. This provides an interesting alternative to Brewka's approach +to equipping them with prioritization-type precedence. +",Transforming Prioritized Defaults and Specificity into Parallel Defaults +" This paper discusses techniques for performing efficient decision-theoretic +planning. We give an overview of the DRIPS decision-theoretic refinement +planning system, which uses abstraction to efficiently identify optimal plans. +We present techniques for automatically generating search control information, +which can significantly improve the planner's performance. We evaluate the +efficiency of DRIPS both with and without the search control rules on a complex +medical planning problem and compare its performance to that of a +branch-and-bound decision tree algorithm. +",Efficient Decision-Theoretic Planning: Techniques and Empirical Analysis +" In this paper we deal with a new approach to probabilistic reasoning in a +logical framework. Nearly almost all logics of probability that have been +proposed in the literature are based on classical two-valued logic. After +making clear the differences between fuzzy logic and probability theory, here +we propose a {em fuzzy} logic of probability for which completeness results (in +a probabilistic sense) are provided. The main idea behind this approach is that +probability values of crisp propositions can be understood as truth-values of +some suitable fuzzy propositions associated to the crisp ones. Moreover, +suggestions and examples of how to extend the formalism to cope with +conditional probabilities and with other uncertainty formalisms are also +provided. +",Fuzzy Logic and Probability +" This paper presents a probabilistic model for reasoning about the state of a +system as it changes over time, both due to exogenous and endogenous +influences. Our target domain is a class of medical prediction problems that +are neither so urgent as to preclude careful diagnosis nor progress so slowly +as to allow arbitrary testing and treatment options. In these domains there is +typically enough time to gather information about the patient's state and +consider alternative diagnoses and treatments, but the temporal interaction +between the timing of tests, treatments, and the course of the disease must +also be considered. Our approach is to elicit a qualitative structural model of +the patient from a human expert---the model identifies important attributes, +the way in which exogenous changes affect attribute values, and the way in +which the patient's condition changes endogenously. We then elicit +probabilistic information to capture the expert's uncertainty about the effects +of tests and treatments and the nature and timing of endogenous state changes. +This paper describes the model in the context of a problem in treating vehicle +accident trauma, and suggests a method for solving the model based on the +technique of sequential imputation. A complementary goal of this work is to +understand and synthesize a disparate collection of research efforts all using +the name ?probabilistic temporal reasoning.? This paper analyzes related work +and points out essential differences between our proposed model and other +approaches in the literature. +",Probabilistic Temporal Reasoning with Endogenous Change +" This is a working paper summarizing results of an ongoing research project +whose aim is to uniquely characterize the uncertainty measure for the +Dempster-Shafer Theory. A set of intuitive axiomatic requirements is presented, +some of their implications are shown, and the proof is given of the minimality +of recently proposed measure AU among all measures satisfying the proposed +requirements. +","Toward a Characterization of Uncertainty Measure for the Dempster-Shafer + Theory" +" We present a precise definition of cause and effect in terms of a fundamental +notion called unresponsiveness. Our definition is based on Savage's (1954) +formulation of decision theory and departs from the traditional view of +causation in that our causal assertions are made relative to a set of +decisions. An important consequence of this departure is that we can reason +about cause locally, not requiring a causal explanation for every dependency. +Such local reasoning can be beneficial because it may not be necessary to +determine whether a particular dependency is causal to make a decision. Also in +this paper, we examine the graphical encoding of causal relationships. We show +that influence diagrams in canonical form are an accurate and efficient +representation of causal relationships. In addition, we establish a +correspondence between canonical form and Pearl's causal theory. +",A Definition and Graphical Representation for Causality +" We examine Bayesian methods for learning Bayesian networks from a combination +of prior knowledge and statistical data. In particular, we unify the approaches +we presented at last year's conference for discrete and Gaussian domains. We +derive a general Bayesian scoring metric, appropriate for both domains. We then +use this metric in combination with well-known statistical facts about the +Dirichlet and normal--Wishart distributions to derive our metrics for discrete +and Gaussian domains. +","Learning Bayesian Networks: A Unification for Discrete and Gaussian + Domains" +" Whereas acausal Bayesian networks represent probabilistic independence, +causal Bayesian networks represent causal relationships. In this paper, we +examine Bayesian methods for learning both types of networks. Bayesian methods +for learning acausal networks are fairly well developed. These methods often +employ assumptions to facilitate the construction of priors, including the +assumptions of parameter independence, parameter modularity, and likelihood +equivalence. We show that although these assumptions also can be appropriate +for learning causal networks, we need additional assumptions in order to learn +causal networks. We introduce two sufficient assumptions, called {em mechanism +independence} and {em component independence}. We show that these new +assumptions, when combined with parameter independence, parameter modularity, +and likelihood equivalence, allow us to apply methods for learning acausal +networks to learn causal networks. +",A Bayesian Approach to Learning Causal Networks +" We describe methods for managing the complexity of information displayed to +people responsible for making high-stakes, time-critical decisions. The +techniques provide tools for real-time control of the configuration and +quantity of information displayed to a user, and a methodology for designing +flexible human-computer interfaces for monitoring applications. After defining +a prototypical set of display decision problems, we introduce the expected +value of revealed information (EVRI) and the related measure of expected value +of displayed information (EVDI). We describe how these measures can be used to +enhance computer displays used for monitoring complex systems. We motivate the +presentation by discussing our efforts to employ decision-theoretic control of +displays for a time-critical monitoring application at the NASA Mission Control +Center in Houston. +",Display of Information for Time-Critical Decision Making +" In earlier work, we introduced flexible inference and decision-theoretic +metareasoning to address the intractability of normative inference. Here, +rather than pursuing the task of computing beliefs and actions with decision +models composed of distinctions about uncertain events, we examine methods for +inferring beliefs about mathematical truth before an automated theorem prover +completes a proof. We employ a Bayesian analysis to update belief in truth, +given theorem-proving progress, and show how decision-theoretic methods can be +used to determine the value of continuing to deliberate versus taking immediate +action in time-critical situations. +","Reasoning, Metareasoning, and Mathematical Truth: Studies of Theorem + Proving under Limited Resources" +" Bayesian networks offer great potential for use in automating large scale +diagnostic reasoning tasks. Gibbs sampling is the main technique used to +perform diagnostic reasoning in large richly interconnected Bayesian networks. +Unfortunately Gibbs sampling can take an excessive time to generate a +representative sample. In this paper we describe and test a number of heuristic +strategies for improving sampling in noisy-or Bayesian networks. The strategies +include Monte Carlo Markov chain sampling techniques other than Gibbs sampling. +Emphasis is put on strategies that can be implemented in distributed systems. +",Improved Sampling for Diagnostic Reasoning in Bayesian Networks +" Consider the situation where some evidence e has been entered to a Bayesian +network. When performing conflict analysis, sensitivity analysis, or when +answering questions like ""What if the finding on X had been y instead of x?"" +you need probabilities P (e'| h), where e' is a subset of e, and h is a +configuration of a (possibly empty) set of variables. Cautious propagation is a +modification of HUGIN propagation into a Shafer-Shenoy-like architecture. It is +less efficient than HUGIN propagation; however, it provides easy access to P +(e'| h) for a great deal of relevant subsets e'. +",Cautious Propagation in Bayesian Networks +" In this paper we extend the influence diagram (ID) representation for +decisions under uncertainty. In the standard ID, arrows into a decision node +are only informational; they do not represent constraints on what the decision +maker can do. We can represent such constraints only indirectly, using arrows +to the children of the decision and sometimes adding more variables to the +influence diagram, thus making the ID more complicated. Users of influence +diagrams often want to represent constraints by arrows into decision nodes. We +represent constraints on decisions by allowing relevance arrows into decision +nodes. We call the resulting representation information/relevance influence +diagrams (IRIDs). Information/relevance influence diagrams allow for direct +representation and specification of constrained decisions. We use a combination +of stochastic dynamic programming and Gibbs sampling to solve IRIDs. This +method is especially useful when exact methods for solving IDs fail. +",Information/Relevance Influence Diagrams +" Stochastic simulation algorithms such as likelihood weighting often give +fast, accurate approximations to posterior probabilities in probabilistic +networks, and are the methods of choice for very large networks. Unfortunately, +the special characteristics of dynamic probabilistic networks (DPNs), which are +used to represent stochastic temporal processes, mean that standard simulation +algorithms perform very poorly. In essence, the simulation trials diverge +further and further from reality as the process is observed over time. In this +paper, we present simulation algorithms that use the evidence observed at each +time step to push the set of trials back towards reality. The first algorithm, +""evidence reversal"" (ER) restructures each time slice of the DPN so that the +evidence nodes for the slice become ancestors of the state variables. The +second algorithm, called ""survival of the fittest"" sampling (SOF), +""repopulates"" the set of trials at each time step using a stochastic +reproduction rate weighted by the likelihood of the evidence according to each +trial. We compare the performance of each algorithm with likelihood weighting +on the original network, and also investigate the benefits of combining the ER +and SOF methods. The ER/SOF combination appears to maintain bounded error +independent of the number of time steps in the simulation. +",Stochastic Simulation Algorithms for Dynamic Probabilistic Networks +" Sequential decision tasks with incomplete information are characterized by +the exploration problem; namely the trade-off between further exploration for +learning more about the environment and immediate exploitation of the accrued +information for decision-making. Within artificial intelligence, there has been +an increasing interest in studying planning-while-learning algorithms for these +decision tasks. In this paper we focus on the exploration problem in +reinforcement learning and Q-learning in particular. The existing exploration +strategies for Q-learning are of a heuristic nature and they exhibit limited +scaleability in tasks with large (or infinite) state and action spaces. +Efficient experimentation is needed for resolving uncertainties when possible +plans are compared (i.e. exploration). The experimentation should be sufficient +for selecting with statistical significance a locally optimal plan (i.e. +exploitation). For this purpose, we develop a probabilistic hill-climbing +algorithm that uses a statistical selection procedure to decide how much +exploration is needed for selecting a plan which is, with arbitrarily high +probability, arbitrarily close to a locally optimal one. Due to its generality +the algorithm can be employed for the exploration strategy of robust +Q-learning. An experiment on a relatively complex control task shows that the +proposed exploration strategy performs better than a typical exploration +strategy. +",Probabilistic Exploration in Planning while Learning +" An important issue in the use of expert systems is the so-called brittleness +problem. Expert systems model only a limited part of the world. While the +explicit management of uncertainty in expert systems itigates the brittleness +problem, it is still possible for a system to be used, unwittingly, in ways +that the system is not prepared to address. Such a situation may be detected by +the method of straw models, first presented by Jensen et al. [1990] and later +generalized and justified by Laskey [1991]. We describe an algorithm, which we +have implemented, that takes as input an annotated diagnostic Bayesian network +(the base model) and constructs, without assistance, a bipartite network to be +used as a straw model. We show that in some cases this straw model is better +that the independent straw model of Jensen et al., the only other straw model +for which a construction algorithm has been designed and implemented. +","On the Detection of Conflicts in Diagnostic Bayesian Networks Using + Abstraction" +" Dawid, Kjaerulff and Lauritzen (1994) provided a preliminary description of a +hybrid between Monte-Carlo sampling methods and exact local computations in +junction trees. Utilizing the strengths of both methods, such hybrid inference +methods has the potential of expanding the class of problems which can be +solved under bounded resources as well as solving problems which otherwise +resist exact solutions. The paper provides a detailed description of a +particular instance of such a hybrid scheme; namely, combination of exact +inference and Gibbs sampling in discrete Bayesian networks. We argue that this +combination calls for an extension of the usual message passing scheme of +ordinary junction trees. +",HUGS: Combining Exact Inference and Gibbs Sampling in Junction Trees +" We show an alternative way of representing a Bayesian belief network by +sensitivities and probability distributions. This representation is equivalent +to the traditional representation by conditional probabilities, but makes +dependencies between nodes apparent and intuitively easy to understand. We also +propose a QR matrix representation for the sensitivities and/or conditional +probabilities which is more efficient, in both memory requirements and +computational speed, than the traditional representation for computer-based +implementations of probabilistic inference. We use sensitivities to show that +for a certain class of binary networks, the computation time for approximate +probabilistic inference with any positive upper bound on the error of the +result is independent of the size of the network. Finally, as an alternative to +traditional algorithms that use conditional probabilities, we describe an exact +algorithm for probabilistic inference that uses the QR-representation for +sensitivities and updates probability distributions of nodes in a network +according to messages from the neighbors. +","Sensitivities: An Alternative to Conditional Probabilities for Bayesian + Belief Networks" +" Classically, risk is characterized by a point value probability indicating +the likelihood of occurrence of an adverse effect. However, there are domains +where the attainability of objective numerical risk characterizations is +increasingly being questioned. This paper reviews the arguments in favour of +extending classical techniques of risk assessment to incorporate meaningful +qualitative and weak quantitative risk characterizations. A technique in which +linguistic uncertainty terms are defined in terms of patterns of argument is +then proposed. The technique is demonstrated using a prototype computer-based +system for predicting the carcinogenic risk due to novel chemical compounds. +",Is There a Role for Qualitative Risk Assessment? +" Markov decision problems (MDPs) provide the foundations for a number of +problems of interest to AI researchers studying automated planning and +reinforcement learning. In this paper, we summarize results regarding the +complexity of solving MDPs and the running time of MDP solution algorithms. We +argue that, although MDPs can be solved efficiently in theory, more study is +needed to reveal practical algorithms for solving large problems quickly. To +encourage future research, we sketch some alternative methods of analysis that +rely on the structure of MDPs. +",On the Complexity of Solving Markov Decision Problems +" This paper presents correct algorithms for answering the following two +questions; (i) Does there exist a causal explanation consistent with a set of +background knowledge which explains all of the observed independence facts in a +sample? (ii) Given that there is such a causal explanation what are the causal +relationships common to every such causal explanation? +",Causal Inference and Causal Explanation with Background Knowledge +" A completeness result for d-separation applied to discrete Bayesian networks +is presented and it is shown that in a strong measure-theoretic sense almost +all discrete distributions for a given network structure are faithful; i.e. the +independence facts true of the distribution are all and only those entailed by +the network structure. +",Strong Completeness and Faithfulness in Bayesian Networks +" We define a context-sensitive temporal probability logic for representing +classes of discrete-time temporal Bayesian networks. Context constraints allow +inference to be focused on only the relevant portions of the probabilistic +knowledge. We provide a declarative semantics for our language. We present a +Bayesian network construction algorithm whose generated networks give sound and +complete answers to queries. We use related concepts in logic programming to +justify our approach. We have implemented a Bayesian network construction +algorithm for a subset of the theory and demonstrate it's application to the +problem of evaluating the effectiveness of treatments for acute cardiac +conditions. +","A Theoretical Framework for Context-Sensitive Temporal Probability Model + Construction with Application to Plan Projection" +" In recent years there has been a spate of papers describing systems for +probabilisitic reasoning which do not use numerical probabilities. In some +cases the simple set of values used by these systems make it impossible to +predict how a probability will change or which hypothesis is most likely given +certain evidence. This paper concentrates on such situations, and suggests a +number of ways in which they may be resolved by refining the representation. +",Refining Reasoning in Qualitative Probabilistic Networks +" Certain causal models involving unmeasured variables induce no independence +constraints among the observed variables but imply, nevertheless, inequality +contraints on the observed distribution. This paper derives a general formula +for such instrumental variables, that is, exogenous variables that directly +affect some variables but not all. With the help of this formula, it is +possible to test whether a model involving instrumental variables may account +for the data, or, conversely, whether a given variables can be deemed +instrumental. +","On the Testability of Causal Models with Latent and Instrumental + Variables" +" The paper concerns the probabilistic evaluation of plans in the presence of +unmeasured variables, each plan consisting of several concurrent or sequential +actions. We establish a graphical criterion for recognizing when the effects of +a given plan can be predicted from passive observations on measured variables +only. When the criterion is satisfied, a closed-form expression is provided for +the probability that the plan will achieve a specified goal. +","Probabilistic Evaluation of Sequential Plans from Causal Models with + Hidden Variables" +" This paper introduces the independent choice logic, and in particular the +""single agent with nature"" instance of the independent choice logic, namely +ICLdt. This is a logical framework for decision making uncertainty that extends +both logic programming and stochastic models such as influence diagrams. This +paper shows how the representation of a decision problem within the independent +choice logic can be exploited to cut down the combinatorics of dynamic +programming. One of the main problems with influence diagram evaluation +techniques is the need to optimise a decision for all values of the 'parents' +of a decision variable. In this paper we show how the rule based nature of the +ICLdt can be exploited so that we only make distinctions in the values of the +information available for a decision that will make a difference to utility. +","Exploiting the Rule Structure for Decision Making within the Independent + Choice Logic" +" Bayesian belief networks are bing increasingly used as a knowledge +representation for diagnostic reasoning. One simple method for conducting +diagnostic reasoning is to represent system faults and observations only. In +this paper, we investigate how having intermediate nodes-nodes other than fault +and observation nodes affects the diagnostic performance of a Bayesian belief +network. We conducted a series of experiments on a set of real belief networks +for medical diagnosis in liver and bile disease. We compared the effects on +diagnostic performance of a two-level network consisting just of disease and +finding nodes with that of a network which models intermediate +pathophysiological disease states as well. We provide some theoretical evidence +for differences observed between the abstracted two-level network and the full +network. +","Abstraction in Belief Networks: The Role of Intermediate States in + Diagnostic Reasoning" +" Typical approaches to plan recognition start from a representation of an +agent's possible plans, and reason evidentially from observations of the +agent's actions to assess the plausibility of the various candidates. A more +expansive view of the task (consistent with some prior work) accounts for the +context in which the plan was generated, the mental state and planning process +of the agent, and consequences of the agent's actions in the world. We present +a general Bayesian framework encompassing this view, and focus on how context +can be exploited in plan recognition. We demonstrate the approach on a problem +in traffic monitoring, where the objective is to induce the plan of the driver +from observation of vehicle movements. Starting from a model of how the driver +generates plans, we show how the highway context can appropriately influence +the recognizer's interpretation of observed driver behavior. +","Accounting for Context in Plan Recognition, with Application to Traffic + Monitoring" +" The main goal of this paper is to describe a new pruning method for solving +decision trees and game trees. The pruning method for decision trees suggests a +slight variant of decision trees that we call scenario trees. In scenario +trees, we do not need a conditional probability for each edge emanating from a +chance node. Instead, we require a joint probability for each path from the +root node to a leaf node. We compare the pruning method to the traditional +rollback method for decision trees and game trees. For problems that require +Bayesian revision of probabilities, a scenario tree representation with the +pruning method is more efficient than a decision tree representation with the +rollback method. For game trees, the pruning method is more efficient than the +rollback method. +",A New Pruning Method for Solving Decision Trees and Game Trees +" The use of directed acyclic graphs (DAGs) to represent conditional +independence relations among random variables has proved fruitful in a variety +of ways. Recursive structural equation models are one kind of DAG model. +However, non-recursive structural equation models of the kinds used to model +economic processes are naturally represented by directed cyclic graphs with +independent errors, a characterization of conditional independence errors, a +characterization of conditional independence constraints is obtained, and it is +shown that the result generalizes in a natural way to systems in which the +error variables or noises are statistically dependent. For non-linear systems +with independent errors a sufficient condition for conditional independence of +variables in associated distributions is obtained. +",Directed Cyclic Graphical Representations of Feedback Models +" We show that there is a general, informative and reliable procedure for +discovering causal relations when, for all the investigator knows, both latent +variables and selection bias may be at work. Given information about +conditional independence and dependence relations between measured variables, +even when latent variables and selection bias may be present, there are +sufficient conditions for reliably concluding that there is a causal path from +one variable to another, and sufficient conditions for reliably concluding when +no such causal path exists. +",Causal Inference in the Presence of Latent Variables and Selection Bias +" Probabilistic model-based diagnosis computes the posterior probabilities of +failure of components from the prior probabilities of component failure and +observations of system behavior. One problem with this method is that such +priors are almost never directly available. One of the reasons is that the +prior probability estimates include an implicit notion of a time interval over +which they are specified -- for example, if the probability of failure of a +component is 0.05, is this over the period of a day or is this over a week? A +second problem facing probabilistic model-based diagnosis is the modeling of +persistence. Say we have an observation about a system at time t_1 and then +another observation at a later time t_2. To compute posterior probabilities +that take into account both the observations, we need some model of how the +state of the system changes from time t_1 to t_2. In this paper, we address +these problems using techniques from Reliability theory. We show how to compute +the failure prior of a component from an empirical measure of its reliability +-- the Mean Time Between Failure (MTBF). We also develop a scheme to model +persistence when handling multiple time tagged observations. +",Modeling Failure Priors and Persistence in Model-Based Diagnosis +" The goal of diagnosis is to compute good repair strategies in response to +anomalous system behavior. In a decision theoretic framework, a good repair +strategy has low expected cost. In a general formulation of the problem, the +computation of the optimal (lowest expected cost) repair strategy for a system +with multiple faults is intractable. In this paper, we consider an interesting +and natural restriction on the behavior of the system being diagnosed: (a) the +system exhibits faulty behavior if and only if one or more components is +malfunctioning. (b) The failures of the system components are independent. +Given this restriction on system behavior, we develop a polynomial time +algorithm for computing the optimal repair strategy. We then go on to introduce +a system hierarchy and the notion of inspecting (testing) components before +repair. We develop a linear time algorithm for computing an optimal repair +strategy for the hierarchical system which includes both repair and inspection. +","A Polynomial Algorithm for Computing the Optimal Repair Strategy in a + System with Independent Component Failures" +" The goal of model-based diagnosis is to isolate causes of anomalous system +behavior and recommend inexpensive repair actions in response. In general, +precomputing optimal repair policies is intractable. To date, investigators +addressing this problem have explored approximations that either impose +restrictions on the system model (such as a single fault assumption) or compute +an immediate best action with limited lookahead. In this paper, we develop a +formulation of repair in model-based diagnosis and a repair algorithm that +computes optimal sequences of actions. This optimal approach is costly but can +be applied to precompute an optimal repair strategy for compact systems. We +show how we can exploit a hierarchical system specification to make this +approach tractable for large systems. When introducing hierarchy, we also +consider the tradeoff between simply replacing a component and decomposing it +to repair its subcomponents. The hierarchical repair algorithm is suitable for +off-line precomputation of an optimal repair strategy. A modification of the +algorithm takes advantage of an iterative deepening scheme to trade off +inference time and the quality of the computed strategy. +","Exploiting System Hierarchy to Compute Repair Plans in Probabilistic + Model-based Diagnosis" +" Standard algorithms for finding the shortest path in a graph require that the +cost of a path be additive in edge costs, and typically assume that costs are +deterministic. We consider the problem of uncertain edge costs, with potential +probabilistic dependencies among the costs. Although these dependencies violate +the standard dynamic-programming decomposition, we identify a weaker stochastic +consistency condition that justifies a generalized