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Artificial Intelligence

What is Artificial Intelligence (AI)? Tutorial, Meaning - Javatpoint

Artificial Intelligence (AI) Tutorial Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is Artificial Intelligence (AI)? Why Artificial Intelligence? Goals of Artificial Intelligence History of AI What Comprises to Artificial Intelligence? Types of Artificial Intelligence Advantages of Artificial Intelligence Disadvantages of Artificial Intelligence Challenges of AI AI Tools and Services Prerequisite Audience Problems Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews AI Type 1: Based on Capabilities AI Type 2: Based on Functionality Contact info Follow us Tutorials Interview Questions Online Compiler The Artificial Intelligence tutorial provides an introduction to AI which will help you to understand the concepts behind Artificial Intelligence. In this tutorial, we have also discussed various popular topics such as History of AI, applications of AI, deep learning, machine learning, natural language processing, Reinforcement learning, Q-learning, Intelligent agents, Various search algorithms, etc. Our AI tutorial is prepared from an elementary level so you can easily understand the complete tutorial from basic concepts to the high-level concepts. In today's world, technology is growing very fast, and we are getting in touch with different new technologies day by day. Here, one of the booming technologies of computer science is Artificial Intelligence which is ready to create a new revolution in the world by making intelligent machines.The Artificial Intelligence is now all around us. It is currently working with a variety of subfields, ranging from general to specific, such as self-driving cars, playing chess, proving theorems, playing music, Painting, etc. AI is one of the fascinating and universal fields of Computer science which has a great scope in future. AI holds a tendency to cause a machine to work as a human. Artificial Intelligence is composed of two words Artificial and Intelligence, where Artificial defines "man-made," and intelligence defines "thinking power", hence AI means "a man-made thinking power." So, we can define AI as: Artificial Intelligence exists when a machine can have human based skills such as learning, reasoning, and solving problems With Artificial Intelligence you do not need to preprogram a machine to do some work, despite that you can create a machine with programmed algorithms which can work with own intelligence, and that is the awesomeness of AI. It is believed that AI is not a new technology, and some people says that as per Greek myth, there were Mechanical men in early days which can work and behave like humans. Before Learning about Artificial Intelligence, we should know that what is the importance of AI and why should we learn it. Following are some main reasons to learn about AI: Following are the main goals of Artificial Intelligence: Artificial Intelligence is not just a part of computer science even it's so vast and requires lots of other factors which can contribute to it. To create the AI first we should know that how intelligence is composed, so the Intelligence is an intangible part of our brain which is a combination of Reasoning, learning, problem-solving perception, language understanding, etc. To achieve the above factors for a machine or software Artificial Intelligence requires the following discipline: Artificial Intelligence can be categorized in several ways, primarily based on two main criteria: capabilities and functionality. Following are some main advantages of Artificial Intelligence: Every technology has some disadvantages, and thesame goes for Artificial intelligence. Being so advantageous technology still, it has some disadvantages which we need to keep in our mind while creating an AI system. Following are the disadvantages of AI: Artificial Intelligence offers incredible advantages, but it also presents some challenges that need to be addressed: AI tools and services are advancing quickly, and this progress can be linked back to a significant moment in 2012 when the AlexNet neural network came onto the scene. This marked the start of a new era for high-performance AI, thanks to the use of GPUs and massive data sets. The big shift was the ability to train neural networks using huge amounts of data on multiple GPU cores simultaneously, making it a more scalable process. Before learning about Artificial Intelligence, you must have the fundamental knowledge of following so that you can understand the concepts easily: Our AI tutorial is designed specifically for beginners and also included some high-level concepts for professionals. We assure you that you will not find any difficulty while learning our AI tutorial. But if there any mistake, kindly post the problem in the contact form. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence

Application of AI - Javatpoint

Applications of AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. AI in Astronomy 2. AI in Healthcare 3. AI in Gaming 4. AI in Finance 5. AI in Data Security 6. AI in Social Media 7. AI in Travel & Transport 8. AI in Automotive Industry 9. AI in Robotics: 10. AI in Entertainment 11. AI in Agriculture 12. AI in E-commerce 13. AI in education: Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence has various applications in today's society. It is becoming essential for today's time because it can solve complex problems with an efficient way in multiple industries, such as Healthcare, entertainment, finance, education, etc. AI is making our daily life more comfortable and fast. Following are some sectors which have the application of Artificial Intelligence: The applications of AI are vast and diverse, touching nearly every aspect of our lives. From healthcare to finance, astronomy to gaming, and transportation to entertainment, AI is reshaping industries and propelling us into a future where the possibilities seem limitless. As AI continues to advance, its impact on society is poised to grow, promising increased efficiency, better decision-making, and innovative solutions to some of our most pressing challenges. Embracing and responsibly harnessing the power of AI will be key to unlocking its full potential and ensuring a brighter future for all. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence

History of Artificial Intelligence - Javatpoint

History of Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Maturation of Artificial Intelligence (1943-1952) The birth of Artificial Intelligence (1952-1956) The golden years-Early enthusiasm (1956-1974) The first AI winter (1974-1980) A boom of AI (1980-1987) The second AI winter (1987-1993) The emergence of intelligent agents (1993-2011) Deep learning, big data and artificial general intelligence (2011-present) Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence is not a new word and not a new technology for researchers. This technology is much older than you would imagine. Even there are the myths of Mechanical men in Ancient Greek and Egyptian Myths. Following are some milestones in the history of AI which defines the journey from the AI generation to till date development. Between 1943 and 1952, there was notable progress in the expansion of artificial intelligence (AI). Throughout this period, AI transitioned from a mere concept to tangible experiments and practical applications. Here are some key events that happened during this period: From 1952 to 1956, AI surfaced as a unique domain of investigation. During this period, pioneers and forward-thinkers commenced the groundwork for what would ultimately transform into a revolutionary technological domain. Here are notable occurrences from this era: At that time high-level computer languages such as FORTRAN, LISP, or COBOL were invented. And the enthusiasm for AI was very high at that time. The period from 1956 to 1974 is commonly known as the "Golden Age" of artificial intelligence (AI). In this timeframe, AI researchers and innovators were filled with enthusiasm and achieved remarkable advancements in the field. Here are some notable events from this era: The initial AI winter, occurring from 1974 to 1980, is known as a tough period for artificial intelligence (AI). During this time, there was a substantial decrease in research funding, and AI faced a sense of letdown. Between 1980 and 1987, AI underwent a renaissance and newfound vitality after the challenging era of the First AI Winter. Here are notable occurrences from this timeframe: Between 1993 and 2011, there were significant leaps forward in artificial intelligence (AI), particularly in the development of intelligent computer programs. During this era, AI professionals shifted their emphasis from attempting to match human intelligence to crafting pragmatic, ingenious software tailored to specific tasks. Here are some noteworthy occurrences from this timeframe: From 2011 to the present moment, significant advancements have unfolded within the artificial intelligence (AI) domain. These achievements can be attributed to the amalgamation of deep learning, extensive data application, and the ongoing quest for artificial general intelligence (AGI). Here are notable occurrences from this timeframe: Now AI has developed to a remarkable level. The concept of Deep learning, big data, and data science are now trending like a boom. Nowadays companies like Google, Facebook, IBM, and Amazon are working with AI and creating amazing devices. The future of Artificial Intelligence is inspiring and will come with high intelligence. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence

Types of Artificial Intelligence - Javatpoint

Types of Artificial Intelligence: Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials AI type-1: Based on Capabilities Artificial Intelligence type-2: Based on functionality Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Weak AI or Narrow AI: 2. General AI: 3. Super AI: 1. Reactive Machines 2. Limited Memory 3. Theory of Mind 4. Self-Awareness Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence can be divided in various types, there are mainly two types of main categorization which are based on capabilities and based on functionally of AI. Following is flow diagram which explain the types of AI. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Types of AI Agents - Javatpoint

Types of AI Agents Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials 1. Simple Reflex agent: 2. Model-based reflex agent 3. Goal-based agents 4. Utility-based agents 5. Learning Agents Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Agents can be grouped into five classes based on their degree of perceived intelligence and capability. All these agents can improve their performance and generate better action over the time. These are given below: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Intelligent Agent | Agents in AI - Javatpoint

Agents in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is an Agent? Intelligent Agents: Rational Agent: Structure of an AI Agent PEAS Representation Example of Agents with their PEAS representation Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Rationality: PEAS for self-driving cars: Note: Rational agents in AI are very similar to intelligent agents. Note: Rationality differs from Omniscience because an Omniscient agent knows the actual outcome of its action and act accordingly, which is not possible in reality. Contact info Follow us Tutorials Interview Questions Online Compiler An AI system can be defined as the study of the rational agent and its environment. The agents sense the environment through sensors and act on their environment through actuators. An AI agent can have mental properties such as knowledge, belief, intention, etc. An agent can be anything that perceiveits environment through sensors and act upon that environment through actuators. An Agent runs in the cycle of perceiving, thinking, and acting. An agent can be: Hence the world around us is full of agents such as thermostat, cellphone, camera, and even we are also agents. Before moving forward, we should first know about sensors, effectors, and actuators. Sensor: Sensor is a device which detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors. Actuators: Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system. An actuator can be an electric motor, gears, rails, etc. Effectors: Effectors are the devices which affect the environment. Effectors can be legs, wheels, arms, fingers, wings, fins, and display screen. An intelligent agent is an autonomous entity which act upon an environment using sensors and actuators for achieving goals. An intelligent agent may learn from the environment to achieve their goals. A thermostat is an example of an intelligent agent. Following are the main four rules for an AI agent: A rational agent is an agent which has clear preference, models uncertainty, and acts in a way to maximize its performance measure with all possible actions. A rational agent is said to perform the right things. AI is about creating rational agents to use for game theory and decision theory for various real-world scenarios. For an AI agent, the rational action is most important because in AI reinforcement learning algorithm, for each best possible action, agent gets the positive reward and for each wrong action, an agent gets a negative reward. The rationality of an agent is measured by its performance measure. Rationality can be judged on the basis of following points: The task of AI is to design an agent program which implements the agent function. The structure of an intelligent agent is a combination of architecture and agent program. It can be viewed as: Following are the main three terms involved in the structure of an AI agent: Architecture: Architecture is machinery that an AI agent executes on. Agent Function: Agent function is used to map a percept to an action. Agent program: Agent program is an implementation of agent function. An agent program executes on the physical architecture to produce function f. PEAS is a type of model on which an AI agent works upon. When we define an AI agent or rational agent, then we can group its properties under PEAS representation model. It is made up of four words: Here performance measure is the objective for the success of an agent's behavior. Let's suppose a self-driving car then PEAS representation will be: Performance: Safety, time, legal drive, comfort Environment: Roads, other vehicles, road signs, pedestrian Actuators: Steering, accelerator, brake, signal, horn Sensors: Camera, GPS, speedometer, odometer, accelerometer, sonar. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Agent Environment in AI - Javatpoint

Agent Environment in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Features of Environment 1. Fully observable vs Partially Observable: 2. Deterministic vs Stochastic: 3. Episodic vs Sequential: 4. Single-agent vs Multi-agent 5. Static vs Dynamic: 6. Discrete vs Continuous: 7. Known vs Unknown 8. Accessible vs Inaccessible Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler An environment is everything in the world which surrounds the agent, but it is not a part of an agent itself. An environment can be described as a situation in which an agent is present. The environment is where agent lives, operate and provide the agent with something to sense and act upon it. An environment is mostly said to be non-feministic. As per Russell and Norvig, an environment can have various features from the point of view of an agent: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Intelligent Agent