dynamic-programming approach +based on stochastic dominance. We present a revised path-planning algorithm and +prove that it produces optimal paths under time-dependent uncertain costs. We +test the algorithm by applying it to a model of stochastic bus networks, and +present empirical performance results comparing it to some alternatives. +Finally, we consider extensions of these concepts to a more general class of +problems of heuristic search under uncertainty. +",Path Planning under Time-Dependent Uncertainty +" We develop a new semantics for defeasible inference based on extended +probability measures allowed to take infinitesimal values, on the +interpretation of defaults as generalized conditional probability constraints +and on a preferred-model implementation of entropy maximization. +","Defaults and Infinitesimals: Defeasible Inference by Nonarchimedean + Entropy-Maximization" +" This paper develops a simple calculus for order of magnitude reasoning. A +semantics is given with soundness and completeness results. Order of magnitude +probability functions are easily defined and turn out to be equivalent to kappa +functions, which are slight generalizations of Spohn's Natural Conditional +Functions. The calculus also gives rise to an order of magnitude decision +theory, which can be used to justify an amended version of Pearl's decision +theory for kappa functions, although the latter is weaker and less expressive. +",An Order of Magnitude Calculus +" This paper discusses a method for implementing a probabilistic inference +system based on an extended relational data model. This model provides a +unified approach for a variety of applications such as dynamic programming, +solving sparse linear equations, and constraint propagation. In this framework, +the probability model is represented as a generalized relational database. +Subsequent probabilistic requests can be processed as standard relational +queries. Conventional database management systems can be easily adopted for +implementing such an approximate reasoning system. +",A Method for Implementing a Probabilistic Model as a Relational Database +" Recent developments show that Multiply Sectioned Bayesian Networks (MSBNs) +can be used for diagnosis of natural systems as well as for model-based +diagnosis of artificial systems. They can be applied to single-agent oriented +reasoning systems as well as multi-agent distributed probabilistic reasoning +systems. Belief propagation between a pair of subnets plays a central role in +maintenance of global consistency in a MSBN. This paper studies the operation +UpdateBelief, presented originally with MSBNs, for inter-subnet propagation. We +analyze how the operation achieves its intended functionality, which provides +hints as for how its efficiency can be improved. We then define two new +versions of UpdateBelief that reduce the computation time for inter-subnet +propagation. One of them is optimal in the sense that the minimum amount of +computation for coordinating multi-linkage belief propagation is required. The +optimization problem is solved through the solution of a graph-theoretic +problem: the minimum weight open tour in a tree. +","Optimization of Inter-Subnet Belief Updating in Multiply Sectioned + Bayesian Networks" +" In this paper, we present two methods to provide explanations for reasoning +with belief functions in the valuation-based systems. One approach, inspired by +Strat's method, is based on sensitivity analysis, but its computation is +simpler thus easier to implement than Strat's. The other one is to examine the +impact of evidence on the conclusion based on the measure of the information +content in the evidence. We show the property of additivity for the pieces of +evidence that are conditional independent within the context of the +valuation-based systems. We will give an example to show how these approaches +are applied in an evidential network. +",Generating Explanations for Evidential Reasoning +" This paper reports experiments with the causal independence inference +algorithm proposed by Zhang and Poole (1994b) on the CPSC network created by +Pradhan et al. (1994). It is found that the algorithm is able to answer 420 of +the 422 possible zero-observation queries, 94 of 100 randomly generated +five-observation queries, 87 of 100 randomly generated ten-observation queries, +and 69 of 100 randomly generated twenty-observation queries. +",Inference with Causal Independence in the CPSC Network +" The information retrieval systems that are present nowadays are mainly based +on full text matching of keywords or topic based classification. This matching +of keywords often returns a large number of irrelevant information and this +does not meet the users query requirement. In order to solve this problem and +to enhance the search using semantic environment, a technique named ontology is +implemented for the field of poultry in this paper. Ontology is an emerging +technique in the current field of research in semantic environment. This paper +constructs ontology using the tool named Protege version 4.0 and this also +generates Resource Description Framework schema and XML scripts for using +poultry ontology in web. +","An Ontology Construction Approach for the Domain Of Poultry Science + Using Protege" +" Handling visual complexity is a challenging problem in visualization owing to +the subjectiveness of its definition and the difficulty in devising +generalizable quantitative metrics. In this paper we address this challenge by +measuring the visual complexity of two common forms of cluster-based +visualizations: scatter plots and parallel coordinatess. We conceptualize +visual complexity as a form of visual uncertainty, which is a measure of the +degree of difficulty for humans to interpret a visual representation correctly. +We propose an algorithm for estimating visual complexity for the aforementioned +visualizations using Allen's interval algebra. We first establish a set of +primitive 2-cluster cases in scatter plots and another set for parallel +coordinatess based on symmetric isomorphism. We confirm that both are the +minimal sets and verify the correctness of their members computationally. We +score the uncertainty of each primitive case based on its topological +properties, including the existence of overlapping regions, splitting regions +and meeting points or edges. We compare a few optional scoring schemes against +a set of subjective scores by humans, and identify the one that is the most +consistent with the subjective scores. Finally, we extend the 2-cluster measure +to k-cluster measure as a general purpose estimator of visual complexity for +these two forms of cluster-based visualization. +",Measuring Visual Complexity of Cluster-Based Visualizations +" Modification of a conceptual clustering algorithm Cobweb for the purpose of +its application for numerical data is offered. Keywords: clustering, algorithm +Cobweb, numerical data, fuzzy membership function. +","Modification of conceptual clustering algorithm Cobweb for numerical + data using fuzzy membership function" +" Modelling of complex systems is mainly based on the decomposition of these +systems in autonomous elements, and the identification and definitio9n of +possible interactions between these elements. For this, the agent-based +approach is a modelling solution often proposed. Complexity can also be due to +external events or internal to systems, whose main characteristics are +uncertainty, imprecision, or whose perception is subjective (i.e. interpreted). +Insofar as fuzzy logic provides a solution for modelling uncertainty, the +concept of fuzzy agent can model both the complexity and uncertainty. This +paper focuses on introducing the concept of fuzzy agent: a classical +architecture of agent is redefined according to a fuzzy perspective. A +pedagogical illustration of fuzzy agentification of a smart watering system is +then proposed. +",A Modelling Approach Based on Fuzzy Agents +" Bayesian learning of belief networks (BLN) is a method for automatically +constructing belief networks (BNs) from data using search and Bayesian scoring +techniques. K2 is a particular instantiation of the method that implements a +greedy search strategy. To evaluate the accuracy of K2, we randomly generated a +number of BNs and for each of those we simulated data sets. K2 was then used to +induce the generating BNs from the simulated data. We examine the performance +of the program, and the factors that influence it. We also present a simple BN +model, developed from our results, which predicts the accuracy of K2, when +given various characteristics of the data set. +","An Evaluation of an Algorithm for Inductive Learning of Bayesian Belief + Networks Usin" +" We have previously reported a Bayesian algorithm for determining the +coordinates of points in three-dimensional space from uncertain constraints. +This method is useful in the determination of biological molecular structure. +It is limited, however, by the requirement that the uncertainty in the +constraints be normally distributed. In this paper, we present an extension of +the original algorithm that allows constraint uncertainty to be represented as +a mixture of Gaussians, and thereby allows arbitrary constraint distributions. +We illustrate the performance of this algorithm on a problem drawn from the +domain of molecular structure determination, in which a multicomponent +constraint representation produces a much more accurate solution than the old +single component mechanism. The new mechanism uses mixture distributions to +decompose the problem into a set of independent problems with unimodal +constraint uncertainty. The results of the unimodal subproblems are +periodically recombined using Bayes' law, to avoid combinatorial explosion. The +new algorithm is particularly suited for parallel implementation. +",Probabilistic Constraint Satisfaction with Non-Gaussian Noise +" This paper examines the ""K2"" network scoring metric of Cooper and Herskovits. +It shows counterintuitive results from applying this metric to simple networks. +One family of noninformative priors is suggested for assigning equal scores to +equivalent networks. +",A Bayesian Method Reexamined +" Laplace's method, a family of asymptotic methods used to approximate +integrals, is presented as a potential candidate for the tool box of techniques +used for knowledge acquisition and probabilistic inference in belief networks +with continuous variables. This technique approximates posterior moments and +marginal posterior distributions with reasonable accuracy [errors are O(n^-2) +for posterior means] in many interesting cases. The method also seems promising +for computing approximations for Bayes factors for use in the context of model +selection, model uncertainty and mixtures of pdfs. The limitations, regularity +conditions and computational difficulties for the implementation of Laplace's +method are comparable to those associated with the methods of maximum +likelihood and posterior mode analysis. +","Laplace's Method Approximations for Probabilistic Inference in Belief + Networks with Continuous Variables" +" In previous work [BGHK92, BGHK93], we have studied the random-worlds approach +-- a particular (and quite powerful) method for generating degrees of belief +(i.e., subjective probabilities) from a knowledge base consisting of objective +(first-order, statistical, and default) information. But allowing a knowledge +base to contain only objective information is sometimes limiting. We +occasionally wish to include information about degrees of belief in the +knowledge base as well, because there are contexts in which old beliefs +represent important information that should influence new beliefs. In this +paper, we describe three quite general techniques for extending a method that +generates degrees of belief from objective information to one that can make use +of degrees of belief as well. All of our techniques are bloused on well-known +approaches, such as cross-entropy. We discuss general connections between the +techniques and in particular show that, although conceptually and technically +quite different, all of the techniques give the same answer when applied to the +random-worlds method. +",Generating New Beliefs From Old +" Evaluation of counterfactual queries (e.g., ""If A were true, would C have +been true?"") is important to fault diagnosis, planning, and determination of +liability. In this paper we present methods for computing the probabilities of +such queries using the formulation proposed in [Balke and Pearl, 1994], where +the antecedent of the query is interpreted as an external action that forces +the proposition A to be true. When a prior probability is available on the +causal mechanisms governing the domain, counterfactual probabilities can be +evaluated precisely. However, when causal knowledge is specified as conditional +probabilities on the observables, only bounds can computed. This paper develops +techniques for evaluating these bounds, and demonstrates their use in two +applications: (1) the determination of treatment efficacy from studies in which +subjects may choose their own treatment, and (2) the determination of liability +in product-safety litigation. +","Counterfactual Probabilities: Computational Methods, Bounds and + Applications" +" We discuss the problem of construction of inference procedures which can +manipulate with uncertainties measured in ordinal scales and fulfill to the +property of strict monotonicity of conclusion. The class of A-valuations of +plausibility is considered where operations based only on information about +linear ordering of plausibility values are used. In this class the modus ponens +generating function fulfiling to the property of strict monotonicity of +conclusions is introduced. +","Modus Ponens Generating Function in the Class of ^-valuations of + Plausibility" +" We propose an integration of possibility theory into non-classical logics. We +obtain many formal results that generalize the case where possibility and +necessity functions are based on classical logic. We show how useful such an +approach is by applying it to reasoning under uncertain and inconsistent +information. +",Possibility and Necessity Functions over Non-classical Logics +" Some instances of creative thinking require an agent to build and test +hypothetical theories. Such a reasoner needs to explore the space of not only +those situations that have occurred in the past, but also those that are +rationally conceivable. In this paper we present a formalism for exploring the +space of conceivable situation-models for those domains in which the knowledge +is primarily probabilistic in nature. The formalism seeks to construct +consistent, minimal, and desirable situation-descriptions by selecting suitable +domain-attributes and dependency relationships from the available domain +knowledge. +",Exploratory Model Building +" I describe a planning methodology for domains with uncertainty in the form of +external events that are not completely predictable. The events are represented +by enabling conditions and probabilities of occurrence. The planner is +goal-directed and backward chaining, but the subgoals are suggested by +analyzing the probability of success of the partial plan rather than being +simply the open conditions of the operators in the plan. The partial plan is +represented as a Bayesian belief net to compute its probability of success. +Since calculating the probability of success of a plan can be very expensive I +introduce two other techniques for computing it, one that uses Monte Carlo +simulation to estimate it and one based on a Markov chain representation that +uses knowledge about the dependencies between the predicates describing the +domain. +",Planning with External Events +" Bayesian belief network learning algorithms have three basic components: a +measure of a network structure and a database, a search heuristic that chooses +network structures to be considered, and a method of estimating the probability +tables from the database. This paper contributes to all these three topics. The +behavior of the Bayesian measure of Cooper and Herskovits and a minimum +description length (MDL) measure are compared with respect to their properties +for both limiting size and finite size databases. It is shown that the MDL +measure has more desirable properties than the Bayesian measure when a +distribution is to be learned. It is shown that selecting belief networks with +certain minimallity properties is NP-hard. This result justifies the use of +search heuristics instead of exact algorithms for choosing network structures +to be considered. In some cases, a collection of belief networks can be +represented by a single belief network which leads to a new kind of probability +table estimation called smoothing. We argue that smoothing can be efficiently +implemented by incorporating it in the search heuristic. Experimental results +suggest that for learning probabilities of belief networks smoothing is +helpful. +",Properties of Bayesian Belief Network Learning Algorithms +" Simulation schemes for probabilistic inference in Bayesian belief networks +offer many advantages over exact algorithms; for example, these schemes have a +linear and thus predictable runtime while exact algorithms have exponential +runtime. Experiments have shown that likelihood weighting is one of the most +promising simulation schemes. In this paper, we present a new simulation scheme +that generates samples more evenly spread in the sample space than the +likelihood weighting scheme. We show both theoretically and experimentally that +the stratified scheme outperforms likelihood weighting in average runtime and +error in estimates of beliefs. +",A Stratified Simulation Scheme for Inference in Bayesian Belief Networks +" The expected value of information (EVI) is the most powerful measure of +sensitivity to uncertainty in a decision model: it measures the potential of +information to improve the decision, and hence measures the expected value of +outcome. Standard methods for computing EVI use discrete variables and are +computationally intractable for models that contain more than a few variables. +Monte Carlo simulation provides the basis for more tractable evaluation of +large predictive models with continuous and discrete variables, but so far +computation of EVI in a Monte Carlo setting also has appeared impractical. We +introduce an approximate approach based on pre-posterior analysis for +estimating EVI in Monte Carlo models. Our method uses a linear approximation to +the value function and multiple linear regression to estimate the linear model +from the samples. The approach is efficient and practical for extremely large +models. It allows easy estimation of EVI for perfect or partial information on +individual variables or on combinations of variables. We illustrate its +implementation within Demos (a decision modeling system), and its application +to a large model for crisis transportation planning. +",Efficient Estimation of the Value of Information in Monte Carlo Models +" A BN2O network is a two level belief net in which the parent interactions are +modeled using the noisy-or interaction model. In this paper we discuss +application of the SPI local expression language to efficient inference in +large BN2O networks. In particular, we show that there is significant +structure, which can be exploited to improve over the Quickscore result. We +further describe how symbolic techniques can provide information which can +significantly reduce the computation required for computing all cause posterior +marginals. Finally, we present a novel approximation technique with preliminary +experimental results. +",Symbolic Probabilitistic Inference in Large BN2O Networks +" This work proposes action networks as a semantically well-founded framework +for reasoning about actions and change under uncertainty. Action networks add +two primitives to probabilistic causal networks: controllable variables and +persistent variables. Controllable variables allow the representation of +actions as directly setting the value of specific events in the domain, subject +to preconditions. Persistent variables provide a canonical model of persistence +according to which both the state of a variable and the causal mechanism +dictating its value persist over time unless intervened upon by an action (or +its consequences). Action networks also allow different methods for quantifying +the uncertainty in causal relationships, which go beyond traditional +probabilistic quantification. This paper describes both recent results and work +in progress. +","Action Networks: A Framework for Reasoning about Actions and Change + under Uncertainty" +" We study the connection between kappa calculus and probabilistic reasoning in +diagnosis applications. Specifically, we abstract a probabilistic belief +network for diagnosing faults into a kappa network and compare the ordering of +faults computed using both methods. We show that, at least for the example +examined, the ordering of faults coincide as long as all the causal relations +in the original probabilistic network are taken into account. We also provide a +formal analysis of some network structures where the two methods will differ. +Both kappa rankings and infinitesimal probabilities have been used extensively +to study default reasoning and belief revision. But little has been done on +utilizing their connection as outlined above. This is partly because the +relation between kappa and probability calculi assumes that probabilities are +arbitrarily close to one (or zero). The experiments in this paper investigate +this relation when this assumption is not satisfied. The reported results have +important implications on the use of kappa rankings to enhance the knowledge +engineering of uncertainty models. +",On the Relation between Kappa Calculus and Probabilistic Reasoning +" When agents devise plans for execution in the real world, they face two +important forms of uncertainty: they can never have complete knowledge about +the state of the world, and they do not have complete control, as the effects +of their actions are uncertain. While most classical planning methods avoid +explicit uncertainty reasoning, we believe that uncertainty should be +explicitly represented and reasoned about. We develop a probabilistic +representation for states and actions, based on belief networks. We define +conditional belief nets (CBNs) to capture the probabilistic dependency of the +effects of an action upon the state of the world. We also use a CBN to +represent the intrinsic relationships among entities in the environment, which +persist from state to state. We present a simple projection algorithm to +construct the belief network of the state succeeding an action, using the +environment CBN model to infer indirect effects. We discuss how the qualitative +aspects of belief networks and CBNs make them appropriate for the various +stages of the problem solving process, from model construction to the design of +planning algorithms. +","A Structured, Probabilistic Representation of Action" +" We investigate planning in time-critical domains represented as Markov +Decision Processes, showing that search based techniques can be a very powerful +method for finding close to optimal plans. To reduce the computational cost of +planning in these domains, we execute actions as we construct the plan, and +sacrifice optimality by searching to a fixed depth and using a heuristic +function to estimate the value of states. Although this paper concentrates on +the search algorithm, we also discuss ways of constructing heuristic functions +suitable for this approach. Our results show that by interleaving search and +execution, close to optimal policies can be found without the computational +requirements of other approaches. +",Integrating Planning and Execution in Stochastic Domains +" Most algorithms for propagating evidence through belief networks have been +exact and exhaustive: they produce an exact (point-valued) marginal probability +for every node in the network. Often, however, an application will not need +information about every n ode in the network nor will it need exact +probabilities. We present the localized partial evaluation (LPE) propagation +algorithm, which computes interval bounds on the marginal probability of a +specified query node by examining a subset of the nodes in the entire network. +Conceptually, LPE ignores parts of the network that are ""too far away"" from the +queried node to have much impact on its value. LPE has the ""anytime"" property +of being able to produce better solutions (tighter intervals) given more time +to consider more of the network. +",Localized Partial Evaluation of Belief Networks +" AI planning algorithms have addressed the problem of generating sequences of +operators that achieve some input goal, usually assuming that the planning +agent has perfect control over and information about the world. Relaxing these +assumptions requires an extension to the action representation that allows +reasoning both about the changes an action makes and the information it +provides. This paper presents an action representation that extends the +deterministic STRIPS model, allowing actions to have both causal and +informational effects, both of which can be context dependent and noisy. We +also demonstrate how a standard least-commitment planning algorithm can be +extended to include informational actions and contingent execution. +","A Probabilistic Model of Action for Least-Commitment Planning with + Information Gather" +" Several Artificial Intelligence schemes for reasoning under uncertainty +explore either explicitly or implicitly asymmetries among probabilities of +various states of their uncertain domain models. Even though the correct +working of these schemes is practically contingent upon the existence of a +small number of probable states, no formal justification has been proposed of +why this should be the case. This paper attempts to fill this apparent gap by +studying asymmetries among probabilities of various states of uncertain models. +By rewriting the joint probability distribution over a model's variables into a +product of individual variables' prior and conditional probability +distributions, and applying central limit theorem to this product, we can +demonstrate that the probabilities of individual states of the model can be +expected to be drawn from highly skewed, log-normal distributions. With +sufficient asymmetry in individual prior and conditional probability +distributions, a small fraction of states can be expected to cover a large +portion of the total probability space with the remaining states having +practically negligible probability. Theoretical discussion is supplemented by +simulation results and an illustrative real-world example. +",Some Properties of Joint Probability Distributions +" An ordinal view of independence is studied in the framework of possibility +theory. We investigate three possible definitions of dependence, of increasing +strength. One of them is the counterpart to the multiplication law in +probability theory, and the two others are based on the notion of conditional +possibility. These two have enough expressive power to support the whole +possibility theory, and a complete axiomatization is provided for the strongest +one. Moreover we show that weak independence is well-suited to the problems of +belief change and plausible reasoning, especially to address the problem of +blocking of property inheritance in exception-tolerant taxonomic reasoning. +",An Ordinal View of Independence with Application to Plausible Reasoning +" Penalty logic, introduced by Pinkas, associates to each formula of a +knowledge base the price to pay if this formula is violated. Penalties may be +used as a criterion for selecting preferred consistent subsets in an +inconsistent knowledge base, thus inducing a non-monotonic inference relation. +A precise formalization and the main properties of penalty logic and of its +associated non-monotonic inference relation are given in the first part. We +also show that penalty logic and Dempster-Shafer theory are related, especially +in the infinitesimal case. +",Penalty logic and its Link with Dempster-Shafer Theory +" In this paper, we introduce evidence propagation operations on influence +diagrams and a concept of value of evidence, which measures the value of +experimentation. Evidence propagation operations are critical for the +computation of the value of evidence, general update and inference operations +in normative expert systems which are based on the influence diagram +(generalized Bayesian network) paradigm. The value of evidence allows us to +compute directly an outcome sensitivity, a value of perfect information and a +value of control which are used in decision analysis (the science of decision +making under uncertainty). More specifically, the outcome sensitivity is the +maximum difference among the values of evidence, the value of perfect +information is the expected value of the values of evidence, and the value of +control is the optimal value of the values of evidence. We also discuss an +implementation and a relative computational efficiency issues related to the +value of evidence and the value of perfect information. +",Value of Evidence on Influence Diagrams +" Possibilistic conditional independence is investigated: we propose a +definition of this notion similar to the one used in probability theory. The +links between independence and non-interactivity are investigated, and +properties of these relations are given. The influence of the conjunction used +to define a conditional measure of possibility is also highlighted: we examine +three types of conjunctions: Lukasiewicz - like T-norms, product-like T-norms +and the minimum operator. +",Conditional Independence in Possibility Theory +" Backward simulation is an approximate inference technique for Bayesian belief +networks. It differs from existing simulation methods in that it starts +simulation from the known evidence and works backward (i.e., contrary to the +direction of the arcs). The technique's focus on the evidence leads to improved +convergence in situations where the posterior beliefs are dominated by the +evidence rather than by the prior probabilities. Since this class of situations +is large, the technique may make practical the application of approximate +inference in Bayesian belief networks to many real-world problems. +",Backward Simulation in Bayesian Networks +" Testing the validity of probabilistic models containing unmeasured (hidden) +variables is shown to be a hard task. We show that the task of testing whether +models are structurally incompatible with the data at hand, requires an +exponential number of independence evaluations, each of the form: ""X is +conditionally independent of Y, given Z."" In contrast, a linear number of such +evaluations is required to test a standard Bayesian network (one per vertex). +On the positive side, we show that if a network with hidden variables G has a +tree skeleton, checking whether G represents a given probability model P +requires the polynomial number of such independence evaluations. Moreover, we +provide an algorithm that efficiently constructs a tree-structured Bayesian +network (with hidden variables) that represents P if such a network exists, and +further recognizes when such a network does not exist. +","On Testing Whether an Embedded Bayesian Network Represents a Probability + Model" +" We introduce an approach to high-level conditional planning we call +epsilon-safe planning. This probabilistic approach commits us to planning to +meet some specified goal with a probability of success of at least 1-epsilon +for some user-supplied epsilon. We describe several algorithms for epsilon-safe +planning based on conditional planners. The two conditional planners we discuss +are Peot and Smith's nonlinear conditional planner, CNLP, and our own linear +conditional planner, PLINTH. We present a straightforward extension to +conditional planners for which computing the necessary probabilities is simple, +employing a commonly-made but perhaps overly-strong independence assumption. We +also discuss a second approach to epsilon-safe planning which relaxes this +independence assumption, involving the incremental construction of a +probability dependence model in conjunction with the construction of the plan +graph. +",Epsilon-Safe Planning +" We present a method for dynamically generating Bayesian networks from +knowledge bases consisting of first-order probability logic sentences. We +present a subset of probability logic sufficient for representing the class of +Bayesian networks with discrete-valued nodes. We impose constraints on the form +of the sentences that guarantee that the knowledge base contains all the +probabilistic information necessary to generate a network. We define the +concept of d-separation for knowledge bases and prove that a knowledge base +with independence conditions defined by d-separation is a complete +specification of a probability distribution. We present a network generation +algorithm that, given an inference problem in the form of a query Q and a set +of evidence E, generates a network to compute P(Q|E). We prove the algorithm to +be correct. +",Generating Bayesian Networks from Probability Logic Knowledge Bases +" This paper discusses the problem of abstracting conditional probabilistic +actions. We identify two distinct types of abstraction: intra-action +abstraction and inter-action abstraction. We define what it means for the +abstraction of an action to be correct and then derive two methods of +intra-action abstraction and two methods of inter-action abstraction which are +correct according to this criterion. We illustrate the developed techniques by +applying them to actions described with the temporal action representation used +in the DRIPS decision-theoretic planner and we describe how the planner uses +abstraction to reduce the complexity of planning. +",Abstracting Probabilistic Actions +" Heckerman (1993) defined causal independence in terms of a set of temporal +conditional independence statements. These statements formalized certain types +of causal interaction where (1) the effect is independent of the order that +causes are introduced and (2) the impact of a single cause on the effect does +not depend on what other causes have previously been applied. In this paper, we +introduce an equivalent a temporal characterization of causal independence +based on a functional representation of the relationship between causes and the +effect. In this representation, the interaction between causes and effect can +be written as a nested decomposition of functions. Causal independence can be +exploited by representing this decomposition in the belief network, resulting +in representations that are more efficient for inference than general causal +models. We present empirical results showing the benefits of a +causal-independence representation for belief-network inference. +",A New Look at Causal Independence +" We describe algorithms for learning Bayesian networks from a combination of +user knowledge and statistical data. The algorithms have two components: a +scoring metric and a search procedure. The scoring metric takes a network +structure, statistical data, and a user's prior knowledge, and returns a score +proportional to the posterior probability of the network structure given the +data. The search procedure generates networks for evaluation by the scoring +metric. Our contributions are threefold. First, we identify two important +properties of metrics, which we call event equivalence and parameter +modularity. These properties have been mostly ignored, but when combined, +greatly simplify the encoding of a user's prior knowledge. In particular, a +user can express her knowledge-for the most part-as a single prior Bayesian +network for the domain. Second, we describe local search and annealing +algorithms to be used in conjunction with scoring metrics. In the special case +where each node has at most one parent, we show that heuristic search can be +replaced with a polynomial algorithm to identify the networks with the highest +score. Third, we describe a methodology for evaluating Bayesian-network +learning algorithms. We apply this approach to a comparison of metrics and +search procedures. +","Learning Bayesian Networks: The Combination of Knowledge and Statistical + Data" +" Most traditional models of uncertainty have focused on the associational +relationship among variables as captured by conditional dependence. In order to +successfully manage intelligent systems for decision making, however, we must +be able to predict the effects of actions. In this paper, we attempt to unite +two branches of research that address such predictions: causal modeling and +decision analysis. First, we provide a definition of causal dependence in +decision-analytic terms, which we derive from consequences of causal dependence +cited in the literature. Using this definition, we show how causal dependence +can be represented within an influence diagram. In particular, we identify two +inadequacies of an ordinary influence diagram as a representation for cause. We +introduce a special class of influence diagrams, called causal influence +diagrams, which corrects one of these problems, and identify situations where +the other inadequacy can be eliminated. In addition, we describe the +relationships between Howard Canonical Form and existing graphical +representations of cause. +",A Decision-Based View of Causality +" On the one hand, classical terminological knowledge representation excludes +the possibility of handling uncertain concept descriptions involving, e.g., +""usually true"" concept properties, generalized quantifiers, or exceptions. On +the other hand, purely numerical approaches for handling uncertainty in general +are unable to consider terminological knowledge. This paper presents the +language ACP which is a probabilistic extension of terminological logics and +aims at closing the gap between the two areas of research. We present the +formal semantics underlying the language ALUP and introduce the probabilistic +formalism that is based on classes of probabilities and is realized by means of +probabilistic constraints. Besides inferring implicitly existent probabilistic +relationships, the constraints guarantee terminological and probabilistic +consistency. Altogether, the new language ALUP applies to domains where both +term descriptions and uncertainty have to be handled. +",Probabilistic Description Logics +" Qualitative and infinitesimal probability schemes are consistent with the +axioms of probability theory, but avoid the need for precise numerical +probabilities. Using qualitative probabilities could substantially reduce the +effort for knowledge engineering and improve the robustness of results. We +examine experimentally how well infinitesimal probabilities (the kappa-calculus +of Goldszmidt and Pearl) perform a diagnostic task - troubleshooting a car that +will not start by comparison with a conventional numerical belief network. We +found the infinitesimal scheme to be as good as the numerical scheme in +identifying the true fault. The performance of the infinitesimal scheme worsens +significantly for prior fault probabilities greater than 0.03. These results +suggest that infinitesimal probability methods may be of substantial practical +value for machine diagnosis with small prior fault probabilities. +","An Experimental Comparison of Numerical and Qualitative Probabilistic + Reasoning" +" Possibilistic logic, an extension of first-order logic, deals with +uncertainty that can be estimated in terms of possibility and necessity +measures. Syntactically, this means that a first-order formula is equipped with +a possibility degree or a necessity degree that expresses to what extent the +formula is possibly or necessarily true. Possibilistic resolution yields a +calculus for possibilistic logic which respects the semantics developed for +possibilistic logic. A drawback, which possibilistic resolution inherits from +classical resolution, is that it may not terminate if applied to formulas +belonging to decidable fragments of first-order logic. Therefore we propose an +alternative proof method for possibilistic logic. The main feature of this +method is that it completely abstracts from a concrete calculus but uses as +basic operation a test for classical entailment. We then instantiate +possibilistic logic with a terminological logic, which is a decidable subclass +o f first-order logic but nevertheless much more expressive than propositional +logic. This yields an extension of terminological logics towards the +representation of uncertain knowledge which is satisfactory from a semantic as +well as algorithmic point of view. +","An Alternative Proof Method for Possibilistic Logic and its Application + to Terminological Logics" +" We give an axiomatization of confidence transfer - a known conditioning +scheme - from the perspective of expectation-based inference in the sense of +Gardenfors and Makinson. Then, we use the notion of belief independence to +""filter out"" different proposal s of possibilistic conditioning rules, all are +variations of confidence transfer. Among the three rules that we consider, only +Dempster's rule of conditioning passes the test of supporting the notion of +belief independence. With the use of this conditioning rule, we then show that +we can use local computation for computing desired conditional marginal +possibilities of the joint possibility satisfying the given constraints. It +turns out that our local computation scheme is already proposed by Shenoy. +However, our intuitions are completely different from that of Shenoy. While +Shenoy just defines a local computation scheme that fits his framework of +valuation-based systems, we derive that local computation scheme from II(,8) = +tI(,8 I a) * II(a) and appropriate independence assumptions, just like how the +Bayesians derive their local computation scheme. +",Possibilistic Conditioning and Propagation +" To coordinate with other agents in its environment, an agent needs models of +what the other agents are trying to do. When communication is impossible or +expensive, this information must be acquired indirectly via plan recognition. +Typical approaches to plan recognition start with a specification of the +possible plans the other agents may be following, and develop special +techniques for discriminating among the possibilities. Perhaps more desirable +would be a uniform procedure for mapping plans to general structures supporting +inference based on uncertain and incomplete observations. In this paper, we +describe a set of methods for converting plans represented in a flexible +procedural language to observation models represented as probabilistic belief +networks. +",The Automated Mapping of Plans for Plan Recognition +" A logic is defined that allows to express information about statistical +probabilities and about degrees of belief in specific propositions. By +interpreting the two types of probabilities in one common probability space, +the semantics given are well suited to model the influence of statistical +information on the formation of subjective beliefs. Cross entropy minimization +is a key element in these semantics, the use of which is justified by showing +that the resulting logic exhibits some very reasonable properties. +",A Logic for Default Reasoning About Probabilities +" The paper deals with optimality issues in connection with updating beliefs in +networks. We address two processes: triangulation and construction of junction +trees. In the first part, we give a simple algorithm for constructing an +optimal junction tree from a triangulated network. In the second part, we argue +that any exact method based on local calculations must either be less efficient +than the junction tree method, or it has an optimality problem equivalent to +that of triangulation. +",Optimal Junction Trees +" We present an approach to the solution of decision problems formulated as +influence diagrams. This approach involves a special triangulation of the +underlying graph, the construction of a junction tree with special properties, +and a message passing algorithm operating on the junction tree for computation +of expected utilities and optimal decision policies. +",From Influence Diagrams to Junction Trees +" The paper presents a method for reducing the computational complexity of +Bayesian networks through identification and removal of weak dependencies +(removal of links from the (moralized) independence graph). The removal of a +small number of links may reduce the computational complexity dramatically, +since several fill-ins and moral links may be rendered superfluous by the +removal. The method is described in terms of impact on the independence graph, +the junction tree, and the potential functions associated with these. An +empirical evaluation of the method using large real-world networks demonstrates +the applicability of the method. Further, the method, which has been +implemented in Hugin, complements the approximation method suggested by Jensen +& Andersen (1990). +","Reduction of Computational Complexity in Bayesian Networks through + Removal of Weak Dependencies" +" We explore the issue of refining an existent Bayesian network structure using +new data which might mention only a subset of the variables. Most previous +works have only considered the refinement of the network's conditional +probability parameters, and have not addressed the issue of refining the +network's structure. We develop a new approach for refining the network's +structure. Our approach is based on the Minimal Description Length (MDL) +principle, and it employs an adapted version of a Bayesian network learning +algorithm developed in our previous work. One of the adaptations required is to +modify the previous algorithm to account for the structure of the existent +network. The learning algorithm generates a partial network structure which can +then be used to improve the existent network. We also present experimental +evidence demonstrating the effectiveness of our approach. +",Using New Data to Refine a Bayesian Network +" We view the syntax-based approaches to default reasoning as a model-based +diagnosis problem, where each source giving a piece of information is +considered as a component. It is formalized in the ATMS framework (each source +corresponds to an assumption). We assume then that all sources are independent +and ""fail"" with a very small probability. This leads to a probability +assignment on the set of candidates, or equivalently on the set of consistent +environments. This probability assignment induces a Dempster-Shafer belief +function which measures the probability that a proposition can be deduced from +the evidence. This belief function can be used in several different ways to +define a non-monotonic consequence relation. We study and compare these +consequence relations. The -case of prioritized knowledge bases is briefly +considered. +",Syntax-based Default Reasoning as Probabilistic Model-based Diagnosis +" A model to represent spatial information is presented in this paper. It is +based on fuzzy constraints represented as fuzzy geometric relations that can be +hierarchically structured. The concept of spatial template is introduced to +capture the idea of interrelated objects in two-dimensional space. The +representation model is used to specify imprecise or vague information +consisting in relative locations and orientations of template objects. It is +shown in this paper how a template represented by this model can be matched +against a crisp situation to recognize a particular instance of this template. +Furthermore, the proximity measure (fuzzy measure) between the instance and the +template is worked out - this measure can be interpreted as a degree of +similarity. In this context, template recognition can be viewed as a case of +fuzzy pattern recognition. The results of this work have been implemented and +applied to a complex military problem from which this work originated. +",Fuzzy Geometric Relations to Represent Hierarchical Spatial Information +" This paper examines the problem of constructing belief networks to evaluate +plans produced by an knowledge-based planner. Techniques are presented for +handling various types of complicating plan features. These include plans with +context-dependent consequences, indirect consequences, actions with +preconditions that must be true during the execution of an action, +contingencies, multiple levels of abstraction multiple execution agents with +partially-ordered and temporally overlapping actions, and plans which reference +specific times and time durations. +",Constructing Belief Networks to Evaluate Plans +" This paper describes the best first search strategy used by U-Plan (Mansell +1993a), a planning system that constructs quantitatively ranked plans given an +incomplete description of an uncertain environment. U-Plan uses uncertain and +incomplete evidence de scribing the environment, characterizes it using a +Dempster-Shafer interval, and generates a set of possible world states. Plan +construction takes place in an abstraction hierarchy where strategic decisions +are made before tactical decisions. Search through this abstraction hierarchy +is guided by a quantitative measure (expected fulfillment) based on decision +theory. The search strategy is best first with the provision to update expected +fulfillment and review previous decisions in the light of planning +developments. U-Plan generates multiple plans for multiple possible worlds, and +attempts to use existing plans for new world situations. A super-plan is then +constructed, based on merging the set of plans and appropriately timed +knowledge acquisition operators, which are used to decide between plan +alternatives during plan execution. +",Operator Selection While Planning Under Uncertainty +" In this paper we describe a framework for model-based diagnosis of dynamic +systems, which extends previous work in this field by using and expressing +temporal uncertainty in the form of qualitative interval relations a la Allen. +Based on a logical framework extended by qualitative and quantitative temporal +constraints we show how to describe behavioral models (both consistency- and +abductive-based), discuss how to use abstract observations and show how +abstract temporal diagnoses are computed. This yields an expressive framework, +which allows the representation of complex temporal behavior allowing us to +represent temporal uncertainty. Due to its abstraction capabilities computation +is made independent of the number of observations and time points in a temporal +setting. An example of hepatitis diagnosis is used throughout the paper. +",Model-Based Diagnosis with Qualitative Temporal Uncertainty +" Certain classes of problems, including perceptual data understanding, +robotics, discovery, and learning, can be represented as incremental, +dynamically constructed belief networks. These automatically constructed +networks can be dynamically extended and modified as evidence of new +individuals becomes available. The main result of this paper is the incremental +extension of the singly connected polytree network in such a way that the +network retains its singly connected polytree structure after the changes. The +algorithm is deterministic and is guaranteed to have a complexity of single +node addition that is at most of order proportional to the number of nodes (or +size) of the network. Additional speed-up can be achieved by maintaining the +path information. Despite its incremental and dynamic nature, the algorithm can +also be used for probabilistic inference in belief networks in a fashion +similar to other exact inference algorithms. +",Incremental Dynamic Construction of Layered Polytree Networks +" We present a symbolic machinery that admits both probabilistic and causal +information about a given domain and produces probabilistic statements about +the effect of actions and the impact of observations. The calculus admits two +types of conditioning operators: ordinary Bayes conditioning, P(y|X = x), which +represents the observation X = x, and causal conditioning, P(y|do(X = x)), read +the probability of Y = y conditioned on holding X constant (at x) by deliberate +action. Given a mixture of such observational and causal sentences, together +with the topology of the causal graph, the calculus derives new conditional +probabilities of both types, thus enabling one to quantify the effects of +actions (and policies) from partially specified knowledge bases, such as +Bayesian networks in which some conditional probabilities may not be available. +",A Probabilistic Calculus of Actions +" This paper describes a novel approach to planning which takes advantage of +decision theory to greatly improve robustness in an uncertain environment. We +present an algorithm which computes conditional plans of maximum expected +utility. This algorithm relies on a representation of the search space as an +AND/OR tree and employs a depth-limit to control computation costs. A numeric +robustness factor, which parameterizes the utility function, allows the user to +modulate the degree of risk-aversion employed by the planner. Via a look-ahead +search, the planning algorithm seeks to find an optimal plan using expected +utility as its optimization criterion. We present experimental results obtained +by applying our algorithm to a non-deterministic extension of the blocks world +domain. Our results demonstrate that the robustness factor governs the degree +of risk embodied in the conditional plans computed by our algorithm. +",Robust Planning in Uncertain Environments +" This paper examines methods of decision making that are able to accommodate +limitations on both the form in which uncertainty pertaining to a decision +problem can be realistically represented and the amount of computing time +available before a decision must be made. The methods are anytime algorithms in +the sense of Boddy and Dean 1991. Techniques are presented for use with Frisch +and Haddawy's [1992] anytime deduction system, with an anytime adaptation of +Nilsson's [1986] probabilistic logic, and with a probabilistic database model. +",Anytime Decision Making with Imprecise Probabilities +" We present several techniques for knowledge engineering of large belief +networks (BNs) based on the our experiences with a network derived from a large +medical knowledge base. The noisyMAX, a generalization of the noisy-OR gate, is +used to model causal in dependence in a BN with multi-valued variables. We +describe the use of leak probabilities to enforce the closed-world assumption +in our model. We present Netview, a visualization tool based on causal +independence and the use of leak probabilities. The Netview software allows +knowledge engineers to dynamically view sub-networks for knowledge engineering, +and it provides version control for editing a BN. Netview generates +sub-networks in which leak probabilities are dynamically updated to reflect the +missing portions of the network. +",Knowledge Engineering for Large Belief Networks +" While influence diagrams have many advantages as a representation framework +for Bayesian decision problems, they have a serious drawback in handling +asymmetric decision problems. To be represented in an influence diagram, an +asymmetric decision problem must be symmetrized. A considerable amount of +unnecessary computation may be involved when a symmetrized influence diagram is +evaluated by conventional algorithms. In this paper we present an approach for +avoiding such unnecessary computation in influence diagram evaluation. +",Solving Asymmetric Decision Problems with Influence Diagrams +" Bayesian Belief Networks (BBNs) are a powerful formalism for reasoning under +uncertainty but bear some severe limitations: they require a large amount of +information before any reasoning process can start, they have limited +contradiction handling capabilities, and their ability to provide explanations +for their conclusion is still controversial. There exists a class of reasoning +systems, called Truth Maintenance Systems (TMSs), which are able to deal with +partially specified knowledge, to provide well-founded explanation for their +conclusions, and to detect and handle contradictions. TMSs incorporating +measure of uncertainty are called Belief Maintenance Systems (BMSs). This paper +describes how a BMS based on probabilistic logic can be applied to BBNs, thus +introducing a new class of BBNs, called Ignorant Belief Networks, able to +incrementally deal with partially specified conditional dependencies, to +provide explanations, and to detect and handle contradictions. +",Belief Maintenance in Bayesian Networks +" Independence-based (IB) assignments to Bayesian belief networks were +originally proposed as abductive explanations. IB assignments assign fewer +variables in abductive explanations than do schemes assigning values to all +evidentially supported variables. We use IB assignments to approximate marginal +probabilities in Bayesian belief networks. Recent work in belief updating for +Bayes networks attempts to approximate posterior probabilities by finding a +small number of the highest probability complete (or perhaps evidentially +supported) assignments. Under certain assumptions, the probability mass in the +union of these assignments is sufficient to obtain a good approximation. Such +methods are especially useful for highly-connected networks, where the maximum +clique size or the cutset size make the standard algorithms intractable. Since +IB assignments contain fewer assigned variables, the probability mass in each +assignment is greater than in the respective complete assignment. Thus, fewer +IB assignments are sufficient, and a good approximation can be obtained more +efficiently. IB assignments can be used for efficiently approximating posterior +node probabilities even in cases which do not obey the rather strict skewness +assumptions used in previous research. Two algorithms for finding the high +probability IB assignments are suggested: one by doing a best-first heuristic +search, and another by special-purpose integer linear programming. Experimental +results show that this approach is feasible for highly connected belief +networks. +","Belief Updating by Enumerating High-Probability Independence-Based + Assignments" +" In this paper we propose a new approach to probabilistic inference on belief +networks, global conditioning, which is a simple generalization of Pearl's +(1986b) method of loopcutset conditioning. We show that global conditioning, as +well as loop-cutset conditioning, can be thought of as a special case of the +method of Lauritzen and Spiegelhalter (1988) as refined by Jensen et al (199Oa; +1990b). Nonetheless, this approach provides new opportunities for parallel +processing and, in the case of sequential processing, a tradeoff of time for +memory. We also show how a hybrid method (Suermondt and others 1990) combining +loop-cutset conditioning with Jensen's method can be viewed within our +framework. By exploring the relationships between these methods, we develop a +unifying framework in which the advantages of each approach can be combined +successfully. +",Global Conditioning for Probabilistic Inference in Belief Networks +" We construct the belief function that quantifies the agent, beliefs about +which event of Q will occurred when he knows that the event is selected by a +chance set-up and that the probability function associated to the chance set up +is only partially known. +",Belief Induced by the Partial Knowledge of the Probabilities +" Over time, there have hen refinements in the way that probability +distributions are used for representing beliefs. Models which rely on single +probability distributions depict a complete ordering among the propositions of +interest, yet human beliefs are sometimes not completely ordered. Non-singleton +sets of probability distributions can represent partially ordered beliefs. +Convex sets are particularly convenient and expressive, but it is known that +there are reasonable patterns of belief whose faithful representation require +less restrictive sets. The present paper shows that prior ignorance about three +or more exclusive alternatives and the emergence of partially ordered beliefs +when evidence is obtained defy representation by any single set of +distributions, but yield to a representation baud on several uts. The partial +order is shown to be a partial qualitative probability which shares some +intuitively appealing attributes with probability distributions. +","Ignorance and the Expressiveness of Single- and Set-Valued Probability + Models of Belief" +" Model-based diagnosis reasons backwards from a functional schematic of a +system to isolate faults given observations of anomalous behavior. We develop a +fully probabilistic approach to model based diagnosis and extend it to support +hierarchical models. Our scheme translates the functional schematic into a +Bayesian network and diagnostic inference takes place in the Bayesian network. +A Bayesian network diagnostic inference algorithm is modified to take advantage +of the hierarchy to give computational gains. +",A Probabilistic Approach to Hierarchical Model-based Diagnosis +" The semigraphoid closure of every couple of CI-statements (GI=conditional +independence) is a stochastic CI-model. As a consequence of this result it is +shown that every probabilistically sound inference rule for CI-model, having at +most two antecedents, is derivable from the semigraphoid inference rules. This +justifies the use of semigraphoids as approximations of stochastic CI-models in +probabilistic reasoning. The list of all 19 potential dominant elements of the +mentioned semigraphoid closure is given as a byproduct. +","Semigraphoids Are Two-Antecedental Approximations of Stochastic + Conditional Independence Models" +" System Z+ [Goldszmidt and Pearl, 1991, Goldszmidt, 1992] is a formalism for +reasoning with normality defaults of the form ""typically if phi then + (with +strength cf)"" where 6 is a positive integer. The system has a critical +shortcoming in that it does not sanction inheritance across exceptional +subclasses. In this paper we propose an extension to System Z+ that rectifies +this shortcoming by extracting additional conditions between worlds from the +defaults database. We show that the additional constraints do not change the +notion of the consistency of a database. We also make comparisons with +competing default reasoning systems. +",Exceptional Subclasses in Qualitative Probability +" By analyzing the relationships among chance, weight of evidence and degree of +beliefwe show that the assertion ""probability functions are special cases of +belief functions"" and the assertion ""Dempster's rule can be used to combine +belief functions based on distinct bodies of evidence"" together lead to an +inconsistency in Dempster-Shafer theory. To solve this problem, we must reject +some fundamental postulates of the theory. We introduce a new approach for +uncertainty management that shares many intuitive ideas with D-S theory, while +avoiding this problem. +",A Defect in Dempster-Shafer Theory +" One important factor determining the computational complexity of evaluating a +probabilistic network is the cardinality of the state spaces of the nodes. By +varying the granularity of the state spaces, one can trade off accuracy in the +result for computational efficiency. We present an anytime procedure for +approximate evaluation of probabilistic networks based on this idea. On +application to some simple networks, the procedure exhibits a smooth +improvement in approximation quality as computation time increases. This +suggests that state-space abstraction is one more useful control parameter for +designing real-time probabilistic reasoners. +",State-space Abstraction for Anytime Evaluation of Probabilistic Networks +" Probability measures by themselves, are known to be inappropriate for +modeling the dynamics of plain belief and their excessively strong +measurability constraints make them unsuitable for some representational tasks, +e.g. in the context of firstorder knowledge. In this paper, we are therefore +going to look for possible alternatives and extensions. We begin by delimiting +the general area of interest, proposing a minimal list of assumptions to be +satisfied by any reasonable quasi-probabilistic valuation concept. Within this +framework, we investigate two particularly interesting kinds of quasi-measures +which are not or much less affected by the traditional problems. * Ranking +measures, which generalize Spohn-type and possibility measures. * Cumulative +measures, which combine the probabilistic and the ranking philosophy, allowing +thereby a fine-grained account of static and dynamic belief. +",General Belief Measures +" We take a general approach to uncertainty on product spaces, and give +sufficient conditions for the independence structures of uncertainty measures +to satisfy graphoid properties. Since these conditions are arguably more +intuitive than some of the graphoid properties, they can be viewed as +explanations why probability and certain other formalisms generate graphoids. +The conditions include a sufficient condition for the Intersection property +which can still apply even if there is a strong logical relations hip between +the variables. We indicate how these results can be used to produce theories of +qualitative conditional probability which are semi-graphoids and graphoids. +",Generating Graphoids from Generalised Conditional Probability +" This paper studies the connection between probabilistic conditional +independence in uncertain reasoning and data dependency in relational +databases. As a demonstration of the usefulness of this preliminary +investigation, an alternate proof is presented for refuting the conjecture +suggested by Pearl and Paz that probabilistic conditional independencies have a +complete axiomatization. +",On Axiomatization of Probabilistic Conditional Independencies +" In the existing evidential networks with belief functions, the relations +among the variables are always represented by joint belief functions on the +product space of the involved variables. In this paper, we use conditional +belief functions to represent such relations in the network and show some +relations of these two kinds of representations. We also present a propagation +algorithm for such networks. By analyzing the properties of some special +evidential networks with conditional belief functions, we show that the +reasoning process can be simplified in such kinds of networks. +",Evidential Reasoning with Conditional Belief Functions +" It is well known that conditional independence can be used to factorize a +joint probability into a multiplication of conditional probabilities. This +paper proposes a constructive definition of inter-causal independence, which +can be used to further factorize a conditional probability. An inference +algorithm is developed, which makes use of both conditional independence and +inter-causal independence to reduce inference complexity in Bayesian networks. +",Inter-causal Independence and Heterogeneous Factorization +" We address the problem of causal interpretation of the graphical structure of +Bayesian belief networks (BBNs). We review the concept of causality explicated +in the domain of structural equations models and show that it is applicable to +BBNs. In this view, which we call mechanism-based, causality is defined within +models and causal asymmetries arise when mechanisms are placed in the context +of a system. We lay the link between structural equations models and BBNs +models and formulate the conditions under which the latter can be given causal +interpretation. +",Causality in Bayesian Belief Networks +" The primary theme of this investigation is a decision theoretic account of +conditional ought statements (e.g., ""You ought to do A, if C"") that rectifies +glaring deficiencies in classical deontic logic. The resulting account forms a +sound basis for qualitative decision theory, thus providing a framework for +qualitative planning under uncertainty. In particular, we show that adding +causal relationships (in the form of a single graph) as part of an epistemic +state is sufficient to facilitate the analysis of action sequences, their +consequences, their interaction with observations, their expected utilities +and, hence, the synthesis of plans and strategies under uncertainty. +",From Conditional Oughts to Qualitative Decision Theory +" We have developed a general Bayesian algorithm for determining the +coordinates of points in a three-dimensional space. The algorithm takes as +input a set of probabilistic constraints on the coordinates of the points, and +an a priori distribution for each point location. The output is a +maximum-likelihood estimate of the location of each point. We use the extended, +iterated Kalman filter, and add a search heuristic for optimizing its solution +under nonlinear conditions. This heuristic is based on the same principle as +the simulated annealing heuristic for other optimization problems. Our method +uses any probabilistic constraints that can be expressed as a function of the +point coordinates (for example, distance, angles, dihedral angles, and +planarity). It assumes that all constraints have Gaussian noise. In this paper, +we describe the algorithm and show its performance on a set of synthetic data +to illustrate its convergence properties, and its applicability to domains such +ng molecular structure determination. +","A Probabilistic Algorithm for Calculating Structure: Borrowing from + Simulated Annealing" +" The problems associated with scaling involve active and challenging research +topics in the area of artificial intelligence. The purpose is to solve real +world problems by means of AI technologies, in cases where the complexity of +representation of the real world problem is potentially combinatorial. In this +paper, we present a novel approach to cope with the scaling issues in Bayesian +belief networks for ship classification. The proposed approach divides the +conceptual model of a complex ship classification problem into a set of small +modules that work together to solve the classification problem while preserving +the functionality of the original model. The possible ways of explaining sensor +returns (e.g., the evidence) for some features, such as portholes along the +length of a ship, are sometimes combinatorial. Thus, using an exhaustive +approach, which entails the enumeration of all possible explanations, is +impractical for larger problems. We present a network structure (referred to as +Sequential Decomposition, SD) in which each observation is associated with a +set of legitimate outcomes which are consistent with the explanation of each +observed piece of evidence. The results show that the SD approach allows one to +represent feature-observation relations in a manageable way and achieve the +same explanatory power as an exhaustive approach. +","A Study of Scaling Issues in Bayesian Belief Networks for Ship + Classification" +" This paper addresses the tradeoffs which need to be considered in reasoning +using probabilistic network representations, such as Influence Diagrams (IDs). +In particular, we examine the tradeoffs entailed in using Temporal Influence +Diagrams (TIDs) which adequately capture the temporal evolution of a dynamic +system without prohibitive data and computational requirements. Three +approaches for TID construction which make different tradeoffs are examined: +(1) tailoring the network at each time interval to the data available (rather +then just copying the original Bayes Network for all time intervals); (2) +modeling the evolution of a parsimonious subset of variables (rather than all +variables); and (3) model selection approaches, which seek to minimize some +measure of the predictive accuracy of the model without introducing too many +parameters, which might cause ""overfitting"" of the model. Methods of evaluating +the accuracy/efficiency of the tradeoffs are proposed. +",Tradeoffs in Constructing and Evaluating Temporal Influence Diagrams +" Influence diagrams are ideal knowledge representations for Bayesian +statistical models. However, these diagrams are difficult for end users to +interpret and to manipulate. We present a user-based architecture that enables +end users to create and to manipulate the knowledge representation. We use the +problem of physicians' interpretation of two-arm parallel randomized clinical +trials (TAPRCT) to illustrate the architecture and its use. There are three +primary data structures. Elements of statistical models are encoded as +subgraphs of a restricted class of influence diagram. The interpretations of +those elements are mapped into users' language in a domain-specific, user-based +semantic interface, called a patient-flow diagram, in the TAPRCT problem. +Pennitted transformations of the statistical model that maintain the semantic +relationships of the model are encoded in a metadata-state diagram, called the +cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system +uses modular actions called construction steps. This framework has been +implemented in a system called THOMAS, that allows physicians to interpret the +data reported from a TAPRCT. +",End-User Construction of Influence Diagrams for Bayesian Statistics +" Dynamic network models (DNMs) are belief networks for temporal reasoning. The +DNM methodology combines techniques from time series analysis and probabilistic +reasoning to provide (1) a knowledge representation that integrates +noncontemporaneous and contemporaneous dependencies and (2) methods for +iteratively refining these dependencies in response to the effects of exogenous +influences. We use belief-network inference algorithms to perform forecasting, +control, and discrete event simulation on DNMs. The belief network formulation +allows us to move beyond the traditional assumptions of linearity in the +relationships among time-dependent variables and of normality in their +probability distributions. We demonstrate the DNM methodology on an important +forecasting problem in medicine. We conclude with a discussion of how the +methodology addresses several limitations found in traditional time series +analyses. +",Forecasting Sleep Apnea with Dynamic Network Models +" This paper describes a normative system design that incorporates diagnosis, +dynamic evolution, decision making, and information gathering. A single +influence diagram demonstrates the design's coherence, yet each activity is +more effectively modeled and evaluated separately. Application to offshore oil +platforms illustrates the design. For this application, the normative system is +embedded in a real-time expert system. +",Normative Engineering Risk Management Systems +" We compare the diagnostic accuracy of three diagnostic inference models: the +simple Bayes model, the multimembership Bayes model, which is isomorphic to the +parallel combination function in the certainty-factor model, and a model that +incorporates the noisy OR-gate interaction. The comparison is done on 20 +clinicopathological conference (CPC) cases from the American Journal of +Medicine-challenging cases describing actual patients often with multiple +disorders. We find that the distributions produced by the noisy OR model agree +most closely with the gold-standard diagnoses, although substantial differences +exist between the distributions and the diagnoses. In addition, we find that +the multimembership Bayes model tends to significantly overestimate the +posterior probabilities of diseases, whereas the simple Bayes model tends to +significantly underestimate the posterior probabilities. Our results suggest +that additional work to refine the noisy OR model for internal medicine will be +worthwhile. +",Diagnosis of Multiple Faults: A Sensitivity Analysis +" The inherent intractability of probabilistic inference has hindered the +application of belief networks to large domains. Noisy OR-gates [30] and +probabilistic similarity networks [18, 17] escape the complexity of inference +by restricting model expressiveness. Recent work in the application of +belief-network models to time-series analysis and forecasting [9, 10] has given +rise to the additive belief network model (ABNM). We (1) discuss the nature and +implications of the approximations made by an additive decomposition of a +belief network, (2) show greater efficiency in the induction of additive models +when available data are scarce, (3) generalize probabilistic inference +algorithms to exploit the additive decomposition of ABNMs, (4) show greater +efficiency of inference, and (5) compare results on inference with a simple +additive belief network. +",Additive Belief-Network Models +" Spiegelhalter and Lauritzen [15] studied sequential learning in Bayesian +networks and proposed three models for the representation of conditional +probabilities. A forth model, shown here, assumes that the parameter +distribution is given by a product of Gaussian functions and updates them from +the _ and _r messages of evidence propagation. We also generalize the noisy +OR-gate for multivalued variables, develop the algorithm to compute probability +in time proportional to the number of parents (even in networks with loops) and +apply the learning model to this gate. +",Parameter Adjustment in Bayes Networks. The generalized noisy OR-gate +" Relational models for diagnosis are based on a direct description of the +association between disorders and manifestations. This type of model has been +specially used and developed by Reggia and his co-workers in the late eighties +as a basic starting point for approaching diagnosis problems. The paper +proposes a new relational model which includes Reggia's model as a particular +case and which allows for a more expressive representation of the observations +and of the manifestations associated with disorders. The model distinguishes, +i) between manifestations which are certainly absent and those which are not +(yet) observed, and ii) between manifestations which cannot be caused by a +given disorder and manifestations for which we do not know if they can or +cannot be caused by this disorder. This new model, which can handle uncertainty +in a non-probabilistic way, is based on possibility theory and so-called +twofold fuzzy sets, previously introduced by the authors. +","A fuzzy relation-based extension of Reggia's relational model for + diagnosis handling uncertain and incomplete information" +" From an inconsistent database non-trivial arguments may be constructed both +for a proposition, and for the contrary of that proposition. Therefore, +inconsistency in a logical database causes uncertainty about which conclusions +to accept. This kind of uncertainty is called logical uncertainty. We define a +concept of ""acceptability"", which induces a means for differentiating +arguments. The more acceptable an argument, the more confident we are in it. A +specific interest is to use the acceptability classes to assign linguistic +qualifiers to propositions, such that the qualifier assigned to a propositions +reflects its logical uncertainty. A more general interest is to understand how +classes of acceptability can be defined for arguments constructed from an +inconsistent database, and how this notion of acceptability can be devised to +reflect different criteria. Whilst concentrating on the aspects of assigning +linguistic qualifiers to propositions, we also indicate the more general +significance of the notion of acceptability. +",Dialectic Reasoning with Inconsistent Information +" I introduce a temporal belief-network representation of causal independence +that a knowledge engineer can use to elicit probabilistic models. Like the +current, atemporal belief-network representation of causal independence, the +new representation makes knowledge acquisition tractable. Unlike the atemproal +representation, however, the temporal representation can simplify inference, +and does not require the use of unobservable variables. The representation is +less general than is the atemporal representation, but appears to be useful for +many practical applications. +",Causal Independence for Knowledge Acquisition and Inference +" We take a utility-based approach to categorization. We construct +generalizations about events and actions by considering losses associated with +failing to distinguish among detailed distinctions in a decision model. The +utility-based methods transform detailed states of the world into more abstract +categories comprised of disjunctions of the states. We show how we can cluster +distinctions into groups of distinctions at progressively higher levels of +abstraction, and describe rules for decision making with the abstractions. The +techniques introduce a utility-based perspective on the nature of concepts, and +provide a means of simplifying decision models used in automated reasoning +systems. We demonstrate the techniques by describing the capabilities and +output of TUBA, a program for utility-based abstraction. +",Utility-Based Abstraction and Categorization +" When eliciting probability models from experts, knowledge engineers may +compare the results of the model with expert judgment on test scenarios, then +adjust model parameters to bring the behavior of the model more in line with +the expert's intuition. This paper presents a methodology for analytic +computation of sensitivity values to measure the impact of small changes in a +network parameter on a target probability value or distribution. These values +can be used to guide knowledge elicitation. They can also be used in a gradient +descent algorithm to estimate parameter values that maximize a measure of +goodness-of-fit to both local and holistic probability assessments. +",Sensitivity Analysis for Probability Assessments in Bayesian Networks +" Causal Models are like Dependency Graphs and Belief Nets in that they provide +a structure and a set of assumptions from which a joint distribution can, in +principle, be computed. Unlike Dependency Graphs, Causal Models are models of +hierarchical and/or parallel processes, rather than models of distributions +(partially) known to a model builder through some sort of gestalt. As such, +Causal Models are more modular, easier to build, more intuitive, and easier to +understand than Dependency Graph Models. Causal Models are formally defined and +Dependency Graph Models are shown to be a special case of them. Algorithms +supporting inference are presented. Parsimonious methods for eliciting +dependent probabilities are presented. +",Causal Modeling +" One topic that is likely to attract an increasing amount of attention within +the Knowledge-base systems research community is the coordination of +information provided by multiple experts. We envision a situation in which +several experts independently encode information as belief networks. A +potential user must then coordinate the conclusions and recommendations of +these networks to derive some sort of consensus. One approach to such a +consensus is the fusion of the contributed networks into a single, consensus +model prior to the consideration of any case-specific data (specific +observations, test results). This approach requires two types of combination +procedures, one for probabilities, and one for graphs. Since the combination of +probabilities is relatively well understood, the key barriers to this approach +lie in the realm of graph theory. This paper provides formal definitions of +some of the operations necessary to effect the necessary graphical +combinations, and provides complexity analyses of these procedures. The paper's +key result is that most of these operations are NP-hard, and its primary +message is that the derivation of ?good? consensus networks must be done +heuristically. +",Some Complexity Considerations in the Combination of Belief Networks +" This paper identifies and solves a new optimization problem: Given a belief +network (BN) and a target ordering on its variables, how can we efficiently +derive its minimal I-map whose arcs are consistent with the target ordering? We +present three solutions to this problem, all of which lead to directed acyclic +graphs based on the original BN's recursive basis relative to the specified +ordering (such a DAG is sometimes termed the boundary DAG drawn from the given +BN relative to the said ordering [5]). Along the way, we also uncover an +important general principal about arc reversals: when reordering a BN according +to some target ordering, (while attempting to minimize the number of arcs +generated), the sequence of arc reversals should follow the topological +ordering induced by the original belief network's arcs to as great an extent as +possible. These results promise to have a significant impact on the derivation +of consensus models, as well as on other algorithms that require the +reconfiguration and/or combination of BN's. +","Deriving a Minimal I-map of a Belief Network Relative to a Target + Ordering of its Nodes" +" Probabilistic conceptual network is a knowledge representation scheme +designed for reasoning about concepts and categorical abstractions in +utility-based categorization. The scheme combines the formalisms of abstraction +and inheritance hierarchies from artificial intelligence, and probabilistic +networks from decision analysis. It provides a common framework for +representing conceptual knowledge, hierarchical knowledge, and uncertainty. It +facilitates dynamic construction of categorization decision models at varying +levels of abstraction. The scheme is applied to an automated machining problem +for reasoning about the state of the machine at varying levels of abstraction +in support of actions for maintaining competitiveness of the plant. +","Probabilistic Conceptual Network: A Belief Representation Scheme for + Utility-Based Categorization" +" We investigate the value of extending the completeness of a decision model +along different dimensions of refinement. Specifically, we analyze the expected +value of quantitative, conceptual, and structural refinement of decision +models. We illustrate the key dimensions of refinement with examples. The +analyses of value of model refinement can be used to focus the attention of an +analyst or an automated reasoning system on extensions of a decision model +associated with the greatest expected value. +","Reasoning about the Value of Decision-Model Refinement: Methods and + Application" +" Problems of probabilistic inference and decision making under uncertainty +commonly involve continuous random variables. Often these are discretized to a +few points, to simplify assessments and computations. An alternative +approximation is to fit analytically tractable continuous probability +distributions. This approach has potential simplicity and accuracy advantages, +especially if variables can be transformed first. This paper shows how a +minimum relative entropy criterion can drive both transformation and fitting, +illustrating with a power and logarithm family of transformations and mixtures +of Gaussian (normal) distributions, which allow use of efficient influence +diagram methods. The fitting procedure in this case is the well-known EM +algorithm. The selection of the number of components in a fitted mixture +distribution is automated with an objective that trades off accuracy and +computational cost. +","Mixtures of Gaussians and Minimum Relative Entropy Techniques for + Modeling Continuous Uncertainties" +" Valuation networks have been proposed as graphical representations of +valuation-based systems (VBSs). The VBS framework is able to capture many +uncertainty calculi including probability theory, Dempster-Shafer's +belief-function theory, Spohn's epistemic belief theory, and Zadeh's +possibility theory. In this paper, we show how valuation networks encode +conditional independence relations. For the probabilistic case, the class of +probability models encoded by valuation networks includes undirected graph +models, directed acyclic graph models, directed balloon graph models, and +recursive causal graph models. +",Valuation Networks and Conditional Independence +" Relevance-based explanation is a scheme in which partial assignments to +Bayesian belief network variables are explanations (abductive conclusions). We +allow variables to remain unassigned in explanations as long as they are +irrelevant to the explanation, where irrelevance is defined in terms of +statistical independence. When multiple-valued variables exist in the system, +especially when subsets of values correspond to natural types of events, the +over specification problem, alleviated by independence-based explanation, +resurfaces. As a solution to that, as well as for addressing the question of +explanation specificity, it is desirable to collapse such a subset of values +into a single value on the fly. The equivalent method, which is adopted here, +is to generalize the notion of assignments to allow disjunctive assignments. We +proceed to define generalized independence based explanations as maximum +posterior probability independence based generalized assignments (GIB-MAPs). +GIB assignments are shown to have certain properties that ease the design of +algorithms for computing GIB-MAPs. One such algorithm is discussed here, as +well as suggestions for how other algorithms may be adapted to compute +GIB-MAPs. GIB-MAP explanations still suffer from instability, a problem which +may be addressed using ?approximate? conditional independence as a condition +for irrelevance. +",Relevant Explanations: Allowing Disjunctive Assignments +" The Noisy-Or model is convenient for describing a class of uncertain +relationships in Bayesian networks [Pearl 1988]. Pearl describes the Noisy-Or +model for Boolean variables. Here we generalize the model to nary input and +output variables and to arbitrary functions other than the Boolean OR function. +This generalization is a useful modeling aid for construction of Bayesian +networks. We illustrate with some examples including digital circuit diagnosis +and network reliability analysis. +",A Generalization of the Noisy-Or Model +" We present a mechanism for constructing graphical models, specifically +Bayesian networks, from a knowledge base of general probabilistic information. +The unique feature of our approach is that it uses a powerful first-order +probabilistic logic for expressing the general knowledge base. This logic +allows for the representation of a wide range of logical and probabilistic +information. The model construction procedure we propose uses notions from +direct inference to identify pieces of local statistical information from the +knowledge base that are most appropriate to the particular event we want to +reason about. These pieces are composed to generate a joint probability +distribution