Turing Test in AI - Javatpoint

Turing Test in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials History of Turing Test Variations of the Turing Test Chatbots to attempt the Turing test: The Chinese Room Argument: Features required for a machine to pass the Turing test: Limitation of Turing Test Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler In 1950, Alan Turing introduced a test to check whether a machine can think like a human or not, this test is known as the Turing Test. In this test, Turing proposed that the computer can be said to be an intelligent if it can mimic human response under specific conditions. Turing Test was introduced by Turing in his 1950 paper, "Computing Machinery and Intelligence," which considered the question, "Can Machine think?" The Turing test is based on a party game "Imitation game," with some modifications. This game involves three players in which one player is Computer, another player is human responder, and the third player is a human Interrogator, who is isolated from other two players and his job is to find that which player is machine among two of them. Consider, Player A is a computer, Player B is human, and Player C is an interrogator. Interrogator is aware that one of them is machine, but he needs to identify this on the basis of questions and their responses. The conversation between all players is via keyboard and screen so the result would not depend on the machine's ability to convert words as speech. The test result does not depend on each correct answer, but only how closely its responses like a human answer. The computer is permitted to do everything possible to force a wrong identification by the interrogator. The questions and answers can be like: Interrogator: Are you a computer? PlayerA (Computer): No Interrogator: Multiply two large numbers such as (256896489*456725896) Player A: Long pause and give the wrong answer. In this game, if an interrogator would not be able to identify which is a machine and which is human, then the computer passes the test successfully, and the machine is said to be intelligent and can think like a human. "In 1991, the New York businessman Hugh Loebner announces the prize competition, offering a $100,000 prize for the first computer to pass the Turing test. However, no AI program to till date, come close to passing an undiluted Turing test". The Turing Test, introduced by Alan Turing in 1950, is a crucial milestone in the history of artificial intelligence (AI). It came to light in his paper titled 'Computing Machinery and Intelligence.' Turing aimed to address a profound question: Can machines mimic human-like intelligence? This curiosity arose from Turing's fascination with the concept of creating thinking machines that exhibit intelligent behavior. He proposed the Turing Test as a practical method to determine if a machine can engage in natural language conversations convincingly, making a human evaluator believe it's human. Turing's work on this test laid the foundation for AI research and spurred discussions about machine intelligence. It provided a framework for evaluating AI systems. Over time, the Turing Test has evolved and remains a topic of debate and improvement. Its historical importance in shaping AI is undeniable, continuously motivating AI researchers and serving as a benchmark for gauging AI advancements. Over the years, different versions of the Turing Test have appeared to overcome its constraints and deliver a more thorough assessment of AI capabilities: ELIZA: ELIZA was a Natural language processing computer program created by Joseph Weizenbaum. It was created to demonstrate the ability of communication between machine and humans. It was one of the first chatterbots, which has attempted the Turing Test. Parry: Parry was a chatterbot created by Kenneth Colby in 1972. Parry was designed to simulate a person with Paranoid schizophrenia(most common chronic mental disorder). Parry was described as "ELIZA with attitude." Parry was tested using a variation of the Turing Test in the early 1970s. Eugene Goostman: Eugene Goostman was a chatbot developed in Saint Petersburg in 2001. This bot has competed in the various number of Turing Test. In June 2012, at an event, Goostman won the competition promoted as largest-ever Turing test content, in which it has convinced 29% of judges that it was a human.Goostman resembled as a 13-year old virtual boy. There were many philosophers who really disagreed with the complete concept of Artificial Intelligence. The most famous argument in this list was "Chinese Room." In the year 1980, John Searle presented "Chinese Room" thought experiment, in his paper "Mind, Brains, and Program," which was against the validity of Turing's Test. According to his argument, "Programming a computer may make it to understand a language, but it will not produce a real understanding of language or consciousness in a computer." He argued that Machine such as ELIZA and Parry could easily pass the Turing test by manipulating keywords and symbol, but they had no real understanding of language. So it cannot be described as "thinking" capability of a machine such as a human. The Turing Test still serves as a pivotal benchmark for assessing AI's conversational skills in today's context. It continues to be instrumental in the development and evaluation of chatbots and virtual assistants. Many companies and developers employ different versions of the test to gauge how well their AI systems can engage in conversation. However, it's worth noting that while the Turing Test maintains its relevance, the AI field has progressed significantly beyond its scope. Modern AI systems leverage advanced natural language processing, machine learning, and deep learning techniques, empowering them to execute tasks much more intricate than imitating human dialogue. AI's applications now span a wide array of fields, from healthcare and finance to autonomous vehicles and image recognition, showcasing its diverse capabilities that extend well beyond mere conversation. 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Problem-solving

Search Algorithms in AI - Javatpoint

Search Algorithms in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction Search Algorithm Terminologies: Properties of Search Algorithms: Importance of Search Algorithms in Artificial Intelligence Types of search algorithms Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Problem-solving agents: Uninformed/Blind Search: Informed Search Contact info Follow us Tutorials Interview Questions Online Compiler Search algorithms in AI are the algorithms that are created to aid the searchers in getting the right solution. The search issue contains search space, first start and end point. Now by performing simulation of scenarios and alternatives, searching algorithms help AI agents find the optimal state for the task. Logic used in algorithms processes the initial state and tries to get the expected state as the solution. Because of this, AI machines and applications just functioning using search engines and solutions that come from these algorithms can only be as effective as the algorithms. AI agents can make the AI interfaces usable without any software literacy. The agents that carry out such activities do so with the aim of reaching an end goal and develop action plans that in the end will bring the mission to an end. Completion of the action is gained after the steps of these different actions. The AI-agents finds the best way through the process by evaluating all the alternatives which are present. Search systems are a common task in artificial intelligence by which you are going to find the optimum solution for the AI agents. In Artificial Intelligence, Search techniques are universal problem-solving methods. Rational agents or Problem-solving agents in AI mostly used these search strategies or algorithms to solve a specific problem and provide the best result. Problem-solving agents are the goal-based agents and use atomic representation. In this topic, we will learn various problem-solving search algorithms. Following are the four essential properties of search algorithms to compare the efficiency of these algorithms: Completeness: A search algorithm is said to be complete if it guarantees to return a solution if at least any solution exists for any random input. Optimality: If a solution found for an algorithm is guaranteed to be the best solution (lowest path cost) among all other solutions, then such a solution for is said to be an optimal solution. Time Complexity: Time complexity is a measure of time for an algorithm to complete its task. Space Complexity: It is the maximum storage space required at any point during the search, as the complexity of the problem. Here, are some important factors of role of search algorithms used AI are as follow. 1. Solving problems: "Workflow" logical search methods like describing the issue, getting the necessary steps together, and specifying an area to search help AI search algorithms getting better in solving problems. Take for instance the development of AI search algorithms which support applications like Google Maps by finding the fastest way or shortest route between given destinations. These programs basically conduct the search through various options to find the best solution possible. 2. Search programming: Many AI functions can be designed as search oscillations, which thus specify what to look for in formulating the solution of the given problem. 3. Goal-based agents: Instead, the goal-directed and high-performance systems use a wide range of search algorithms to improve the efficiency of AI. Though they are not robots, these agents look for the ideal route for action dispersion so as to avoid the most impacting steps that can be used to solve a problem. It is their main aims to come up with an optimal solution which takes into account all possible factors. 4. Support production systems: AI Algorithms in search engines for systems manufacturing help them run faster. These programmable systems assist AI applications with applying rules and methods, thus making an effective implementation possible. Production systems involve learning of artificial intelligence systems and their search for canned rules that lead to the wanted action. 5. Neural network systems: Beyond this, employing neural network algorithms is also of importance of the neural network systems. The systems are composed of these structures: a hidden layer, and an input layer, an output layer, and nodes that are interconnected. One of the most important functions offered by neural networks is to address the challenges of AI within any given scenarios. AI is somehow able to navigate the search space to find the connection weights that will be required in the mapping of inputs to outputs. This is made better by search algorithms in AI. Based on the search problems we can classify the search algorithms into uninformed (Blind search) search and informed search (Heuristic search) algorithms. The uninformed search does not contain any domain knowledge such as closeness, the location of the goal. It operates in a brute-force way as it only includes information about how to traverse the tree and how to identify leaf and goal nodes. Uninformed search applies a way in which search tree is searched without any information about the search space like initial state operators and test for the goal, so it is also called blind search.It examines each node of the tree until it achieves the goal node. It can be divided into six main types: Informed search algorithms use domain knowledge. In an informed search, problem information is available which can guide the search. Informed search strategies can find a solution more efficiently than an uninformed search strategy. Informed search is also called a Heuristic search. A heuristic is a way which might not always be guaranteed for best solutions but guaranteed to find a good solution in reasonable time. Informed search can solve much complex problem which could not be solved in another way. An example of informed search algorithms is a traveling salesman problem. 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Problem-solving