specified as a Bayesian network. Although there are fundamental +difficulties in dealing with fully general knowledge, our procedure is +practical for quite rich knowledge bases and it supports the construction of a +far wider range of networks than allowed for by current template technology. +","Using First-Order Probability Logic for the Construction of Bayesian + Networks" +" PAGODA (Probabilistic Autonomous Goal-Directed Agent) is a model for +autonomous learning in probabilistic domains [desJardins, 1992] that +incorporates innovative techniques for using the agent's existing knowledge to +guide and constrain the learning process and for representing, reasoning with, +and learning probabilistic knowledge. This paper describes the probabilistic +representation and inference mechanism used in PAGODA. PAGODA forms theories +about the effects of its actions and the world state on the environment over +time. These theories are represented as conditional probability distributions. +A restriction is imposed on the structure of the theories that allows the +inference mechanism to find a unique predicted distribution for any action and +world state description. These restricted theories are called uniquely +predictive theories. The inference mechanism, Probability Combination using +Independence (PCI), uses minimal independence assumptions to combine the +probabilities in a theory to make probabilistic predictions. +","Representing and Reasoning With Probabilistic Knowledge: A Bayesian + Approach" +" One of the most difficult aspects of modeling complex dilemmas in +decision-analytic terms is composing a diagram of relevance relations from a +set of domain concepts. Decision models in domains such as medicine, however, +exhibit certain prototypical patterns that can guide the modeling process. +Medical concepts can be classified according to semantic types that have +characteristic positions and typical roles in an influence-diagram model. We +have developed a graph-grammar production system that uses such inherent +interrelationships among medical terms to facilitate the modeling of medical +decisions. +",Graph-Grammar Assistance for Automated Generation of Influence Diagrams +" In previous work we developed a method of learning Bayesian Network models +from raw data. This method relies on the well known minimal description length +(MDL) principle. The MDL principle is particularly well suited to this task as +it allows us to tradeoff, in a principled way, the accuracy of the learned +network against its practical usefulness. In this paper we present some new +results that have arisen from our work. In particular, we present a new local +way of computing the description length. This allows us to make significant +improvements in our search algorithm. In addition, we modify our algorithm so +that it can take into account partial domain information that might be provided +by a domain expert. The local computation of description length also opens the +door for local refinement of an existent network. The feasibility of our +approach is demonstrated by experiments involving networks of a practical size. +",Using Causal Information and Local Measures to Learn Bayesian Networks +" As belief networks are used to model increasingly complex situations, the +need to automatically construct them from large databases will become +paramount. This paper concentrates on solving a part of the belief network +induction problem: that of learning the quantitative structure (the conditional +probabilities), given the qualitative structure. In particular, a theory is +presented that shows how to propagate inference distributions in a belief +network, with the only assumption being that the given qualitative structure is +correct. Most inference algorithms must make at least this assumption. The +theory is based on four network transformations that are sufficient for any +inference in a belief network. Furthermore, the claim is made that contrary to +popular belief, error will not necessarily grow as the inference chain grows. +Instead, for QBN belief nets induced from large enough samples, the error is +more likely to decrease as the size of the inference chain increases. +",Minimal Assumption Distribution Propagation in Belief Networks +" Previous algorithms for the construction of Bayesian belief network +structures from data have been either highly dependent on conditional +independence (CI) tests, or have required an ordering on the nodes to be +supplied by the user. We present an algorithm that integrates these two +approaches - CI tests are used to generate an ordering on the nodes from the +database which is then used to recover the underlying Bayesian network +structure using a non CI based method. Results of preliminary evaluation of the +algorithm on two networks (ALARM and LED) are presented. We also discuss some +algorithm performance issues and open problems. +","An Algorithm for the Construction of Bayesian Network Structures from + Data" +" This paper addresses learning stochastic rules especially on an +inter-attribute relation based on a Minimum Description Length (MDL) principle +with a finite number of examples, assuming an application to the design of +intelligent relational database systems. The stochastic rule in this paper +consists of a model giving the structure like the dependencies of a Bayesian +Belief Network (BBN) and some stochastic parameters each indicating a +conditional probability of an attribute value given the state determined by the +other attributes' values in the same record. Especially, we propose the +extended version of the algorithm of Chow and Liu in that our learning +algorithm selects the model in the range where the dependencies among the +attributes are represented by some general plural number of trees. +","A Construction of Bayesian Networks from Databases Based on an MDL + Principle" +" Numerous methods for probabilistic reasoning in large, complex belief or +decision networks are currently being developed. There has been little research +on automating the dynamic, incremental construction of decision models. A +uniform value-driven method of decision model construction is proposed for the +hierarchical complete diagnosis. Hierarchical complete diagnostic reasoning is +formulated as a stochastic process and modeled using influence diagrams. Given +observations, this method creates decision models in order to obtain the best +actions sequentially for locating and repairing a fault at minimum cost. This +method construct decision models incrementally, interleaving probe actions with +model construction and evaluation. The method treats meta-level and baselevel +tasks uniformly. That is, the method takes a decision-theoretic look at the +control of search in causal pathways and structural hierarchies. +","Knowledge-Based Decision Model Construction for Hierarchical Diagnosis: + A Preliminary Report" +" We describe the integration of logical and uncertain reasoning methods to +identify the likely source and location of software problems. To date, software +engineers have had few tools for identifying the sources of error in complex +software packages. We describe a method for diagnosing software problems +through combining logical and uncertain reasoning analyses. Our preliminary +results suggest that such methods can be of value in directing the attention of +software engineers to paths of an algorithm that have the highest likelihood of +harboring a programming error. +","A Synthesis of Logical and Probabilistic Reasoning for Program + Understanding and Debugging" +" In recent years the belief network has been used increasingly to model +systems in Al that must perform uncertain inference. The development of +efficient algorithms for probabilistic inference in belief networks has been a +focus of much research in AI. Efficient algorithms for certain classes of +belief networks have been developed, but the problem of reporting the +uncertainty in inferred probabilities has received little attention. A system +should not only be capable of reporting the values of inferred probabilities +and/or the favorable choices of a decision; it should report the range of +possible error in the inferred probabilities and/or choices. Two methods have +been developed and implemented for determining the variance in inferred +probabilities in belief networks. These methods, the Approximate Propagation +Method and the Monte Carlo Integration Method are discussed and compared in +this paper. +","An Implementation of a Method for Computing the Uncertainty in Inferred + Probabilities in Belief Networks" +" Propositional representation services such as truth maintenance systems offer +powerful support for incremental, interleaved, problem-model construction and +evaluation. Probabilistic inference systems, in contrast, have lagged behind in +supporting this incrementality typically demanded by problem solvers. The +problem, we argue, is that the basic task of probabilistic inference is +typically formulated at too large a grain-size. We show how a system built +around a smaller grain-size inference task can have the desired incrementality +and serve as the basis for a low-level (propositional) probabilistic +representation service. +",Incremental Probabilistic Inference +" We describe a method for time-critical decision making involving sequential +tasks and stochastic processes. The method employs several iterative refinement +routines for solving different aspects of the decision making problem. This +paper concentrates on the meta-level control problem of deliberation +scheduling, allocating computational resources to these routines. We provide +different models corresponding to optimization problems that capture the +different circumstances and computational strategies for decision making under +time constraints. We consider precursor models in which all decision making is +performed prior to execution and recurrent models in which decision making is +performed in parallel with execution, accounting for the states observed during +execution and anticipating future states. We describe algorithms for precursor +and recurrent models and provide the results of our empirical investigations to +date. +",Deliberation Scheduling for Time-Critical Sequential Decision Making +" Intercausal reasoning is a common inference pattern involving probabilistic +dependence of causes of an observed common effect. The sign of this dependence +is captured by a qualitative property called product synergy. The current +definition of product synergy is insufficient for intercausal reasoning where +there are additional uninstantiated causes of the common effect. We propose a +new definition of product synergy and prove its adequacy for intercausal +reasoning with direct and indirect evidence for the common effect. The new +definition is based on a new property matrix half positive semi-definiteness, a +weakened form of matrix positive semi-definiteness. +",Intercausal Reasoning with Uninstantiated Ancestor Nodes +" We examine two types of similarity networks each based on a distinct notion +of relevance. For both types of similarity networks we present an efficient +inference algorithm that works under the assumption that every event has a +nonzero probability of occurrence. Another inference algorithm is developed for +type 1 similarity networks that works under no restriction, albeit less +efficiently. +",Inference Algorithms for Similarity Networks +" Two algorithms are presented for ""compiling"" influence diagrams into a set of +simple decision rules. These decision rules define simple-to-execute, complete, +consistent, and near-optimal decision procedures. These compilation algorithms +can be used to derive decision procedures for human teams solving time +constrained decision problems. +",Two Procedures for Compiling Influence Diagrams +" Given a belief network with evidence, the task of finding the I most probable +explanations (MPE) in the belief network is that of identifying and ordering +the I most probable instantiations of the non-evidence nodes of the belief +network. Although many approaches have been proposed for solving this problem, +most work only for restricted topologies (i.e., singly connected belief +networks). In this paper, we will present a new approach for finding I MPEs in +an arbitrary belief network. First, we will present an algorithm for finding +the MPE in a belief network. Then, we will present a linear time algorithm for +finding the next MPE after finding the first MPE. And finally, we will discuss +the problem of finding the MPE for a subset of variables of a belief network, +and show that the problem can be efficiently solved by this approach. +",An efficient approach for finding the MPE in belief networks +" This paper describes ongoing research into planning in an uncertain +environment. In particular, it introduces U-Plan, a planning system that +constructs quantitatively ranked plans given an incomplete description of the +state of the world. U-Plan uses a DempsterShafer interval to characterise +uncertain and incomplete information about the state of the world. The planner +takes as input what is known about the world, and constructs a number of +possible initial states with representations at different abstraction levels. A +plan is constructed for the initial state with the greatest support, and this +plan is tested to see if it will work for other possible initial states. All, +part, or none of the existing plans may be used in the generation of the plans +for the remaining possible worlds. Planning takes place in an abstraction +hierarchy where strategic decisions are made before tactical decisions. A +super-plan is then constructed, based on merging the set of plans and the +appropriately timed acquisition of essential knowledge, which is used to decide +between plan alternatives. U-Plan usually produces a super-plan in less time +than a classical planner would take to produce a set of plans, one for each +possible world. +",A Method for Planning Given Uncertain and Incomplete Information +" This paper discusses how conflicts (as used by the consistency-based +diagnosis community) can be adapted to be used in a search-based algorithm for +computing prior and posterior probabilities in discrete Bayesian Networks. This +is an ""anytime"" algorithm, that at any stage can estimate the probabilities and +give an error bound. Whereas the most popular Bayesian net algorithms exploit +the structure of the network for efficiency, we exploit probability +distributions for efficiency; this algorithm is most suited to the case with +extreme probabilities. This paper presents a solution to the inefficiencies +found in naive algorithms, and shows how the tools of the consistency-based +diagnosis community (namely conflicts) can be used effectively to improve the +efficiency. Empirical results with networks having tens of thousands of nodes +are presented. +",The use of conflicts in searching Bayesian networks +" Bayesian belief networks can be used to represent and to reason about complex +systems with uncertain, incomplete and conflicting information. Belief networks +are graphs encoding and quantifying probabilistic dependence and conditional +independence among variables. One type of reasoning of interest in diagnosis is +called abductive inference (determination of the global most probable system +description given the values of any partial subset of variables). In some +cases, abductive inference can be performed with exact algorithms using +distributed network computations but it is an NP-hard problem and complexity +increases drastically with the presence of undirected cycles, number of +discrete states per variable, and number of variables in the network. This +paper describes an approximate method based on genetic algorithms to perform +abductive inference in large, multiply connected networks for which complexity +is a concern when using most exact methods and for which systematic search +methods are not feasible. The theoretical adequacy of the method is discussed +and preliminary experimental results are presented. +","GALGO: A Genetic ALGOrithm Decision Support Tool for Complex Uncertain + Systems Modeled with Bayesian Belief Networks" +" Tree structures have been shown to provide an efficient framework for +propagating beliefs [Pearl,1986]. This paper studies the problem of finding an +optimal approximating tree. The star decomposition scheme for sets of three +binary variables [Lazarsfeld,1966; Pearl,1986] is shown to enhance the class of +probability distributions that can support tree structures; such structures are +called tree-decomposable structures. The logarithm scoring rule is found to be +an appropriate optimality criterion to evaluate different tree-decomposable +structures. Characteristics of such structures closest to the actual belief +network are identified using the logarithm rule, and greedy and exact +techniques are developed to find the optimal approximation. +",Using Tree-Decomposable Structures to Approximate Belief Networks +" The potential influence diagram is a generalization of the standard +""conditional"" influence diagram, a directed network representation for +probabilistic inference and decision analysis [Ndilikilikesha, 1991]. It allows +efficient inference calculations corresponding exactly to those on undirected +graphs. In this paper, we explore the relationship between potential and +conditional influence diagrams and provide insight into the properties of the +potential influence diagram. In particular, we show how to convert a potential +influence diagram into a conditional influence diagram, and how to view the +potential influence diagram operations in terms of the conditional influence +diagram. +","Using Potential Influence Diagrams for Probabilistic Inference and + Decision Making" +" In order to find a causal explanation for data presented in the form of +covariance and concentration matrices it is necessary to decide if the graph +formed by such associations is a projection of a directed acyclic graph (dag). +We show that the general problem of deciding whether such a dag exists is +NP-complete. +",Deciding Morality of Graphs is NP-complete +" To determine the value of perfect information in an influence diagram, one +needs first to modify the diagram to reflect the change in information +availability, and then to compute the optimal expected values of both the +original diagram and the modified diagram. The value of perfect information is +the difference between the two optimal expected values. This paper is about how +to speed up the computation of the optimal expected value of the modified +diagram by making use of the intermediate computation results obtained when +computing the optimal expected value of the original diagram. +","Incremental computation of the value of perfect information in + stepwise-decomposable influence diagrams" +" This paper presents and discusses several methods for reasoning from +inconsistent knowledge bases. A so-called argumentative-consequence relation +taking into account the existence of consistent arguments in favor of a +conclusion and the absence of consistent arguments in favor of its contrary, is +particularly investigated. Flat knowledge bases, i.e. without any priority +between their elements, as well as prioritized ones where some elements are +considered as more strongly entrenched than others are studied under different +consequence relations. Lastly a paraconsistent-like treatment of prioritized +knowledge bases is proposed, where both the level of entrenchment and the level +of paraconsistency attached to a formula are propagated. The priority levels +are handled in the framework of possibility theory. +",Argumentative inference in uncertain and inconsistent knowledge bases +" A major reason behind the success of probability calculus is that it +possesses a number of valuable tools, which are based on the notion of +probabilistic independence. In this paper, I identify a notion of logical +independence that makes some of these tools available to a class of +propositional databases, called argument databases. Specifically, I suggest a +graphical representation of argument databases, called argument networks, which +resemble Bayesian networks. I also suggest an algorithm for reasoning with +argument networks, which resembles a basic algorithm for reasoning with +Bayesian networks. Finally, I show that argument networks have several +applications: Nonmonotonic reasoning, truth maintenance, and diagnosis. +",Argument Calculus and Networks +" Argumentation is the process of constructing arguments about propositions, +and the assignment of statements of confidence to those propositions based on +the nature and relative strength of their supporting arguments. The process is +modelled as a labelled deductive system, in which propositions are doubly +labelled with the grounds on which they are based and a representation of the +confidence attached to the argument. Argument construction is captured by a +generalized argument consequence relation based on the ^,--fragment of minimal +logic. Arguments can be aggregated by a variety of numeric and symbolic +flattening functions. This approach appears to shed light on the common logical +structure of a variety of quantitative, qualitative and defeasible uncertainty +calculi. +",Argumentation as a General Framework for Uncertain Reasoning +" In this paper some initial work towards a new approach to qualitative +reasoning under uncertainty is presented. This method is not only applicable to +qualitative probabilistic reasoning, as is the case with other methods, but +also allows the qualitative propagation within networks of values based upon +possibility theory and Dempster-Shafer evidence theory. The method is applied +to two simple networks from which a large class of directed graphs may be +constructed. The results of this analysis are used to compare the qualitative +behaviour of the three major quantitative uncertainty handling formalisms, and +to demonstrate that the qualitative integration of the formalisms is possible +under certain assumptions. +",On reasoning in networks with qualitative uncertainty +" This paper introduces a qualitative measure of ambiguity and analyses its +relationship with other measures of uncertainty. Probability measures relative +likelihoods, while ambiguity measures vagueness surrounding those judgments. +Ambiguity is an important representation of uncertain knowledge. It deals with +a different, type of uncertainty modeled by subjective probability or belief. +",Qualitative Measures of Ambiguity +" Shafer's theory of belief and the Bayesian theory of probability are two +alternative and mutually inconsistent approaches toward modelling uncertainty +in artificial intelligence. To help reduce the conflict between these two +approaches, this paper reexamines expected utility theory-from which Bayesian +probability theory is derived. Expected utility theory requires the decision +maker to assign a utility to each decision conditioned on every possible event +that might occur. But frequently the decision maker cannot foresee all the +events that might occur, i.e., one of the possible events is the occurrence of +an unforeseen event. So once we acknowledge the existence of unforeseen events, +we need to develop some way of assigning utilities to decisions conditioned on +unforeseen events. The commonsensical solution to this problem is to assign +similar utilities to events which are similar. Implementing this commonsensical +solution is equivalent to replacing Bayesian subjective probabilities over the +space of foreseen and unforeseen events by random set theory probabilities over +the space of foreseen events. This leads to an expected utility principle in +which normalized variants of Shafer's commonalities play the role of subjective +probabilities. Hence allowing for unforeseen events in decision analysis causes +Bayesian probability theory to become much more similar to Shaferian theory. +","A Bayesian Variant of Shafer's Commonalities For Modelling Unforeseen + Events" +" We present a semantics for adding uncertainty to conditional logics for +default reasoning and belief revision. We are able to treat conditional +sentences as statements of conditional probability, and express rules for +revision such as ""If A were believed, then B would be believed to degree p."" +This method of revision extends conditionalization by allowing meaningful +revision by sentences whose probability is zero. This is achieved through the +use of counterfactual probabilities. Thus, our system accounts for the best +properties of qualitative methods of update (in particular, the AGM theory of +revision) and probabilistic methods. We also show how our system can be viewed +as a unification of probability theory and possibility theory, highlighting +their orthogonality and providing a means for expressing the probability of a +possibility. We also demonstrate the connection to Lewis's method of imaging. +",The Probability of a Possibility: Adding Uncertainty to Default Rules +" A key issue in the handling of temporal data is the treatment of persistence; +in most approaches it consists in inferring defeasible confusions by +extrapolating from the actual knowledge of the history of the world; we propose +here a gradual modelling of persistence, following the idea that persistence is +decreasing (the further we are from the last time point where a fluent is known +to be true, the less certainly true the fluent is); it is based on possibility +theory, which has strong relations with other well-known ordering-based +approaches to nonmonotonic reasoning. We compare our approach with Dean and +Kanazawa's probabilistic projection. We give a formal modelling of the +decreasing persistence problem. Lastly, we show how to infer nonmonotonic +conclusions using the principle of decreasing persistence. +",Possibilistic decreasing persistence +" Evidential reasoning is now a leading topic in Artificial Intelligence. +Evidence is represented by a variety of evidential functions. Evidential +reasoning is carried out by certain kinds of fundamental operation on these +functions. This paper discusses two of the basic operations on evidential +functions, the discount operation and the well-known orthogonal sum operation. +We show that the discount operation is not commutative with the orthogonal sum +operation, and derive expressions for the two operations applied to the various +evidential function. +",Discounting and Combination Operations in Evidential Reasoning +" The classical propositional assumption-based model is extended to incorporate +probabilities for the assumptions. Then it is placed into the framework of +evidence theory. Several authors like Laskey, Lehner (1989) and Provan (1990) +already proposed a similar point of view, but the first paper is not as much +concerned with mathematical foundations, and Provan's paper develops into a +different direction. Here we thoroughly develop and present the mathematical +foundations of this theory, together with computational methods adapted from +Reiter, De Kleer (1987) and Inoue (1992). Finally, recently proposed techniques +for computing degrees of support are presented. +",Probabilistic Assumption-Based Reasoning +" This paper presents a procedure to determine a complete belief function from +the known values of belief for some of the subsets of the frame of discerment. +The method is based on the principle of minimum commitment and a new principle +called the focusing principle. This additional principle is based on the idea +that belief is specified for the most relevant sets: the focal elements. The +resulting procedure is compared with existing methods of building complete +belief functions: the minimum specificity principle and the least commitment +principle. +",Partially Specified Belief Functions +" Jeffrey's rule of conditioning has been proposed in order to revise a +probability measure by another probability function. We generalize it within +the framework of the models based on belief functions. We show that several +forms of Jeffrey's conditionings can be defined that correspond to the +geometrical rule of conditioning and to Dempster's rule of conditioning, +respectively. +",Jeffrey's rule of conditioning generalized to belief functions +" In this paper, the concept of possibilistic evidence which is a possibility +distribution as well as a body of evidence is proposed over an infinite +universe of discourse. The inference with possibilistic evidence is +investigated based on a unified inference framework maintaining both the +compatibility of concepts and the consistency of the probability logic. +",Inference with Possibilistic Evidence +" An elaboration of Dempster's method of constructing belief functions suggests +a broadly applicable strategy for constructing lower probabilities under a +variety of evidentiary constraints. +",Constructing Lower Probabilities +" In a probability-based reasoning system, Bayes' theorem and its variations +are often used to revise the system's beliefs. However, if the explicit +conditions and the implicit conditions of probability assignments `me properly +distinguished, it follows that Bayes' theorem is not a generally applicable +revision rule. Upon properly distinguishing belief revision from belief +updating, we see that Jeffrey's rule and its variations are not revision rules, +either. Without these distinctions, the limitation of the Bayesian approach is +often ignored or underestimated. Revision, in its general form, cannot be done +in the Bayesian approach, because a probability distribution function alone +does not contain the information needed by the operation. +",Belief Revision in Probability Theory +" This paper examines the concept of a combination rule for belief functions. +It is shown that two fairly simple and apparently reasonable assumptions +determine Dempster's rule, giving a new justification for it. +",The Assumptions Behind Dempster's Rule +" In this paper, we present a decision support system based on belief functions +and the pignistic transformation. The system is an integration of an evidential +system for belief function propagation and a valuation-based system for +Bayesian decision analysis. The two subsystems are connected through the +pignistic transformation. The system takes as inputs the user's ""gut feelings"" +about