Uninformed Search Algorithms - Javatpoint

Uninformed Search Algorithms Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction: 1. Breadth-first Search: 2. Depth-first Search 3. Depth-Limited Search Algorithm: 4. Uniform-cost Search Algorithm: 5. Iterative deepeningdepth-first Search: 6. Bidirectional Search Algorithm: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Example: Example: Example: Example: Example: Note: Backtracking is an algorithm technique for finding all possible solutions using recursion. Contact info Follow us Tutorials Interview Questions Online Compiler Uninformed search is one in which the search systems do not use any clues about the suitable area but it depend on the random nature of search. Nevertheless, they begins the exploration of search space (all possible solutions) synchronously,. The search operation begins from the initial state and providing all possible next steps arrangement until goal is reached. These are mostly the simplest search strategies, but they may not be suitable for complex paths which involve in irrelevant or even irrelevant components. These algorithms are necessary for solving basic tasks or providing simple processing before passing on the data to more advanced search algorithms that incorporate prioritized information. Following are the various types of uninformed search algorithms: Advantages: Disadvantages: In the below tree structure, we have shown the traversing of the tree using BFS algorithm from the root node S to goal node K. BFS search algorithm traverse in layers, so it will follow the path which is shown by the dotted arrow, and the traversed path will be: Time Complexity: Time Complexity of BFS algorithm can be obtained by the number of nodes traversed in BFS until the shallowest Node. Where the d= depth of shallowest solution and b is a node at every state. T (b) = 1+b2+b3+.......+ bd= O (bd) Space Complexity: Space complexity of BFS algorithm is given by the Memory size of frontier which is O(bd). Completeness: BFS is complete, which means if the shallowest goal node is at some finite depth, then BFS will find a solution. Optimality: BFS is optimal if path cost is a non-decreasing function of the depth of the node. Advantage: Disadvantage: In the below search tree, we have shown the flow of depth-first search, and it will follow the order as: Root node--->Left node ----> right node. It will start searching from root node S, and traverse A, then B, then D and E, after traversing E, it will backtrack the tree as E has no other successor and still goal node is not found. After backtracking it will traverse node C and then G, and here it will terminate as it found goal node. Completeness: DFS search algorithm is complete within finite state space as it will expand every node within a limited search tree. Time Complexity: Time complexity of DFS will be equivalent to the node traversed by the algorithm. It is given by: T(n)= 1+ n2+ n3 +.........+ nm=O(nm) Where, m= maximum depth of any node and this can be much larger than d (Shallowest solution depth) Space Complexity: DFS algorithm needs to store only single path from the root node, hence space complexity of DFS is equivalent to the size of the fringe set, which is O(bm). Optimal: DFS search algorithm is non-optimal, as it may generate a large number of steps or high cost to reach to the goal node. A depth-limited search algorithm is similar to depth-first search with a predetermined limit. Depth-limited search can solve the drawback of the infinite path in the Depth-first search. In this algorithm, the node at the depth limit will treat as it has no successor nodes further. Depth-limited search can be terminated with two Conditions of failure: Advantages: Disadvantages: Completeness: DLS search algorithm is complete if the solution is above the depth-limit. Time Complexity: Time complexity of DLS algorithm is O(bℓ) where b is the branching factor of the search tree, and l is the depth limit. Space Complexity: Space complexity of DLS algorithm is O(b×ℓ) where b is the branching factor of the search tree, and l is the depth limit. Optimal: Depth-limited search can be viewed as a special case of DFS, and it is also not optimal even if ℓ>d. Uniform-cost search is a searching algorithm used for traversing a weighted tree or graph. This algorithm comes into play when a different cost is available for each edge. The primary goal of the uniform-cost search is to find a path to the goal node which has the lowest cumulative cost. Uniform-cost search expands nodes according to their path costs form the root node. It can be used to solve any graph/tree where the optimal cost is in demand. A uniform-cost search algorithm is implemented by the priority queue. It gives maximum priority to the lowest cumulative cost. Uniform cost search is equivalent to BFS algorithm if the path cost of all edges is the same. Advantages: Disadvantages: Completeness: Uniform-cost search is complete, such as if there is a solution, UCS will find it. Time Complexity: Let C* is Cost of the optimal solution, and ε is each step to get closer to the goal node. Then the number of steps is = C*/ε+1. Here we have taken +1, as we start from state 0 and end to C*/ε. Hence, the worst-case time complexity of Uniform-cost search isO(b1 + [C*/ε])/. Space Complexity: The same logic is for space complexity so, the worst-case space complexity of Uniform-cost search is O(b1 + [C*/ε]). Optimal: Uniform-cost search is always optimal as it only selects a path with the lowest path cost. The iterative deepening algorithm is a combination of DFS and BFS algorithms. This search algorithm finds out the best depth limit and does it by gradually increasing the limit until a goal is found. This algorithm performs depth-first search up to a certain "depth limit", and it keeps increasing the depth limit after each iteration until the goal node is found. This Search algorithm combines the benefits of Breadth-first search's fast search and depth-first search's memory efficiency. The iterative search algorithm is useful uninformed search when search space is large, and depth of goal node is unknown. Here are the steps for Iterative deepening depth first search algorithm: Advantages: Disadvantages: Following tree structure is showing the iterative deepening depth-first search. IDDFS algorithm performs various iterations until it does not find the goal node. The iteration performed by the algorithm is given as: 1'st Iteration-----> A2'nd Iteration----> A, B, C3'rd Iteration------>A, B, D, E, C, F, G4'th Iteration------>A, B, D, H, I, E, C, F, K, GIn the fourth iteration, the algorithm will find the goal node. Completeness: This algorithm is complete is ifthe branching factor is finite. Time Complexity: Let's suppose b is the branching factor and depth is d then the worst-case time complexity is O(bd). Space Complexity: The space complexity of IDDFS will be O(bd). Optimal: IDDFS algorithm is optimal if path cost is a non- decreasing function of the depth of the node. Bidirectional search algorithm runs two simultaneous searches, one form initial state called as forward-search and other from goal node called as backward-search, to find the goal node. Bidirectional search replaces one single search graph with two small subgraphs in which one starts the search from an initial vertex and other starts from goal vertex. The search stops when these two graphs intersect each other. Bidirectional search can use search techniques such as BFS, DFS, DLS, etc. Advantages: Disadvantages: In the below search tree, bidirectional search algorithm is applied. This algorithm divides one graph/tree into two sub-graphs. It starts traversing from node 1 in the forward direction and starts from goal node 16 in the backward direction. The algorithm terminates at node 9 where two searches meet. Completeness: Bidirectional Search is complete if we use BFS in both searches. Time Complexity: Time complexity of bidirectional search using BFS is O(bd). Space Complexity: Space complexity of bidirectional search is O(bd). Optimal: Bidirectional search is Optimal. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem-solving

Informed Search Algorithms in AI - Javatpoint

A* Search Algorithm in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials An Introduction to A* Search Algorithm in AI History of the A* Search Algorithm in Artificial Intelligence How does the A* search algorithm work in Artificial Intelligence? Advantages of A* Search Algorithm in Artificial Intelligence Disadvantages of A* Search Algorithm in Artificial Intelligence Applications of the A* Search Algorithm in Artificial Intelligence C program for A* Search Algorithm in Artificial Intelligence A* Search Algorithm Complexity in Artificial Intelligence Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews C++ program for A* Search Algorithm in Artificial Intelligence Java program for A* Search Algorithm in Artificial Intelligence Contact info Follow us Tutorials Interview Questions Online Compiler A* (pronounced "A-star") is a powerful graph traversal and pathfinding algorithm widely used in artificial intelligence and computer science. It is mainly used to find the shortest path between two nodes in a graph, given the estimated cost of getting from the current node to the destination node. The main advantage of the algorithm is its ability to provide an optimal path by exploring the graph in a more informed way compared to traditional search algorithms such as Dijkstra's algorithm. Algorithm A* combines the advantages of two other search algorithms: Dijkstra's algorithm and Greedy Best-First Search. Like Dijkstra's algorithm, A* ensures that the path found is as short as possible but does so more efficiently by directing its search through a heuristic similar to Greedy Best-First Search. A heuristic function, denoted h(n), estimates the cost of getting from any given node n to the destination node. The main idea of A* is to evaluate each node based on two parameters: Algorithm A* selects the nodes to be explored based on the lowest value of f(n), preferring the nodes with the lowest estimated total cost to reach the goal. The A* algorithm works: However, choosing a suitable and acceptable heuristic function is essential so that the algorithm performs correctly and provides an optimal solution. It was developed by Peter Hart, Nils Nilsson, and Bertram Raphael at the Stanford Research Institute (now SRI International) as an extension of Dijkstra's algorithm and other search algorithms of the time. A* was first published in 1968 and quickly gained recognition for its importance and effectiveness in the artificial intelligence and computer science communities. Here is a brief overview of the most critical milestones in the history of the search algorithm A*: The A* (pronounced "letter A") search algorithm is a popular and widely used graph traversal algorithm in artificial intelligence and computer science. It is used to find the shortest path from a start node to a destination node in a weighted graph. A* is an informed search algorithm that uses heuristics to guide the search efficiently. The search algorithm A* works as follows: The algorithm starts with a priority queue to store the nodes to be explored. It also instantiates two data structures g(n): The cost of the shortest path so far from the starting node to node n and h(n), the estimated cost (heuristic) from node n to the destination node. It is often a reasonable heuristic, meaning it never overestimates the actual cost of achieving a goal. Put the initial node in the priority queue and set its g(n) to 0. If the priority queue is not empty, Remove the node with the lowest f(n) from the priority queue. f(n) = g(n) h(n). If the deleted node is the destination node, the algorithm ends, and the path is found. Otherwise, expand the node and create its neighbors. For each neighbor node, calculate its initial g(n) value, which is the sum of the g value of the current node and the cost of moving from the current node to a neighboring node. If the neighbor node is not in priority order or the original g(n) value is less than its current g value, update its g value and set its parent node to the current node. Calculate the f(n) value from the neighbor node and add it to the priority queue. If the cycle ends without finding the destination node, the graph has no path from start to finish. The key to the efficiency of A* is its use of a heuristic function h(n) that provides an estimate of the remaining cost of reaching the goal of any node. By combining the actual cost g (n) with the heuristic cost h (n), the algorithm effectively explores promising paths, prioritizing nodes likely to lead to the shortest path. It is important to note that the efficiency of the A* algorithm is highly dependent on the choice of the heuristic function. Acceptable heuristics ensure that the algorithm always finds the shortest path, but more informed and accurate heuristics can lead to faster convergence and reduced search space. The A* search algorithm offers several advantages in artificial intelligence and problem-solving scenarios: Although the A* (letter A) search algorithm is a widely used and powerful technique for solving AI pathfinding and graph traversal problems, it has disadvantages and limitations. Here are some of the main disadvantages of the search algorithm: The search algorithm A* (letter A) is a widely used and robust pathfinding algorithm in artificial intelligence and computer science. Its efficiency and optimality make it suitable for various applications. Here are some typical applications of the A* search algorithm in artificial intelligence: These are just a few examples of how the A* search algorithm finds applications in various areas of artificial intelligence. Its flexibility, efficiency, and optimization make it a valuable tool for many problems. Explanation: Sample Output Explanation: Sample Output Explanation: Sample Output The A* (pronounced "A-star") search algorithm is a popular and widely used graph traversal and path search algorithm in artificial intelligence. Finding the shortest path between two nodes in a graph or grid-based environment is usually common. The algorithm combines Dijkstra's and greedy best-first search elements to explore the search space while ensuring optimality efficiently. Several factors determine the complexity of the A* search algorithm. Graph size (nodes and edges): A graph's number of nodes and edges greatly affects the algorithm's complexity. More nodes and edges mean more possible options to explore, which can increase the execution time of the algorithm. Heuristic function: A* uses a heuristic function (often denoted h(n)) to estimate the cost from the current node to the destination node. The precision of this heuristic greatly affects the efficiency of the A* search. A good heuristic can help guide the search to a goal more quickly, while a bad heuristic can lead to unnecessary searching. In practice, however, A* often performs significantly better due to the influence of a heuristic function that helps guide the algorithm to promising paths. In the case of a well-designed heuristic, the effective branching factor is much smaller, which leads to a faster approach to the optimal solution. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem-solving