a situation and suggests what, if any, are to be tested and in what +order, and it does so with a user friendly interface. +",A Belief-Function Based Decision Support System +" This paper provides an overview of the SP theory of intelligence and its +central idea that artificial intelligence, mainstream computing, and much of +human perception and cognition, may be understood as information compression. + The background and origins of the SP theory are described, and the main +elements of the theory, including the key concept of multiple alignment, +borrowed from bioinformatics but with important differences. Associated with +the SP theory is the idea that redundancy in information may be understood as +repetition of patterns, that compression of information may be achieved via the +matching and unification (merging) of patterns, and that computing and +information compression are both fundamentally probabilistic. It appears that +the SP system is Turing-equivalent in the sense that anything that may be +computed with a Turing machine may, in principle, also be computed with an SP +machine. + One of the main strengths of the SP theory and the multiple alignment concept +is in modelling concepts and phenomena in artificial intelligence. Within that +area, the SP theory provides a simple but versatile means of representing +different kinds of knowledge, it can model both the parsing and production of +natural language, with potential for the understanding and translation of +natural languages, it has strengths in pattern recognition, with potential in +computer vision, it can model several kinds of reasoning, and it has +capabilities in planning, problem solving, and unsupervised learning. + The paper includes two examples showing how alternative parsings of an +ambiguous sentence may be modelled as multiple alignments, and another example +showing how the concept of multiple alignment may be applied in medical +diagnosis. +",Computing as compression: the SP theory of intelligence +" This paper presents capabilities of using genetic algorithms to find +approximations of function extrema, which cannot be found using analytic ways. +To enhance effectiveness of calculations, algorithm has been parallelized using +OpenMP library. We gained much increase in speed on platforms using +multithreaded processors with shared memory free access. During analysis we +used different modifications of genetic operator, using them we obtained varied +evolution process of potential solutions. Results allow to choose best methods +among many applied in genetic algorithms and observation of acceleration on +Yorkfield, Bloomfield, Westmere-EX and most recent Sandy Bridge cores. +","Generating extrema approximation of analytically incomputable functions + through usage of parallel computer aided genetic algorithms" +" Several algorithms have been proposed for discovering patterns from +trajectories of moving objects, but only a few have concentrated on outlier +detection. Existing approaches, in general, discover spatial outliers, and do +not provide any further analysis of the patterns. In this paper we introduce +semantic spatial and spatio-temporal outliers and propose a new algorithm for +trajectory outlier detection. Semantic outliers are computed between regions of +interest, where objects have similar movement intention, and there exist +standard paths which connect the regions. We show with experiments on real data +that the method finds semantic outliers from trajectory data that are not +discovered by similar approaches. +","Discovering Semantic Spatial and Spatio-Temporal Outliers from Moving + Object Trajectories" +" Hepatitis C virus (HCV) is a widely spread disease all over the world. HCV +has very high mutation rate that makes it resistant to antibodies. Modeling HCV +to identify the virus mutation process is essential to its detection and +predicting its evolution. This paper presents a model based framework for +estimating mutation rate of HCV in two steps. Firstly profile hidden Markov +model (PHMM) architecture was builder to select the sequences which represents +sequence per year. Secondly mutation rate was calculated by using pair-wise +distance method between sequences. A pilot study is conducted on NS5B zone of +HCV dataset of genotype 4 subtype a (HCV4a) in Egypt. +","Model Based Framework for Estimating Mutation Rate of Hepatitis C Virus + in Egypt" +" In this paper we describe a novel method for evidential reasoning [1]. It +involves modelling the process of evidential reasoning in three steps, namely, +evidence structure construction, evidence accumulation, and decision making. +The proposed method, called RES, is novel in that evidence strength is +associated with an evidential support relationship (an argument) between a pair +of statements and such strength is carried by comparison between arguments. +This is in contrast to the onventional approaches, where evidence strength is +represented numerically and is associated with a statement. +",RES - a Relative Method for Evidential Reasoning +" An algorithm for generating the structure of a directed acyclic graph from +data using the notion of causal input lists is presented. The algorithm +manipulates the ordering of the variables with operations which very much +resemble arc reversal. Operations are only applied if the DAG after the +operation represents at least the independencies represented by the DAG before +the operation until no more arcs can be removed from the DAG. The resulting DAG +is a minimal l-map. +",Optimizing Causal Orderings for Generating DAGs from Data +" Possibilistic logic has been proposed as a numerical formalism for reasoning +with uncertainty. There has been interest in developing qualitative accounts of +possibility, as well as an explanation of the relationship between possibility +and modal logics. We present two modal logics that can be used to represent and +reason with qualitative statements of possibility and necessity. Within this +modal framework, we are able to identify interesting relationships between +possibilistic logic, beliefs and conditionals. In particular, the most natural +conditional definable via possibilistic means for default reasoning is +identical to Pearl's conditional for e-semantics. +",Modal Logics for Qualitative Possibility and Beliefs +" Influence diagram is a graphical representation of belief networks with +uncertainty. This article studies the structural properties of a probabilistic +model in an influence diagram. In particular, structural controllability +theorems and structural observability theorems are developed and algorithms are +formulated. Controllability and observability are fundamental concepts in +dynamic systems (Luenberger 1979). Controllability corresponds to the ability +to control a system while observability analyzes the inferability of its +variables. Both properties can be determined by the ranks of the system +matrices. Structural controllability and observability, on the other hand, +analyze the property of a system with its structure only, without the specific +knowledge of the values of its elements (tin 1974, Shields and Pearson 1976). +The structural analysis explores the connection between the structure of a +model and the functional dependence among its elements. It is useful in +comprehending problem and formulating solution by challenging the underlying +intuitions and detecting inconsistency in a model. This type of qualitative +reasoning can sometimes provide insight even when there is insufficient +numerical information in a model. +",Structural Controllability and Observability in Influence Diagrams +" Experts do not always feel very, comfortable when they have to give precise +numerical estimations of certainty degrees. In this paper we present a +qualitative approach which allows for attaching partially ordered symbolic +grades to logical formulas. Uncertain information is expressed by means of +parameterized modal operators. We propose a semantics for this multimodal logic +and give a sound and complete axiomatization. We study the links with related +approaches and suggest how this framework might be used to manage both +uncertain and incomplere knowledge. +",Lattice-Based Graded Logic: a Multimodal Approach +" We have developed a probabilistic forecasting methodology through a synthesis +of belief network models and classical time-series analysis. We present the +dynamic network model (DNM) and describe methods for constructing, refining, +and performing inference with this representation of temporal probabilistic +knowledge. The DNM representation extends static belief-network models to more +general dynamic forecasting models by integrating and iteratively refining +contemporaneous and time-lagged dependencies. We discuss key concepts in terms +of a model for forecasting U.S. car sales in Japan. +",Dynamic Network Models for Forecasting +" We describe how we selectively reformulate portions of a belief network that +pose difficulties for solution with a stochastic-simulation algorithm. With +employ the selective conditioning approach to target specific nodes in a belief +network for decomposition, based on the contribution the nodes make to the +tractability of stochastic simulation. We review previous work on BNRAS +algorithms- randomized approximation algorithms for probabilistic inference. We +show how selective conditioning can be employed to reformulate a single BNRAS +problem into multiple tractable BNRAS simulation problems. We discuss how we +can use another simulation algorithm-logic sampling-to solve a component of the +inference problem that provides a means for knitting the solutions of +individual subproblems into a final result. Finally, we analyze tradeoffs among +the computational subtasks associated with the selective conditioning approach +to reformulation. +",Reformulating Inference Problems Through Selective Conditioning +" The product expansion of conditional probabilities for belief nets is not +maximum entropy. This appears to deny a desirable kind of assurance for the +model. However, a kind of guarantee that is almost as strong as maximum entropy +can be derived. Surprisingly, a variant model also exhibits the guarantee, and +for many cases obtains a higher performance score than the product expansion. +",Entropy and Belief Networks +" We report on an experimental investigation into opportunities for parallelism +in beliefnet inference. Specifically, we report on a study performed of the +available parallelism, on hypercube style machines, of a set of randomly +generated belief nets, using factoring (SPI) style inference algorithms. Our +results indicate that substantial speedup is available, but that it is +available only through parallelization of individual conformal product +operations, and depends critically on finding an appropriate factoring. We find +negligible opportunity for parallelism at the topological, or clustering tree, +level. +",Parallelizing Probabilistic Inference: Some Early Explorations +" This paper introduces the notion of objection-based causal networks which +resemble probabilistic causal networks except that they are quantified using +objections. An objection is a logical sentence and denotes a condition under +which a, causal dependency does not exist. Objection-based causal networks +enjoy almost all the properties that make probabilistic causal networks +popular, with the added advantage that objections are, arguably more intuitive +than probabilities. +",Objection-Based Causal Networks +" This paper investigates the possibility of performing automated reasoning in +probabilistic logic when probabilities are expressed by means of linguistic +quantifiers. Each linguistic term is expressed as a prescribed interval of +proportions. Then instead of propagating numbers, qualitative terms are +propagated in accordance with the numerical interpretation of these terms. The +quantified syllogism, modelling the chaining of probabilistic rules, is studied +in this context. It is shown that a qualitative counterpart of this syllogism +makes sense, and is relatively independent of the threshold defining the +linguistically meaningful intervals, provided that these threshold values +remain in accordance with the intuition. The inference power is less than that +of a full-fledged probabilistic con-quaint propagation device but better +corresponds to what could be thought of as commonsense probabilistic reasoning. +",A Symbolic Approach to Reasoning with Linguistic Quantifiers +" Data fusion allows the elaboration and the evaluation of a situation +synthesized from low level informations provided by different kinds of sensors. +The fusion of the collected data will result in fewer and higher level +informations more easily assessed by a human operator and that will assist him +effectively in his decision process. In this paper we present the suitability +and the advantages of using a Possibilistic Assumption based Truth Maintenance +System (n-ATMS) in a data fusion military application. We first describe the +problem, the needed knowledge representation formalisms and problem solving +paradigms. Then we remind the reader of the basic concepts of ATMSs, +Possibilistic Logic and 11-ATMSs. Finally we detail the solution to the given +data fusion problem and conclude with the results and comparison with a +non-possibilistic solution. +","Possibilistic Assumption based Truth Maintenance System, Validation in a + Data Fusion Application" +" In the probabilistic approach to uncertainty management the input knowledge +is usually represented by means of some probability distributions. In this +paper we assume that the input knowledge is given by two discrete conditional +probability distributions, represented by two stochastic matrices P and Q. The +consistency of the knowledge base is analyzed. Coherence conditions and +explicit formulas for the extension to marginal distributions are obtained in +some special cases. +",Knowledge Integration for Conditional Probability Assessments +" To date, most probabilistic reasoning systems have relied on a fixed belief +network constructed at design time. The network is used by an application +program as a representation of (in)dependencies in the domain. Probabilistic +inference algorithms operate over the network to answer queries. Recognizing +the inflexibility of fixed models has led researchers to develop automated +network construction procedures that use an expressive knowledge base to +generate a network that can answer a query. Although more flexible than fixed +model approaches, these construction procedures separate construction and +evaluation into distinct phases. In this paper we develop an approach to +combining incremental construction and evaluation of a partial probability +model. The combined method holds promise for improved methods for control of +model construction based on a trade-off between fidelity of results and cost of +construction. +",Integrating Model Construction and Evaluation +" We recently described a formalism for reasoning with if-then rules that re +expressed with different levels of firmness [18]. The formalism interprets +these rules as extreme conditional probability statements, specifying orders of +magnitude of disbelief, which impose constraints over possible rankings of +worlds. It was shown that, once we compute a priority function Z+ on the rules, +the degree to which a given query is confirmed or denied can be computed in +O(log n`) propositional satisfiability tests, where n is the number of rules in +the knowledge base. In this paper, we show that computing Z+ requires O(n2 X +log n) satisfiability tests, not an exponential number as was conjectured in +[18], which reduces to polynomial complexity in the case of Horn expressions. +We also show how reasoning with imprecise observations can be incorporated in +our formalism and how the popular notions of belief revision and epistemic +entrenchment are embodied naturally and tractably. +",Reasoning With Qualitative Probabilities Can Be Tractable +" A computational scheme for reasoning about dynamic systems using (causal) +probabilistic networks is presented. The scheme is based on the framework of +Lauritzen and Spiegelhalter (1988), and may be viewed as a generalization of +the inference methods of classical time-series analysis in the sense that it +allows description of non-linear, multivariate dynamic systems with complex +conditional independence structures. Further, the scheme provides a method for +efficient backward smoothing and possibilities for efficient, approximate +forecasting methods. The scheme has been implemented on top of the HUGIN shell. +",A computational scheme for Reasoning in Dynamic Probabilistic Networks +" The fundamental updating process in the transferable belief model is related +to the concept of specialization and can be described by a specialization +matrix. The degree of belief in the truth of a proposition is a degree of +justified support. The Principle of Minimal Commitment implies that one should +never give more support to the truth of a proposition than justified. We show +that Dempster's rule of conditioning corresponds essentially to the least +committed specialization, and that Dempster's rule of combination results +essentially from commutativity requirements. The concept of generalization, +dual to thc concept of specialization, is described. +","The Dynamic of Belief in the Transferable Belief Model and + Specialization-Generalization Matrices" +" A new entropy-like measure as well as a new measure of total uncertainty +pertaining to the Dempster-Shafer theory are introduced. It is argued that +these measures are better justified than any of the previously proposed +candidates. +",A Note on the Measure of Discord +" A number of writers(Joseph Halpern and Fahiem Bacchus among them) have +offered semantics for formal languages in which inferences concerning +probabilities can be made. Our concern is different. This paper provides a +formalization of nonmonotonic inferences in which the conclusion is supported +only to a certain degree. Such inferences are clearly 'invalid' since they must +allow the falsity of a conclusion even when the premises are true. +Nevertheless, such inferences can be characterized both syntactically and +semantically. The 'premises' of probabilistic arguments are sets of statements +(as in a database or knowledge base), the conclusions categorical statements in +the language. We provide standards for both this form of inference, for which +high probability is required, and for an inference in which the conclusion is +qualified by an intermediate interval of support. +",Semantics for Probabilistic Inference +" We discuss problems for convex Bayesian decision making and uncertainty +representation. These include the inability to accommodate various natural and +useful constraints and the possibility of an analog of the classical Dutch Book +being made against an agent behaving in accordance with convex Bayesian +prescriptions. A more general set-based Bayesianism may be as tractable and +would avoid the difficulties we raise. +",Some Problems for Convex Bayesians +" This paper presents a Bayesian framework for assessing the adequacy of a +model without the necessity of explicitly enumerating a specific alternate +model. A test statistic is developed for tracking the performance of the model +across repeated problem instances. Asymptotic methods are used to derive an +approximate distribution for the test statistic. When the model is rejected, +the individual components of the test statistic can be used to guide search for +an alternate model. +","Bayesian Meta-Reasoning: Determining Model Adequacy from Within a Small + World" +" The ideal Bayesian agent reasons from a global probability model, but real +agents are restricted to simplified models which they know to be adequate only +in restricted circumstances. Very little formal theory has been developed to +help fallibly rational agents manage the process of constructing and revising +small world models. The goal of this paper is to present a theoretical +framework for analyzing model management approaches. For a probability +forecasting problem, a search process over small world models is analyzed as an +approximation to a larger-world model which the agent cannot explicitly +enumerate or compute. Conditions are given under which the sequence of +small-world models converges to the larger-world probabilities. +",The Bounded Bayesian +" Automated decision making is often complicated by the complexity of the +knowledge involved. Much of this complexity arises from the context sensitive +variations of the underlying phenomena. We propose a framework for representing +descriptive, context-sensitive knowledge. Our approach attempts to integrate +categorical and uncertain knowledge in a network formalism. This paper outlines +the basic representation constructs, examines their expressiveness and +efficiency, and discusses the potential applications of the framework. +","Representing Context-Sensitive Knowledge in a Network Formalism: A + Preliminary Report" +" Bayesian networks are directed acyclic graphs representing independence +relationships among a set of random variables. A random variable can be +regarded as a set of exhaustive and mutually exclusive propositions. We argue +that there are several drawbacks resulting from the propositional nature and +acyclic structure of Bayesian networks. To remedy these shortcomings, we +propose a probabilistic network where nodes represent unary predicates and +which may contain directed cycles. The proposed representation allows us to +represent domain knowledge in a single static network even though we cannot +determine the instantiations of the predicates before hand. The ability to deal +with cycles also enables us to handle cyclic causal tendencies and to recognize +recursive plans. +",A Probabilistic Network of Predicates +" The Dempster-Shafer theory of evidence has been used intensively to deal with +uncertainty in knowledge-based systems. However the representation of uncertain +relationships between evidence and hypothesis groups (heuristic knowledge) is +still a major research problem. This paper presents an approach to representing +such heuristic knowledge by evidential mappings which are defined on the basis +of mass functions. The relationships between evidential mappings and multi +valued mappings, as well as between evidential mappings and Bayesian multi- +valued causal link models in Bayesian theory are discussed. Following this the +detailed procedures for constructing evidential mappings for any set of +heuristic rules are introduced. Several situations of belief propagation are +discussed. +",Representing Heuristic Knowledge in D-S Theory +" Bayes nets are relatively recent innovations. As a result, most of their +theoretical development has focused on the simplest class of single-author +models. The introduction of more sophisticated multiple-author settings raises +a variety of interesting questions. One such question involves the nature of +compromise and consensus. Posterior compromises let each model process all data +to arrive at an independent response, and then split the difference. Prior +compromises, on the other hand, force compromise to be reached on all points +before data is observed. This paper introduces prior compromises in a Bayes net +setting. It outlines the problem and develops an efficient algorithm for fusing +two directed acyclic graphs into a single, consensus structure, which may then +be used as the basis of a prior compromise. +",The Topological Fusion of Bayes Nets +" In Moral, Campos (1991) and Cano, Moral, Verdegay-Lopez (1991) a new method +of conditioning convex sets of probabilities has been proposed. The result of +it is a convex set of non-necessarily normalized probability distributions. The +normalizing factor of each probability distribution is interpreted as the +possibility assigned to it by the conditioning information. From this, it is +deduced that the natural value for the conditional probability of an event is a +possibility distribution. The aim of this paper is to study methods of +transforming this possibility distribution into a probability (or uncertainty) +interval. These methods will be based on the use of Sugeno and Choquet +integrals. Their behaviour will be compared in basis to some selected examples. +","Calculating Uncertainty Intervals From Conditional Convex Sets of + Probabilities" +" The trajectory of a robot is monitored in a restricted dynamic environment +using light beam sensor data. We have a Dynamic Belief Network (DBN), based on +a discrete model of the domain, which provides discrete monitoring analogous to +conventional quantitative filter techniques. Sensor observations are added to +the basic DBN in the form of specific evidence. However, sensor data is often +partially or totally incorrect. We show how the basic DBN, which infers only an +impossible combination of evidence, may be modified to handle specific types of +incorrect data which may occur in the domain. We then present an extension to +the DBN, the addition of an invalidating node, which models the status of the +sensor as working or defective. This node provides a qualitative explanation of +inconsistent data: it is caused by a defective sensor. The connection of +successive instances of the invalidating node models the status of a sensor +over time, allowing the DBN to handle both persistent and intermittent faults. +",Sensor Validation Using Dynamic Belief Networks +" The paper describes aHUGIN, a tool for creating adaptive systems. aHUGIN is +an extension of the HUGIN shell, and is based on the methods reported by +Spiegelhalter and Lauritzen (1990a). The adaptive systems resulting from aHUGIN +are able to adjust the C011ditional probabilities in the model. A short +analysis of the adaptation task is given and the features of aHUGIN are +described. Finally a session with experiments is reported and the results are +discussed. +",aHUGIN: A System Creating Adaptive Causal Probabilistic Networks +" Probabilistic reasoning systems combine different probabilistic rules and +probabilistic facts to arrive at the desired probability values of +consequences. In this paper we describe the MESA-algorithm (Maximum Entropy by +Simulated Annealing) that derives a joint distribution of variables or +propositions. It takes into account the reliability of probability values and +can resolve conflicts between contradictory statements. The joint distribution +is represented in terms of marginal distributions and therefore allows to +process large inference networks and to determine desired probability values +with high precision. The procedure derives a maximum entropy distribution +subject to the given constraints. It can be applied to inference networks of +arbitrary topology and may be extended into a number of directions. +",MESA: Maximum Entropy by Simulated Annealing +" This paper describes some results of research on associate systems: +knowledge-based systems that flexibly and adaptively support their human users +in carrying out complex, time-dependent problem-solving tasks under +uncertainty. Based on principles derived from decision theory and decision +analysis, a problem-solving approach is presented which can overcome many of +the limitations of traditional expert-systems. This approach implements an +explicit model of the human user's problem-solving capabilities as an integral +element in the overall problem solving architecture. This integrated model, +represented as an influence diagram, is the basis for achieving adaptive task +sharing behavior between the associate system and the human user. This +associate system model has been applied toward ongoing research on a Mars Rover +Manager's Associate (MRMA). MRMA's role would be to manage a small fleet of +robotic rovers on the Martian surface. The paper describes results for a +specific scenario where MRMA examines the benefits and costs of consulting +human experts on Earth to assist a Mars rover with a complex resource +management decision. +",Decision Methods for Adaptive Task-Sharing in Associate Systems +" Although the notion of diagnostic problem has been extensively investigated +in the context of static systems, in most practical applications the behavior +of the modeled system is significantly variable during time. The goal of the +paper is to propose a novel approach to the modeling of uncertainty about +temporal evolutions of time-varying systems and a characterization of +model-based temporal diagnosis. Since in most real world cases knowledge about +the temporal evolution of the system to be diagnosed is uncertain, we consider +the case when probabilistic temporal knowledge is available for each component +of the system and we choose to model it by means of Markov chains. In fact, we +aim at exploiting the statistical assumptions underlying reliability theory in +the context of the diagnosis of timevarying systems. We finally show how to +exploit Markov chain theory in order to discard, in the diagnostic process, +very unlikely diagnoses. +",Modeling Uncertain Temporal Evolutions in Model-Based Diagnosis +" An expert classification system having statistical information about the +prior probabilities of the different classes should be able to use this +knowledge to reduce the amount of additional information that it must collect, +e.g., through questions, in order to make a correct classification. This paper +examines how best to use such prior information and additional +information-collection opportunities to reduce uncertainty about the class to +which a case belongs, thus minimizing the average cost or effort required to +correctly classify new cases. +","Guess-And-Verify Heuristics for Reducing Uncertainties in Expert + Classification Systems" +" This paper describes the architecture of R&D Analyst, a commercial +intelligent decision system for evaluating corporate research and development +projects and portfolios. In analyzing projects, R&D Analyst interactively +guides a user in constructing an influence diagram model for an individual +research project. The system's interactive approach can be clearly explained +from a blackboard system perspective. The opportunistic reasoning emphasis of +blackboard systems satisfies the flexibility requirements of model +construction, thereby suggesting that a similar