Hill Climbing Algorithm in AI - Javatpoint

Hill Climbing Algorithm in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Features of Hill Climbing: State-space Diagram for Hill Climbing: Different regions in the state space landscape: Types of Hill Climbing Algorithm: Problems in Hill Climbing Algorithm: Applications of Hill Climbing Algorithm Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Advantages of Hill climb algorithm: Disadvantages of Hill Climbing Algorithm 1. Simple Hill Climbing: Algorithm for Simple Hill Climbing: 2. Steepest-Ascent hill climbing: Algorithm for Steepest-Ascent hill climbing: 3. Stochastic hill climbing: Simulated Annealing: Contact info Follow us Tutorials Interview Questions Online Compiler The merits of Hill Climbing algorithm are given below. Following are some main features of Hill Climbing Algorithm: The state-space landscape is a graphical representation of the hill-climbing algorithm which is showing a graph between various states of algorithm and Objective function/Cost. On Y-axis we have taken the function which can be an objective function or cost function, and state-space on the x-axis. If the function on Y-axis is cost then, the goal of search is to find the global minimum and local minimum. If the function of Y-axis is Objective function, then the goal of the search is to find the global maximum and local maximum. Local Maximum: Local maximum is a state which is better than its neighbor states, but there is also another state which is higher than it. Global Maximum: Global maximum is the best possible state of state space landscape. It has the highest value of objective function. Current state: It is a state in a landscape diagram where an agent is currently present. Flat local maximum: It is a flat space in the landscape where all the neighbor states of current states have the same value. Shoulder: It is a plateau region which has an uphill edge. Simple hill climbing is the simplest way to implement a hill climbing algorithm. It only evaluates the neighbor node state at a time and selects the first one which optimizes current cost and set it as a current state. It only checks it's one successor state, and if it finds better than the current state, then move else be in the same state. This algorithm has the following features: The steepest-Ascent algorithm is a variation of simple hill climbing algorithm. This algorithm examines all the neighboring nodes of the current state and selects one neighbor node which is closest to the goal state. This algorithm consumes more time as it searches for multiple neighbors Stochastic hill climbing does not examine for all its neighbor before moving. Rather, this search algorithm selects one neighbor node at random and decides whether to choose it as a current state or examine another state. 1. Local Maximum: A local maximum is a peak state in the landscape which is better than each of its neighboring states, but there is another state also present which is higher than the local maximum. Solution: Backtracking technique can be a solution of the local maximum in state space landscape. Create a list of the promising path so that the algorithm can backtrack the search space and explore other paths as well. 2. Plateau: A plateau is the flat area of the search space in which all the neighbor states of the current state contains the same value, because of this algorithm does not find any best direction to move. A hill-climbing search might be lost in the plateau area. Solution: The solution for the plateau is to take big steps or very little steps while searching, to solve the problem. Randomly select a state which is far away from the current state so it is possible that the algorithm could find non-plateau region. 3. Ridges: A ridge is a special form of the local maximum. It has an area which is higher than its surrounding areas, but itself has a slope, and cannot be reached in a single move. Solution: With the use of bidirectional search, or by moving in different directions, we can improve this problem. A hill-climbing algorithm which never makes a move towards a lower value guaranteed to be incomplete because it can get stuck on a local maximum. And if algorithm applies a random walk, by moving a successor, then it may complete but not efficient. Simulated Annealing is an algorithm which yields both efficiency and completeness. In mechanical term Annealing is a process of hardening a metal or glass to a high temperature then cooling gradually, so this allows the metal to reach a low-energy crystalline state. The same process is used in simulated annealing in which the algorithm picks a random move, instead of picking the best move. If the random move improves the state, then it follows the same path. Otherwise, the algorithm follows the path which has a probability of less than 1 or it moves downhill and chooses another path. The hill climbing technique has seen wide-spread usage in artificial intelligence and optimization respectively. It methodically solves those problems via coupled research activities by systematically testing options and picking out the most appropriate one. Some of the application are as follows: Some of the application are as follows: 1. Machine Learning: Fine tuning of machine learning models frequently is doing the hyper parameter optimization that provides the model with guidance on how it learns and behaves. Another exercise which serves the same purpose is hill training. Gradual adjustment of hyperparameters and their evaluation according to the respectively reached the essence of the hill climbing method. 2. Robotics: In robotics, hill climbing technique turns out to be useful for an artificial agent roaming through a physical environment where its path is adjusted before arriving at the destination. 3. Network Design: The tool may be employed for improvement of network forms, processes, and topologies in the telecommunications industry and computer networks. This approach erases the redundancy thus the efficiency of the networks are increased by studying and adjusting their configurations. It facilitates better cooperation, efficiency, and the reliability of diverse communication system. 4. Game playing: Altough the hill climbing can be optimal in game playing AI by developing the strategies which helps to get the maximum scores. 5. Natural language processing: The software assists in adjusting the algorithms to enable the software to be efficient at dealing with the tasks at hand such as summarizing text, translating languages and recognizing speech. These abilities owing to it as a significant tool for many applications. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Problem-solving

Means-Ends Analysis in AI - Javatpoint

Means-Ends Analysis in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials How means-ends analysis Works: Operator Subgoaling Algorithm for Means-Ends Analysis: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example of Mean-Ends Analysis: Solution: Contact info Follow us Tutorials Interview Questions Online Compiler The means-ends analysis process can be applied recursively for a problem. It is a strategy to control search in problem-solving. Following are the main Steps which describes the working of MEA technique for solving a problem. In the MEA process, we detect the differences between the current state and goal state. Once these differences occur, then we can apply an operator to reduce the differences. But sometimes it is possible that an operator cannot be applied to the current state. So we create the subproblem of the current state, in which operator can be applied, such type of backward chaining in which operators are selected, and then sub goals are set up to establish the preconditions of the operator is called Operator Subgoaling. Let's we take Current state as CURRENT and Goal State as GOAL, then following are the steps for the MEA algorithm. The above-discussed algorithm is more suitable for a simple problem and not adequate for solving complex problems. Let's take an example where we know the initial state and goal state as given below. In this problem, we need to get the goal state by finding differences between the initial state and goal state and applying operators. To solve the above problem, we will first find the differences between initial states and goal states, and for each difference, we will generate a new state and will apply the operators. The operators we have for this problem are: 1. Evaluating the initial state: In the first step, we will evaluate the initial state and will compare the initial and Goal state to find the differences between both states. 2. Applying Delete operator: As we can check the first difference is that in goal state there is no dot symbol which is present in the initial state, so, first we will apply the Delete operator to remove this dot. 3. Applying Move Operator: After applying the Delete operator, the new state occurs which we will again compare with goal state. After comparing these states, there is another difference that is the square is outside the circle, so, we will apply the Move Operator. 4. Applying Expand Operator: Now a new state is generated in the third step, and we will compare this state with the goal state. After comparing the states there is still one difference which is the size of the square, so, we will apply Expand operator, and finally, it will generate the goal state. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Adversarial Search

Artificial Intelligence | Adversarial Search - Javatpoint

Adversarial Search Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Types of Games in AI: Zero-Sum Game Game tree: Important Features of Adversarial Search Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Zero-sum game: Embedded thinking Formalization of the problem: Note: In this topic, we will discuss deterministic games, fully observable environment, zero-sum, and where each agent acts alternatively. Contact info Follow us Tutorials Interview Questions Online Compiler Adversarial search is a search, where we examine the problem which arises when we try to plan ahead of the world and other agents are planning against us. The Zero-sum game involved embedded thinking in which one agent or player is trying to figure out: Each of the players is trying to find out the response of his opponent to their actions. This requires embedded thinking or backward reasoning to solve the game problems in AI. A game can be defined as a type of search in AI which can be formalized of the following elements: A game tree is a tree where nodes of the tree are the game states and Edges of the tree are the moves by players. Game tree involves initial state, actions function, and result Function. Example: Tic-Tac-Toe game tree: The following figure is showing part of the game-tree for tic-tac-toe game. Following are some key points of the game: Example Explanation: Hence adversarial Search for the minimax procedure works as follows: In a given game tree, the optimal strategy can be determined from the minimax value of each node, which can be written as MINIMAX(n). MAX prefer to move to a state of maximum value and MIN prefer to move to a state of minimum value then: An important field in artificial intelligence is adversarial search. This deals with decision-making when faced with hostile situations. Here are some key aspects of adversarial search: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Adversarial Search

Artificial Intelligence | Mini-Max Algorithm - Javatpoint

Mini-Max Algorithm in Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Pseudo-code for MinMax Algorithm: Working of Min-Max Algorithm: Properties of Mini-Max algorithm: Limitation of the minimax Algorithm: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Initial call: Minimax(node, 3, true) Step-1: In the first step, the algorithm generates the entire game-tree and apply the utility function to get the utility values for the terminal states. In the below tree diagram, let's take A is the initial state of the tree. Suppose maximizer takes first turn which has worst-case initial value =- infinity, and minimizer will take next turn which has worst-case initial value = +infinity. Step 2: Now, first we find the utilities value for the Maximizer, its initial value is -∞, so we will compare each value in terminal state with initial value of Maximizer and determines the higher nodes values. It will find the maximum among the all. Step 3: In the next step, it's a turn for minimizer, so it will compare all nodes value with +∞, and will find the 3rd layer node values. Step 4: Now it's a turn for Maximizer, and it will again choose the maximum of all nodes value and find the maximum value for the root node. In this game tree, there are only 4 layers, hence we reach immediately to the root node, but in real games, there will be more than 4 layers. That was the complete workflow of the minimax two player game. The main drawback of the minimax algorithm is that it gets really slow for complex games such as Chess, go, etc. This type of games has a huge branching factor, and the player has lots of choices to decide. This limitation of the minimax algorithm can be improved from alpha-beta pruning which we have discussed in the next topic. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Adversarial Search

Artificial Intelligence | Alpha-Beta Pruning - Javatpoint

Alpha-Beta Pruning Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Condition for Alpha-beta pruning: Key points about alpha-beta pruning: Pseudo-code for Alpha-beta Pruning: Working of Alpha-Beta Pruning: Move Ordering in Alpha-Beta pruning: Rules to find good ordering: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: To better understand this topic, kindly study the minimax algorithm. Contact info Follow us Tutorials Interview Questions Online Compiler The main condition which required for alpha-beta pruning is: Let's take an example of two-player search tree to understand the working of Alpha-beta pruning Step 1: At the first step the, Max player will start first move from node A where α= -∞ and β= +∞, these value of alpha and beta passed down to node B where again α= -∞ and β= +∞, and Node B passes the same value to its child D. Step 2: At Node D, the value of α will be calculated as its turn for Max. The value of α is compared with firstly 2 and then 3, and the max (2, 3) = 3 will be the value of α at node D and node value will also 3. Step 3: Now algorithm backtrack to node B, where the value of β will change as this is a turn of Min, Now β= +∞, will compare with the available subsequent nodes value, i.e. min (∞, 3) = 3, hence at node B now α= -∞, and β= 3. In the next step, algorithm traverse the next successor of Node B which is node E, and the values of α= -∞, and β= 3 will also be passed. Step 4: At node E, Max will take its turn, and the value of alpha will change. The current value of alpha will be compared with 5, so max (-∞, 5) = 5, hence at node E α= 5 and β= 3, where α>=β, so the right successor of E will be pruned, and algorithm will not traverse it, and the value at node E will be 5. Step 5: At next step, algorithm again backtrack the tree, from node B to node A. At node A, the value of alpha will be changed the maximum available value is 3 as max (-∞, 3)= 3, and β= +∞, these two values now passes to right successor of A which is Node C. At node C, α=3 and β= +∞, and the same values will be passed on to node F. Step 6: At node F, again the value of α will be compared with left child which is 0, and max(3,0)= 3, and then compared with right child which is 1, and max(3,1)= 3 still α remains 3, but the node value of F will become 1. Step 7: Node F returns the node value 1 to node C, at C α= 3 and β= +∞, here the value of beta will be changed, it will compare with 1 so min (∞, 1) = 1. Now at C, α=3 and β= 1, and again it satisfies the condition α>=β, so the next child of C which is G will be pruned, and the algorithm will not compute the entire sub-tree G. Step 8: C now returns the value of 1 to A here the best value for A is max (3, 1) = 3. Following is the final game tree which is the showing the nodes which are computed and nodes which has never computed. Hence the optimal value for the maximizer is 3 for this example. The effectiveness of alpha-beta pruning is highly dependent on the order in which each node is examined. Move order is an important aspect of alpha-beta pruning. It can be of two types: Following are some rules to find good ordering in alpha-beta pruning: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Knowledge Based Agent in AI - Javatpoint