architecture would be valuable +for developing normative decision systems in other domains. Current research is +aimed at extending the system architecture to explicitly consider of sequential +decisions involving limited temporal, financial, and physical resources. +","R&D Analyst: An Interactive Approach to Normative Decision System Model + Construction" +" Many AI synthesis problems such as planning or scheduling may be modelized as +constraint satisfaction problems (CSP). A CSP is typically defined as the +problem of finding any consistent labeling for a fixed set of variables +satisfying all given constraints between these variables. However, for many +real tasks such as job-shop scheduling, time-table scheduling, design?, all +these constraints have not the same significance and have not to be necessarily +satisfied. A first distinction can be made between hard constraints, which +every solution should satisfy and soft constraints, whose satisfaction has not +to be certain. In this paper, we formalize the notion of possibilistic +constraint satisfaction problems that allows the modeling of uncertainly +satisfied constraints. We use a possibility distribution over labelings to +represent respective possibilities of each labeling. Necessity-valued +constraints allow a simple expression of the respective certainty degrees of +each constraint. The main advantage of our approach is its integration in the +CSP technical framework. Most classical techniques, such as Backtracking (BT), +arcconsistency enforcing (AC) or Forward Checking have been extended to handle +possibilistics CSP and are effectively implemented. The utility of our approach +is demonstrated on a simple design problem. +","Possibilistic Constraint Satisfaction Problems or ""How to handle soft + constraints?""" +" The analysis of decision making under uncertainty is closely related to the +analysis of probabilistic inference. Indeed, much of the research into +efficient methods for probabilistic inference in expert systems has been +motivated by the fundamental normative arguments of decision theory. In this +paper we show how the developments underlying those efficient methods can be +applied immediately to decision problems. In addition to general approaches +which need know nothing about the actual probabilistic inference method, we +suggest some simple modifications to the clustering family of algorithms in +order to efficiently incorporate decision making capabilities. +",Decision Making Using Probabilistic Inference Methods +" This paper introduces the notions of independence and conditional +independence in valuation-based systems (VBS). VBS is an axiomatic framework +capable of representing many different uncertainty calculi. We define +independence and conditional independence in terms of factorization of the +joint valuation. The definitions of independence and conditional independence +in VBS generalize the corresponding definitions in probability theory. Our +definitions apply not only to probability theory, but also to Dempster-Shafer's +belief-function theory, Spohn's epistemic-belief theory, and Zadeh's +possibility theory. In fact, they apply to any uncertainty calculi that fit in +the framework of valuation-based systems. +",Conditional Independence in Uncertainty Theories +" Within the transferable belief model, positive basic belief masses can be +allocated to the empty set, leading to unnormalized belief functions. The +nature of these unnormalized beliefs is analyzed. +","The Nature of the Unnormalized Beliefs Encountered in the Transferable + Belief Model" +" The general use of subjective probabilities to model belief has been +justified using many axiomatic schemes. For example, ?consistent betting +behavior' arguments are well-known. To those not already convinced of the +unique fitness and generality of probability models, such justifications are +often unconvincing. The present paper explores another rationale for +probability models. ?Qualitative probability,' which is known to provide +stringent constraints on belief representation schemes, is derived from five +simple assumptions about relationships among beliefs. While counterparts of +familiar rationality concepts such as transitivity, dominance, and consistency +are used, the betting context is avoided. The gap between qualitative +probability and probability proper can be bridged by any of several additional +assumptions. The discussion here relies on results common in the recent AI +literature, introducing a sixth simple assumption. The narrative emphasizes +models based on unique complete orderings, but the rationale extends easily to +motivate set-valued representations of partial orderings as well. +",Intuitions about Ordered Beliefs Leading to Probabilistic Models +" Bayesian networks have been used extensively in diagnostic tasks such as +medicine, where they represent the dependency relations between a set of +symptoms and a set of diseases. A criticism of this type of knowledge +representation is that it is restricted to this kind of task, and that it +cannot cope with the knowledge required in other artificial intelligence +applications. For example, in computer vision, we require the ability to model +complex knowledge, including temporal and relational factors. In this paper we +extend Bayesian networks to model relational and temporal knowledge for +high-level vision. These extended networks have a simple structure which +permits us to propagate probability efficiently. We have applied them to the +domain of endoscopy, illustrating how the general modelling principles can be +used in specific cases. +","Expressing Relational and Temporal Knowledge in Visual Probabilistic + Networks" +" This paper discusses a target tracking problem in which no dynamic +mathematical model is explicitly assumed. A nonlinear filter based on the fuzzy +If-then rules is developed. A comparison with a Kalman filter is made, and +empirical results show that the performance of the fuzzy filter is better. +Intensive simulations suggest that theoretical justification of the empirical +results is possible. +",A Fuzzy Logic Approach to Target Tracking +" The DUCK-calculus presented here is a recent approach to cope with +probabilistic uncertainty in a sound and efficient way. Uncertain rules with +bounds for probabilities and explicit conditional independences can be +maintained incrementally. The basic inference mechanism relies on local bounds +propagation, implementable by deductive databases with a bottom-up fixpoint +evaluation. In situations, where no precise bounds are deducible, it can be +combined with simple operations research techniques on a local scope. In +particular, we provide new precise analytical bounds for probabilistic +entailment. +",Towards Precision of Probabilistic Bounds Propagation +" In a previous paper [Pearl and Verma, 1991] we presented an algorithm for +extracting causal influences from independence information, where a causal +influence was defined as the existence of a directed arc in all minimal causal +models consistent with the data. In this paper we address the question of +deciding whether there exists a causal model that explains ALL the observed +dependencies and independencies. Formally, given a list M of conditional +independence statements, it is required to decide whether there exists a +directed acyclic graph (dag) D that is perfectly consistent with M, namely, +every statement in M, and no other, is reflected via dseparation in D. We +present and analyze an effective algorithm that tests for the existence of such +a day, and produces one, if it exists. +","An Algorithm for Deciding if a Set of Observed Independencies Has a + Causal Explanation" +" Jeffrey's rule has been generalized by Wagner to the case in which new +evidence bounds the possible revisions of a prior probability below by a +Dempsterian lower probability. Classical probability kinematics arises within +this generalization as the special case in which the evidentiary focal elements +of the bounding lower probability are pairwise disjoint. We discuss a twofold +extension of this generalization, first allowing the lower bound to be any +two-monotone capacity and then allowing the prior to be a lower envelope. +",Generalizing Jeffrey Conditionalization +" In this paper, a unified framework for representing uncertain information +based on the notion of an interval structure is proposed. It is shown that the +lower and upper approximations of the rough-set model, the lower and upper +bounds of incidence calculus, and the belief and plausibility functions all +obey the axioms of an interval structure. An interval structure can be used to +synthesize the decision rules provided by the experts. An efficient algorithm +to find the desirable set of rules is developed from a set of sound and +complete inference axioms. +",Interval Structure: A Framework for Representing Uncertain Information +" Current Bayesian net representations do not consider structure in the domain +and include all variables in a homogeneous network. At any time, a human +reasoner in a large domain may direct his attention to only one of a number of +natural subdomains, i.e., there is ?localization' of queries and evidence. In +such a case, propagating evidence through a homogeneous network is inefficient +since the entire network has to be updated each time. This paper presents +multiply sectioned Bayesian networks that enable a (localization preserving) +representation of natural subdomains by separate Bayesian subnets. The subnets +are transformed into a set of permanent junction trees such that evidential +reasoning takes place at only one of them at a time. Probabilities obtained are +identical to those that would be obtained from the homogeneous network. We +discuss attention shift to a different junction tree and propagation of +previously acquired evidence. Although the overall system can be large, +computational requirements are governed by the size of only one junction tree. +",Exploring Localization in Bayesian Networks for Large Expert Systems +" Valuation-based system (VBS) provides a general framework for representing +knowledge and drawing inferences under uncertainty. Recent studies have shown +that the semantics of VBS can represent and solve Bayesian decision problems +(Shenoy, 1991a). The purpose of this paper is to propose a decision calculus +for Dempster-Shafer (D-S) theory in the framework of VBS. The proposed calculus +uses a weighting factor whose role is similar to the probabilistic +interpretation of an assumption that disambiguates decision problems +represented with belief functions (Strat 1990). It will be shown that with the +presented calculus, if the decision problems are represented in the valuation +network properly, we can solve the problems by using fusion algorithm (Shenoy +1991a). It will also be shown the presented decision calculus can be reduced to +the calculus for Bayesian probability theory when probabilities, instead of +belief functions, are given. +",A Decision Calculus for Belief Functions in Valuation-Based Systems +" This paper presents a new approach for computing posterior probabilities in +Bayesian nets, which sidesteps the triangulation problem. The current state of +art is the clique tree propagation approach. When the underlying graph of a +Bayesian net is triangulated, this approach arranges its cliques into a tree +and computes posterior probabilities by appropriately passing around messages +in that tree. The computation in each clique is simply direct marginalization. +When the underlying graph is not triangulated, one has to first triangulated it +by adding edges. Referred to as the triangulation problem, the problem of +finding an optimal or even a ?good? triangulation proves to be difficult. In +this paper, we propose to first decompose a Bayesian net into smaller +components by making use of Tarjan's algorithm for decomposing an undirected +graph at all its minimal complete separators. Then, the components are arranged +into a tree and posterior probabilities are computed by appropriately passing +around messages in that tree. The computation in each component is carried out +by repeating the whole procedure from the beginning. Thus the triangulation +problem is sidestepped. +",Sidestepping the Triangulation Problem in Bayesian Net Computations +" VT (Viterbi training), or hard EM, is an efficient way of parameter learning +for probabilistic models with hidden variables. Given an observation $y$, it +searches for a state of hidden variables $x$ that maximizes $p(x,y \mid +\theta)$ by coordinate ascent on parameters $\theta$ and $x$. In this paper we +introduce VT to PRISM, a logic-based probabilistic modeling system for +generative models. VT improves PRISM in three ways. First VT in PRISM converges +faster than EM in PRISM due to the VT's termination condition. Second, +parameters learned by VT often show good prediction performance compared to +those learned by EM. We conducted two parsing experiments with probabilistic +grammars while learning parameters by a variety of inference methods, i.e.\ VT, +EM, MAP and VB. The result is that VT achieved the best parsing accuracy among +them in both experiments. Also we conducted a similar experiment for +classification tasks where a hidden variable is not a prediction target unlike +probabilistic grammars. We found that in such a case VT does not necessarily +yield superior performance. Third since VT always deals with a single +probability of a single explanation, Viterbi explanation, the exclusiveness +condition that is imposed on PRISM programs is no more required if we learn +parameters by VT. + Last but not least we can say that as VT in PRISM is general and applicable +to any PRISM program, it largely reduces the need for the user to develop a +specific VT algorithm for a specific model. Furthermore since VT in PRISM can +be used just by setting a PRISM flag appropriately, it makes VT easily +accessible to (probabilistic) logic programmers. To appear in Theory and +Practice of Logic Programming (TPLP). +",Viterbi training in PRISM +" This paper examines the interdependence generated between two parent nodes +with a common instantiated child node, such as two hypotheses sharing common +evidence. The relation so generated has been termed ""intercausal."" It is shown +by construction that inter-causal independence is possible for binary +distributions at one state of evidence. For such ""CICI"" distributions, the two +measures of inter-causal effect, ""multiplicative synergy"" and ""additive +synergy"" are equal. The well known ""noisy-or"" model is an example of such a +distribution. This introduces novel semantics for the noisy-or, as a model of +the degree of conflict among competing hypotheses of a common observation. +","""Conditional Inter-Causally Independent"" Node Distributions, a Property + of ""Noisy-Or"" Models" +" The way experts manage uncertainty usually changes depending on the task they +are performing. This fact has lead us to consider the problem of communicating +modules (task implementations) in a large and structured knowledge based system +when modules have different uncertainty calculi. In this paper, the analysis of +the communication problem is made assuming that (i) each uncertainty calculus +is an inference mechanism defining an entailment relation, and therefore the +communication is considered to be inference-preserving, and (ii) we restrict +ourselves to the case which the different uncertainty calculi are given by a +class of truth functional Multiple-valued Logics. +",Combining Multiple-Valued Logics in Modular Expert Systems +" An approach to reasoning with default rules where the proportion of +exceptions, or more generally the probability of encountering an exception, can +be at least roughly assessed is presented. It is based on local uncertainty +propagation rules which provide the best bracketing of a conditional +probability of interest from the knowledge of the bracketing of some other +conditional probabilities. A procedure that uses two such propagation rules +repeatedly is proposed in order to estimate any simple conditional probability +of interest from the available knowledge. The iterative procedure, that does +not require independence assumptions, looks promising with respect to the +linear programming method. Improved bounds for conditional probabilities are +given when independence assumptions hold. +",Constraint Propagation with Imprecise Conditional Probabilities +" This paper presents a plausible reasoning system to illustrate some broad +issues in knowledge representation: dualities between different reasoning +forms, the difficulty of unifying complementary reasoning styles, and the +approximate nature of plausible reasoning. These issues have a common +underlying theme: there should be an underlying belief calculus of which the +many different reasoning forms are special cases, sometimes approximate. The +system presented allows reasoning about defaults, likelihood, necessity and +possibility in a manner similar to the earlier work of Adams. The system is +based on the belief calculus of subjective Bayesian probability which itself is +based on a few simple assumptions about how belief should be manipulated. +Approximations, semantics, consistency and consequence results are presented +for the system. While this puts these often discussed plausible reasoning forms +on a probabilistic footing, useful application to practical problems remains an +issue. +",Some Properties of Plausible Reasoning +" Theory refinement is the task of updating a domain theory in the light of new +cases, to be done automatically or with some expert assistance. The problem of +theory refinement under uncertainty is reviewed here in the context of Bayesian +statistics, a theory of belief revision. The problem is reduced to an +incremental learning task as follows: the learning system is initially primed +with a partial theory supplied by a domain expert, and thereafter maintains its +own internal representation of alternative theories which is able to be +interrogated by the domain expert and able to be incrementally refined from +data. Algorithms for refinement of Bayesian networks are presented to +illustrate what is meant by ""partial theory"", ""alternative theory +representation"", etc. The algorithms are an incremental variant of batch +learning algorithms from the literature so can work well in batch and +incremental mode. +",Theory Refinement on Bayesian Networks +" In this paper, we consider several types of information and methods of +combination associated with incomplete probabilistic systems. We discriminate +between 'a priori' and evidential information. The former one is a description +of the whole population, the latest is a restriction based on observations for +a particular case. Then, we propose different combination methods for each one +of them. We also consider conditioning as the heterogeneous combination of 'a +priori' and evidential information. The evidential information is represented +as a convex set of likelihood functions. These will have an associated +possibility distribution with behavior according to classical Possibility +Theory. +",Combination of Upper and Lower Probabilities +" Useless paths are a chronic problem for marker-passing techniques. We use a +probabilistic analysis to justify a method for quickly identifying and +rejecting useless paths. Using the same analysis, we identify key conditions +and assumptions necessary for marker-passing to perform well. +","A Probabilistic Analysis of Marker-Passing Techniques for + Plan-Recognition" +" Research on Symbolic Probabilistic Inference (SPI) [2, 3] has provided an +algorithm for resolving general queries in Bayesian networks. SPI applies the +concept of dependency directed backward search to probabilistic inference, and +is incremental with respect to both queries and observations. Unlike +traditional Bayesian network inferencing algorithms, SPI algorithm is goal +directed, performing only those calculations that are required to respond to +queries. Research to date on SPI applies to Bayesian networks with +discrete-valued variables and does not address variables with continuous +values. In this papers, we extend the SPI algorithm to handle Bayesian networks +made up of continuous variables where the relationships between the variables +are restricted to be ?linear gaussian?. We call this variation of the SPI +algorithm, SPI Continuous (SPIC). SPIC modifies the three basic SPI operations: +multiplication, summation, and substitution. However, SPIC retains the +framework of the SPI algorithm, namely building the search tree and recursive +query mechanism and therefore retains the goal-directed and incrementality +features of SPI. +",Symbolic Probabilistic Inference with Continuous Variables +" Recent research on the Symbolic Probabilistic Inference (SPI) algorithm[2] +has focused attention on the importance of resolving general queries in +Bayesian networks. SPI applies the concept of dependency-directed backward +search to probabilistic inference, and is incremental with respect to both +queries and observations. In response to this research we have extended the +evidence potential algorithm [3] with the same features. We call the extension +symbolic evidence potential inference (SEPI). SEPI like SPI can handle generic +queries and is incremental with respect to queries and observations. While in +SPI, operations are done on a search tree constructed from the nodes of the +original network, in SEPI, a clique-tree structure obtained from the evidence +potential algorithm [3] is the basic framework for recursive query processing. +In this paper, we describe the systematic query and caching procedure of SEPI. +SEPI begins with finding a clique tree from a Bayesian network-the standard +procedure of the evidence potential algorithm. With the clique tree, various +probability distributions are computed and stored in each clique. This is the +?pre-processing? step of SEPI. Once this step is done, the query can then be +computed. To process a query, a recursive process similar to the SPI algorithm +is used. The queries are directed to the root clique and decomposed into +queries for the clique's subtrees until a particular query can be answered at +the clique at which it is directed. The algorithm and the computation are +simple. The SEPI algorithm will be presented in this paper along with several +examples. +",Symbolic Probabilistic Inference with Evidence Potential +" This paper presents a Bayesian method for constructing Bayesian belief +networks from a database of cases. Potential applications include +computer-assisted hypothesis testing, automated scientific discovery, and +automated construction of probabilistic expert systems. Results are presented +of a preliminary evaluation of an algorithm for constructing a belief network +from a database of cases. We relate the methods in this paper to previous work, +and we discuss open problems. +","A Bayesian Method for Constructing Bayesian Belief Networks from + Databases" +" We present a generalization of the local expression language used in the +Symbolic Probabilistic Inference (SPI) approach to inference in belief nets +[1l, [8]. The local expression language in SPI is the language in which the +dependence of a node on its antecedents is described. The original language +represented the dependence as a single monolithic conditional probability +distribution. The extended language provides a set of operators (*, +, and -) +which can be used to specify methods for combining partial conditional +distributions. As one instance of the utility of this extension, we show how +this extended language can be used to capture the semantics, representational +advantages, and inferential complexity advantages of the ""noisy or"" +relationship. +","Local Expression Languages for Probabilistic Dependence: a Preliminary + Report" +" The ability to reason under uncertainty and with incomplete information is a +fundamental requirement of decision support technology. In this paper we argue +that the concentration on theoretical techniques for the evaluation and +selection of decision options has distracted attention from many of the wider +issues in decision making. Although numerical methods of reasoning under +uncertainty have strong theoretical foundations, they are representationally +weak and only deal with a small part of the decision process. Knowledge based +systems, on the other hand, offer greater flexibility but have not been +accompanied by a clear decision theory. We describe here work which is under +way towards providing a theoretical framework for symbolic decision procedures. +A central proposal is an extended form of inference which we call +argumentation; reasoning for and against decision options from generalised +domain theories. The approach has been successfully used in several decision +support applications, but it is argued that a comprehensive decision theory +must cover autonomous decision making, where the agent can formulate questions +as well as take decisions. A major theoretical challenge for this theory is to +capture the idea of reflection to permit decision agents to reason about their +goals, what they believe and why, and what they need to know or do in order to +achieve their goals. +",Symbolic Decision Theory and Autonomous Systems +" A reason maintenance system which extends an ATMS through Mukaidono's fuzzy +logic is described. It supports a problem solver in situations affected by +incomplete information and vague data, by allowing nonmonotonic inferences and +the revision of previous conclusions when contradictions are detected. +",A Reason Maintenace System Dealing with Vague Data +" This paper discuses multiple Bayesian networks representation paradigms for +encoding asymmetric independence assertions. We offer three contributions: (1) +an inference mechanism that makes explicit use of asymmetric independence to +speed up computations, (2) a simplified definition of similarity networks and +extensions of their theory, and (3) a generalized representation scheme that +encodes more types of asymmetric independence assertions than do similarity +networks. +",Advances in Probabilistic Reasoning +" In this paper, we consider one aspect of the problem of applying decision +theory to the design of agents that learn how to make decisions under +uncertainty. This aspect concerns how an agent can estimate probabilities for +the possible states of the world, given that it only makes limited observations +before committing to a decision. We show that the naive application of +statistical tools can be improved upon if the agent can determine which of his +observations are truly relevant to the estimation problem at hand. We give a +framework in which such determinations can be made, and define an estimation +procedure to use them. Our framework also suggests several extensions, which +show how additional knowledge can be used to improve tile estimation procedure +still further. +",Probability Estimation in Face of Irrelevant Information +" Value-of-information analyses provide a straightforward means for selecting +the best next observation to make, and for determining whether it is better to +gather additional information or to act immediately. Determining the next best +test to perform, given a state of uncertainty about the world, requires a +consideration of the value of making all possible sequences of observations. In +practice, decision analysts and expert-system designers have avoided the +intractability of exact computation of the value of information by relying on a +myopic approximation. Myopic analyses are based on the assumption that only one +additional test will be performed, even when there is an opportunity to make a +large number of observations. We present a nonmyopic approximation for value of +information that bypasses the traditional myopic analyses by exploiting the +statistical properties of large samples. +",An Approximate Nonmyopic Computation for Value of Information +" Since exact probabilistic inference is intractable in general for large +multiply connected belief nets, approximate methods are required. A promising +approach is to use heuristic search among hypotheses (instantiations of the +network) to find the most probable ones, as in the TopN algorithm. Search is +based on the relative probabilities of hypotheses which are efficient to +compute. Given upper and lower bounds on the relative probability of partial +hypotheses, it is possible to obtain bounds on the absolute probabilities of +hypotheses. Best-first search aimed at reducing the maximum error progressively +narrows the bounds as more hypotheses are examined. Here, qualitative +probabilistic analysis is employed to obtain bounds on the relative probability +of partial hypotheses for the BN20 class of networks networks and a +generalization replacing the noisy OR assumption by negative synergy. The +approach is illustrated by application to a very large belief network, QMR-BN, +which is a reformulation of the Internist-1 system for diagnosis in internal +medicine. +","Search-based Methods to Bound Diagnostic Probabilities in Very Large + Belief Nets" +" We discuss representing and reasoning with knowledge about the time-dependent +utility of an agent's actions. Time-dependent utility plays a crucial role in +the interaction between computation and action under bounded resources. We +present a semantics for time-dependent utility and describe the use of +time-dependent information in decision contexts. We illustrate our discussion +with examples of time-pressured reasoning in Protos, a system constructed to +explore the ideal control of inference by reasoners with limit abilities. +",Time-Dependent Utility and Action Under Uncertainty +" Traditional approaches to non-monotonic reasoning fail to satisfy a number of +plausible axioms for belief revision and suffer from conceptual difficulties as +well. Recent work on ranked preferential models (RPMs) promises to overcome +some of these difficulties. Here we show that RPMs are not adequate to handle +iterated belief change. Specifically, we show that RPMs do not always allow for +the reversibility of belief change. This result indicates the need for +numerical strengths of belief. +",Non-monotonic Reasoning and the Reversibility of Belief Change +" We motivate and describe a theory of belief in this paper. This theory is +developed with the following view of human belief in mind. Consider the belief +that an event E will occur (or has occurred or is occurring). An agent either +entertains this belief or does not entertain this belief (i.e., there is no +""grade"" in entertaining the belief). If the agent chooses to exercise ""the will +to believe"" and entertain this belief, he/she/it is entitled to a degree of +confidence c (1 > c > 0) in doing so. Adopting this view of human belief, we +conjecture that whenever an agent entertains the belief that E will occur with +c degree of confidence, the agent will be surprised (to the extent c) upon +realizing that E did not occur. +",Belief and Surprise - A Belief-Function Formulation +" The categorial approach to evidential reasoning can be seen as a combination +of the probability kinematics approach of Richard Jeffrey (1965) and the +maximum (cross-) entropy inference approach of E. T. Jaynes (1957). As a +consequence of that viewpoint, it is well known that category theory provides +natural definitions for logical connectives. In particular, disjunction and +conjunction are modelled by general categorial constructions known as products +and coproducts. In this paper, I focus mainly on Dempster-Shafer theory of +belief functions for which I introduce a category I call Dempster?s category. I +prove the existence of and give explicit formulas for conjunction and +disjunction in the subcategory of separable belief functions. In Dempster?s +category, the new defined conjunction can be seen as the most cautious +conjunction of beliefs, and thus no assumption about distinctness (of the +sources) of beliefs is needed as opposed to Dempster?s rule of combination, +which calls for distinctness (of the sources) of beliefs. +","Evidential Reasoning in a Categorial Perspective: Conjunction and + Disjunction of Belief Functions" +" The concept of movable evidence masses that flow from supersets to subsets as +specified by experts represents a suitable framework for reasoning under +uncertainty. The mass flow is controlled by specialization matrices. New +evidence is integrated into the frame of discernment by conditioning or +revision (Dempster's rule of conditioning), for which special specialization +matrices exist. Even some aspects of non-monotonic reasoning can be represented +by certain specialization matrices. +",Reasoning with Mass Distributions +" Any probabilistic model of a problem is based on assumptions which, if +violated, invalidate the model. Users of probability based decision aids need +to be alerted when cases arise that are not covered by the aid's model. +Diagnosis of model failure is also necessary to control dynamic model +construction and revision. This paper presents a set of decision theoretically +motivated heuristics for diagnosing situations in which a model is likely to +provide an inadequate representation of the process being modeled. +",Conflict and Surprise: Heuristics for Model Revision +" A series of monte carlo studies were performed to compare the behavior of +some alternative procedures for reasoning under uncertainty. The behavior of +several Bayesian, linear model and default reasoning procedures were examined +in the context of increasing levels of calibration error. The most interesting +result is that Bayesian procedures tended to output more extreme posterior +belief values (posterior beliefs near 0.0 or 1.0) than other techniques, but +the linear models were relatively less likely to output strong support for an +erroneous conclusion. Also, accounting for the probabilistic dependencies +between evidence items was important for both Bayesian and linear updating +procedures. +",Reasoning under Uncertainty: Some Monte Carlo Results +" This paper outlines a methodology for analyzing the representational support +for knowledge-based decision-modeling in a broad domain. A relevant set of +inference patterns and knowledge types are identified. By comparing the +analysis results to existing representations, some insights are gained into a +design approach for integrating categorical and uncertain knowledge in a +context sensitive manner. +",Representation Requirements for Supporting Decision Model Formulation +" When a planner must decide whether it has enough evidence to make a decision +based on probability, it faces the sample size problem. Current planners using +probabilities need not deal with this problem because they do not generate +their probabilities from observations. This paper presents an event based +language in which the planner's probabilities are calculated from the binomial +random variable generated by the observed ratio of one type of event to +another. Such probabilities are subject to error, so the planner must +introspect about their validity. Inferences about the probability of these +events can be made using statistics. Inferences about the validity of the +approximations can be made using interval estimation. Interval estimation +allows the planner to avoid making choices that are only weakly supported by +the planner's evidence. +",A Language for Planning with Statistics +" Selecting the right reference class and the right interval when faced with +conflicting candidates and no possibility of establishing subset style +dominance has been a problem for Kyburg's Evidential Probability system. +Various methods have been proposed by Loui and Kyburg to solve this problem in +a way that is both intuitively appealing and justifiable within Kyburg's +framework. The scheme proposed in this paper leads to stronger statistical +assertions without sacrificing too much of the intuitive appeal of Kyburg's +latest proposal. +",A Modification to Evidential Probability +" The belief network is a well-known graphical structure for representing +independences in a joint probability distribution. The methods, which perform +probabilistic inference in belief networks, often treat the conditional +probabilities which are stored in the network as certain values. However, if +one takes either a subjectivistic or a limiting frequency approach to +probability, one can never be certain of probability values. An algorithm +should not only be capable of reporting the probabilities of the alternatives +of remaining nodes when other nodes are instantiated; it should also be capable +of reporting the uncertainty in these probabilities relative to the uncertainty +in the probabilities which are stored in the network. In this paper a method +for determining the variances in inferred probabilities is obtained under the +assumption that a posterior distribution on the uncertainty variables can be +approximated by the prior distribution. It is shown that this assumption is +plausible if their is a reasonable amount of confidence in the probabilities +which are stored in the network. Furthermore in this paper, a surprising upper +bound for the prior variances in the probabilities of the alternatives of all +nodes is obtained in the case where the probability distributions of the +probabilities of the alternatives are beta distributions. It is shown that the +prior variance in the probability at an alternative of a node is bounded above +by the largest variance in an element of the conditional probability +distribution for that node. +",Investigation of Variances in Belief Networks +" At last year?s Uncertainty in AI Conference, we reported the results of a +sensitivity analysis study of Pathfinder. Our findings were quite +unexpected-slight variations to Pathfinder?s parameters appeared to lead to +substantial degradations in system performance. A careful look at our first +analysis, together with the valuable feedback provided by the participants of +last year?s conference, led us to conduct a follow-up study. Our follow-up +differs from our initial study in two ways: (i) the probabilities 0.0 and 1.0 +remained unchanged, and (ii) the variations to the probabilities that are close +to both ends (0.0 or 1.0) were less than the ones close to the middle (0.5). +The results of the follow-up study look more reasonable-slight variations to +Pathfinder?s parameters now have little effect on its performance. Taken +together, these two sets of results suggest a viable extension of a common +decision analytic sensitivity analysis to the larger, more complex settings +generally encountered in artificial intelligence. +",A Sensitivity Analysis of Pathfinder: A Follow-up Study +" In this paper we study the uses and the semantics of non-monotonic negation +in probabilistic deductive data bases. Based on the stable semantics for +classical logic programming, we introduce the notion of stable formula, +functions. We show that stable formula, functions are minimal fixpoints of +operators associated with probabilistic deductive databases with negation. +Furthermore, since a. probabilistic deductive database may not necessarily have +a stable formula function, we provide a stable class semantics for such +databases. Finally, we demonstrate that the proposed semantics can handle +default reasoning naturally in the context of probabilistic deduction. +",Non-monotonic Negation in Probabilistic Deductive Databases +" We present a general architecture for the monitoring and diagnosis of large +scale sensor-based systems with real time diagnostic constraints. This +architecture is multileveled, combining a single monitoring level based on +statistical methods with two model based diagnostic levels. At each level, +sources of uncertainty are identified, and integrated methodologies for +uncertainty management are developed. The general architecture was applied to +the monitoring and diagnosis of a specific nuclear physics detector at Lawrence +Berkeley National Laboratory that contained approximately 5000 components and +produced over 500 channels of output data. The general architecture is +scalable, and work is ongoing to apply it to detector systems one and two +orders of magnitude more complex. +","Management of Uncertainty in the Multi-Level Monitoring and Diagnosis of + the Time of Flight Scintillation Array" +" The EM-algorithm is a general procedure to get maximum likelihood estimates +if part of the observations on the variables of a network are missing. In this +paper a stochastic version of the algorithm is adapted to probabilistic neural +networks describing the associative dependency of variables. These networks +have a probability distribution, which is a special case of the distribution +generated by probabilistic inference networks. Hence both types of networks can +be combined allowing to integrate probabilistic rules as well as unspecified +associations in a sound way. The resulting network may have a number of +interesting features including cycles of probabilistic rules, hidden +'unobservable' variables, and uncertain and contradictory evidence. +","Integrating Probabilistic Rules into Neural Networks: A Stochastic EM + Learning Algorithm" +" This paper presents a simple framework for Horn clause abduction, with +probabilities associated with hypotheses. It is shown how this representation +can represent any probabilistic knowledge representable in a Bayesian belief +network. The main contributions are in finding a relationship between logical +and probabilistic notions of evidential reasoning. This can be used as a basis +for a new way to implement Bayesian Networks that allows for approximations to +the value of the posterior probabilities, and also points to a way that +Bayesian networks can be extended beyond a propositional language. +",Representing Bayesian Networks within Probabilistic Horn Abduction +" A new probabilistic network construction system, DYNASTY, is proposed for +diagnostic reasoning given variables whose probabilities change over time. +Diagnostic reasoning is formulated as a sequential stochastic process, and is +modeled using influence diagrams. Given a set O of observations, DYNASTY +creates an influence diagram in order to devise the best action given O. +Sensitivity analyses are conducted to determine if the best network has been +created, given the uncertainty in network parameters and topology. DYNASTY uses +an equivalence class approach to provide decision thresholds for the +sensitivity analysis. This equivalence-class approach to diagnostic reasoning +differentiates diagnoses only if the required actions are different. A set of +network-topology updating algorithms are proposed for dynamically updating the +network when necessary. +",Dynamic Network Updating Techniques For Diagnostic Reasoning +" For high level path planning, environments are usually modeled as distance +graphs, and path planning problems are reduced to computing the shortest path +in distance graphs. One major drawback of this modeling is the inability to +model uncertainties, which are often encountered in practice. In this paper, a +new tool, called U-yraph, is proposed for environment modeling. A U-graph is an +extension of distance graphs with the ability to handle a kind of uncertainty. +By modeling an uncertain environment as a U-graph, and a navigation problem as +a Markovian decision process, we can precisely define a new optimality +criterion for navigation plans, and more importantly, we can come up with a +general algorithm for computing optimal plans for navigation tasks. +",High Level Path Planning with Uncertainty +" Given a universe of discourse X-a domain of possible outcomes-an experiment +may consist of selecting one of its elements, subject to the operation of +chance, or of observing the elements, subject to imprecision. A priori +uncertainty about the actual result of the experiment may be quantified, +representing either the likelihood of the choice of :r_X or the degree to which +any such X would be suitable as a description of the outcome. The former case +corresponds to a probability distribution, while the latter gives a possibility +assignment on X. The study of such assignments and their properties falls +within the purview of possibility theory [DP88, Y80, Z783. It, like probability +theory, assigns values between 0 and 1 to express likelihoods of outcomes. +Here, however, the similarity ends. Possibility theory uses the maximum and +minimum functions to combine uncertainties, whereas probability theory uses the +plus and times operations. This leads to very dissimilar theories in terms of +analytical framework, even though they share several semantic concepts. One of +the shared concepts consists of expressing quantitatively the uncertainty +associated with a given distribution. In probability theory its value +corresponds to the gain of information that would result from conducting an +experiment and ascertaining an actual result. This gain of information can +equally well be viewed as a decrease in uncertainty about the outcome of an +experiment. In this case the standard measure of information, and thus +uncertainty, is Shannon entropy [AD75, G77]. It enjoys several advantages-it is +characterized uniquely by a few, very natural properties, and it can be +conveniently used in decision processes. This application is based on the +principle of maximum entropy; it has become a popular method of relating +decisions to uncertainty. This paper demonstrates that an equally integrated +theory can be built on the foundation of possibility theory. We first show how +to define measures of in formation and uncertainty for possibility assignments. +Next we construct an information-based metric on the space of all possibility +distributions defined on a given domain. It allows us to capture the notion of +proximity in information content among the distributions. Lastly, we show that +all the above constructions can be carried out for continuous +distributions-possibility assignments on arbitrary measurable domains. We +consider this step very significant-finite domains of discourse are but +approximations of the real-life infinite domains. If possibility theory is to +represent real world situations, it must handle continuous distributions both +directly and through finite approximations. In the last section we discuss a +principle of maximum uncertainty for possibility distributions. We show how +such a principle could be formalized as an inference rule. We also suggest it +could be derived as a consequence of simple assumptions about combining +information. We would like to mention that possibility assignments can be +viewed as fuzzy sets and that every fuzzy set gives rise to an assignment of +possibilities. This correspondence has far reaching consequences in logic and +in control theory. Our treatment here is independent of any special +interpretation; in particular we speak of possibility distributions and +possibility measures, defining them as measurable mappings into the interval +[0, 1]. Our presentation is intended as a self-contained, albeit terse summary. +Topics discussed were selected with care, to demonstrate both the completeness +and a certain elegance of the theory. Proofs are not included; we only offer +illustrative examples. +",Formal Model of Uncertainty for Possibilistic Rules +" Deliberation plays an important role in the design of rational agents +embedded in the real-world. In particular, deliberation leads to the formation +of intentions, i.e., plans of action that the agent is committed to achieving. +In this paper, we present a branching time possible-worlds model for +representing and reasoning about, beliefs, goals, intentions, time, actions, +probabilities, and payoffs. We compare this possible-worlds approach with the +more traditional decision tree representation and provide a transformation from +decision trees to possible worlds. Finally, we illustrate how an agent can +perform deliberation using a decision-tree representation and then use a +possible-worlds model to form and reason about his intentions. +",Deliberation and its Role in the Formation of Intentions +" During interactions with human consultants, people are used to providing +partial and/or inaccurate information, and still be understood and assisted. We +attempt to emulate this capability of human consultants; in computer +consultation systems. In this paper, we present a mechanism for handling +uncertainty in plan recognition during task-oriented consultations. The +uncertainty arises while choosing an appropriate interpretation of a user?s +statements among many possible interpretations. Our mechanism handles this +uncertainty by using probability theory to assess the probabilities of the +interpretations, and complements this assessment by taking into account the +information content of the interpretations. The information content of an +interpretation is a measure of how well defined an interpretation is in terms +of the actions to be performed on the basis of the interpretation. This measure +is used to guide the inference process towards interpretations with a higher +information content. The information content for an interpretation depends on +the specificity and the strength of the inferences in it, where the strength of +an inference depends on the reliability of the information on which the +inference is based. Our mechanism has been developed for use in task-oriented +consultation systems. The domain that we have chosen for exploration is that of +a travel agency. +","Handling Uncertainty during Plan Recognition in Task-Oriented + Consultation Systems" +" This paper introduces conceptual relations that synthesize utilitarian and +logical concepts, extending the logics of preference of Rescher. We define +first, in the context of a possible worlds model, constraint-dependent measures +that quantify the relative quality of alternative solutions of reasoning +problems or the relative desirability of various policies in control, decision, +and planning problems. We show that these measures may be interpreted as truth +values in a multi valued logic and propose mechanisms for the representation of +complex constraints as combinations of simpler restrictions. These extended +logical operations permit also the combination and aggregation of goal-specific +quality measures into global measures of utility. We identify also relations +that represent differential preferences between alternative solutions and +relate them to the previously defined desirability measures. Extending +conventional modal logic formulations, we introduce structures for the +representation of ignorance about the utility of alternative solutions. +Finally, we examine relations between these concepts and similarity based +semantic models of fuzzy logic. +",Truth as Utility: A Conceptual Synthesis +" We present PULCinella and its use in comparing uncertainty theories. +PULCinella is a general tool for Propagating Uncertainty based on the Local +Computation technique of Shafer and Shenoy. It may be specialized to different +uncertainty theories: at the moment, Pulcinella can propagate probabilities, +belief functions, Boolean values, and possibilities. Moreover, Pulcinella +allows the user to easily define his own specializations. To illustrate +Pulcinella, we analyze two examples by using each of the four theories above. +In the first one, we mainly focus on intrinsic differences between theories. In +the second one, we take a knowledge engineer viewpoint, and check the adequacy +of each theory to a given problem. +","Pulcinella: A General Tool for Propagating Uncertainty in Valuation + Networks" +" In this article we present two ways of structuring bodies of evidence, which +allow us to reduce the complexity of the operations usually performed in the +framework of evidence theory. The first structure just partitions the focal +elements in a body of evidence by their cardinality. With this structure we are +able to reduce the complexity on the calculation of the belief functions Bel, +Pl, and Q. The other structure proposed here, the Hierarchical Trees, permits +us to reduce the complexity of the calculation of Bel, Pl, and Q, as well as of +the Dempster's rule of combination in relation to the brute-force algorithm. +Both these structures do not require the generation of all the subsets of the +reference domain. +",Structuring Bodies of Evidence +" In general, the best explanation for a given observation makes no promises on +how good it is with respect to other alternative explanations. A major +deficiency of message-passing schemes for belief revision in Bayesian networks +is their inability to generate alternatives beyond the second best. In this +paper, we present a general approach based on linear constraint systems that +naturally generates alternative explanations in an orderly and highly efficient +manner. This approach is then applied to cost-based abduction problems as well +as belief revision in Bayesian net works. +","On the Generation of Alternative Explanations with Implications for + Belief Revision" +" A control strategy for expert systems is presented which is based on Shafer's +Belief theory and the combination rule of Dempster. In contrast to well known +strategies it is not sequentially and hypotheses-driven, but parallel and self +organizing, determined by the concept of information gain. The information +gain, calculated as the maximal difference between the actual evidence +distribution in the knowledge base and the potential evidence determines each +consultation step. Hierarchically structured knowledge is an important +representation form and experts even use several hierarchies in parallel for +constituting their knowledge. Hence the control strategy is applied to a +layered set of distinct hierarchies. Depending on the actual data one of these +hierarchies is chosen by the control strategy for the next step in the +reasoning process. Provided the actual data are well matched to the structure +of one hierarchy, this hierarchy remains selected for a longer consultation +time. If no good match can be achieved, a switch from the actual hierarchy to a +competing one will result, very similar to the phenomenon of restructuring in +problem solving tasks. Up to now the control strategy is restricted to multi +hierarchical knowledge bases with disjunct hierarchies. It is implemented in +the expert system IBIG (inference by information gain), being presently applied +to acquired speech disorders (aphasia). +",Completing Knowledge by Competing Hierarchies +" The graphoid axioms for conditional independence, originally described by +Dawid [1979], are fundamental to probabilistic reasoning [Pearl, 19881. Such +axioms provide a mechanism for manipulating conditional independence assertions +without resorting to their numerical definition. This paper explores a +representation for independence statements using multiple undirected graphs and +some simple graphical transformations. The independence statements derivable in +this system are equivalent to those obtainable by the graphoid axioms. +Therefore, this is a purely graphical proof technique for conditional +independence. +",A Graph-Based Inference Method for Conditional Independence +" This paper proposes a new method for solving Bayesian decision problems. The +method consists of representing a Bayesian decision problem as a +valuation-based system and applying a fusion algorithm for solving it. The +fusion algorithm is a hybrid of local computational methods for computation of +marginals of joint probability distributions and the local computational +methods for discrete optimization problems. +",A Fusion Algorithm for Solving Bayesian Decision Problems +" Irrelevance-based partial MAPs are useful constructs for domain-independent +explanation using belief networks. We look at two definitions for such partial +MAPs, and prove important properties that are useful in designing algorithms +for computing them effectively. We make use of these properties in modifying +our standard MAP best-first algorithm, so as to handle irrelevance-based +partial MAPs. +",Algorithms for Irrelevance-Based Partial MAPs +" Survey of several forms of updating, with a practical illustrative example. +We study several updating (conditioning) schemes that emerge naturally from a +common scenarion to provide some insights into their meaning. Updating is a +subtle operation and there is no single method, no single 'good' rule. The +choice of the appropriate rule must always be given due consideration. Planchet +(1989) presents a mathematical survey of many rules. We focus on the practical +meaning of these rules. After summarizing the several rules for conditioning, +we present an illustrative example in which the various forms of conditioning +can be explained. +",About Updating +" In probabilistic logic entailments, even moderate size problems can yield +linear constraint systems with so many variables that exact methods are +impractical. This difficulty can be remedied in many cases of interest by +introducing a three valued logic (true, false, and ""don't care""). The +three-valued approach allows the construction of ""compressed"" constraint +systems which have the same solution sets as their two-valued counterparts, but +which may involve dramatically fewer variables. Techniques to calculate point +estimates for the posterior probabilities of entailed sentences are discussed. +",Compressed Constraints in Probabilistic Logic and Their Revision +" The presence of latent variables can greatly complicate inferences about +causal relations between measured variables from statistical data. In many +cases, the presence of latent variables makes it impossible to determine for +two measured variables A and B, whether A causes B, B causes A, or there is +some common cause. In this paper I present several theorems that state +conditions under which it is possible to reliably infer the causal relation +between two measured variables, regardless of whether latent variables are +acting or not. +",Detecting Causal Relations in the Presence of Unmeasured Variables +" In mechanical design, there is often unavoidable uncertainty in estimates of +design performance. Evaluation of design alternatives requires consideration of +the impact of this uncertainty. Expert heuristics embody assumptions regarding +the designer's attitude towards risk and uncertainty that might be reasonable +in most cases but inaccurate in others. We present a technique to allow +designers to incorporate their own unique attitude towards uncertainty as +opposed to those assumed by the domain expert's rules. The general approach is +to eliminate aspects of heuristic rules which directly or indirectly include +assumptions regarding the user's attitude towards risk, and replace them with +explicit, user-specified probabilistic multi attribute utility and probability +distribution functions. We illustrate the method in a system for material +selection for automobile bumpers. +","A Method for Integrating Utility Analysis into an Expert System for + Design Evaluation" +" The relationship between belief networks and relational databases is +examined. Based on this analysis, a method to construct belief networks +automatically from statistical relational data is proposed. A comparison +between our method and other methods shows that our method has several +advantages when generalization or prediction is deeded. +",From Relational Databases to Belief Networks +" A very computationally-efficient Monte-Carlo algorithm for the calculation of +Dempster-Shafer belief is described. If Bel is the combination using Dempster's +Rule of belief functions Bel, ..., Bel,7, then, for subset b of the frame C), +Bel(b) can be calculated in time linear in 1(31 and m (given that the weight of +conflict is bounded). The algorithm can also be used to improve the complexity +of the Shenoy-Shafer algorithms on Markov trees, and be generalised to +calculate Dempster-Shafer Belief over other logics. +",A Monte-Carlo Algorithm for Dempster-Shafer Belief +" The compatibility of quantitative and qualitative representations of beliefs +was studied extensively in probability theory. It is only recently that this +important topic is considered in the context of belief functions. In this +paper, the compatibility of various quantitative belief measures and +qualitative belief structures is investigated. Four classes of belief measures +considered are: the probability function, the monotonic belief function, +Shafer's belief function, and Smets' generalized belief function. The analysis +of their individual compatibility with different belief structures not only +provides a sound b