Knowledge-Based Agent in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials The architecture of knowledge-based agent: Why use a knowledge base? Inference system Operations Performed by KBA A generic knowledge-based agent: Various levels of knowledge-based agent: Approaches to designing a knowledge-based agent: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Knowledge level 2. Logical level: 3. Implementation level: Contact info Follow us Tutorials Interview Questions Online Compiler A knowledge-based agent must able to do the following: The above diagram is representing a generalized architecture for a knowledge-based agent. The knowledge-based agent (KBA) take input from the environment by perceiving the environment. The input is taken by the inference engine of the agent and which also communicate with KB to decide as per the knowledge store in KB. The learning element of KBA regularly updates the KB by learning new knowledge. Knowledge base: Knowledge-base is a central component of a knowledge-based agent, it is also known as KB. It is a collection of sentences (here 'sentence' is a technical term and it is not identical to sentence in English). These sentences are expressed in a language which is called a knowledge representation language. The Knowledge-base of KBA stores fact about the world. Knowledge-base is required for updating knowledge for an agent to learn with experiences and take action as per the knowledge. Inference means deriving new sentences from old. Inference system allows us to add a new sentence to the knowledge base. A sentence is a proposition about the world. Inference system applies logical rules to the KB to deduce new information. Inference system generates new facts so that an agent can update the KB. An inference system works mainly in two rules which are given as: Following are three operations which are performed by KBA in order to show the intelligent behavior: Following is the structure outline of a generic knowledge-based agents program: The knowledge-based agent takes percept as input and returns an action as output. The agent maintains the knowledge base, KB, and it initially has some background knowledge of the real world. It also has a counter to indicate the time for the whole process, and this counter is initialized with zero. Each time when the function is called, it performs its three operations: The MAKE-PERCEPT-SENTENCE generates a sentence as setting that the agent perceived the given percept at the given time. The MAKE-ACTION-QUERY generates a sentence to ask which action should be done at the current time. MAKE-ACTION-SENTENCE generates a sentence which asserts that the chosen action was executed. A knowledge-based agent can be viewed at different levels which are given below: Knowledge level is the first level of knowledge-based agent, and in this level, we need to specify what the agent knows, and what the agent goals are. With these specifications, we can fix its behavior. For example, suppose an automated taxi agent needs to go from a station A to station B, and he knows the way from A to B, so this comes at the knowledge level. At this level, we understand that how the knowledge representation of knowledge is stored. At this level, sentences are encoded into different logics. At the logical level, an encoding of knowledge into logical sentences occurs. At the logical level we can expect to the automated taxi agent to reach to the destination B. This is the physical representation of logic and knowledge. At the implementation level agent perform actions as per logical and knowledge level. At this level, an automated taxi agent actually implement his knowledge and logic so that he can reach to the destination. There are mainly two approaches to build a knowledge-based agent: However, in the real world, a successful agent can be built by combining both declarative and procedural approaches, and declarative knowledge can often be compiled into more efficient procedural code. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Knowledge Representation in Artificial Intelligence - Javatpoint

What is knowledge representation? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What to Represent: Types of knowledge The relation between knowledge and intelligence: AI knowledge cycle: Approaches to knowledge representation: Requirements for knowledge Representation system: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Simple relational knowledge: 2. Inheritable knowledge: 3. Inferential knowledge: 4. Procedural knowledge: Contact info Follow us Tutorials Interview Questions Online Compiler Humans are best at understanding, reasoning, and interpreting knowledge. Human knows things, which is knowledge and as per their knowledge they perform various actions in the real world. But how machines do all these things comes under knowledge representation and reasoning. Hence we can describe Knowledge representation as following: Following are the kind of knowledge which needs to be represented in AI systems: Knowledge: Knowledge is awareness or familiarity gained by experiences of facts, data, and situations. Following are the types of knowledge in artificial intelligence: Following are the various types of knowledge: 1. Declarative Knowledge: 2. Procedural Knowledge 3. Meta-knowledge: 4. Heuristic knowledge: 5. Structural knowledge: Knowledge of real-worlds plays a vital role in intelligence and same for creating artificial intelligence. Knowledge plays an important role in demonstrating intelligent behavior in AI agents. An agent is only able to accurately act on some input when he has some knowledge or experience about that input. Let's suppose if you met some person who is speaking in a language which you don't know, then how you will able to act on that. The same thing applies to the intelligent behavior of the agents. As we can see in below diagram, there is one decision maker which act by sensing the environment and using knowledge. But if the knowledge part will not present then, it cannot display intelligent behavior. An Artificial intelligence system has the following components for displaying intelligent behavior: The above diagram is showing how an AI system can interact with the real world and what components help it to show intelligence. AI system has Perception component by which it retrieves information from its environment. It can be visual, audio or another form of sensory input. The learning component is responsible for learning from data captured by Perception comportment. In the complete cycle, the main components are knowledge representation and Reasoning. These two components are involved in showing the intelligence in machine-like humans. These two components are independent with each other but also coupled together. The planning and execution depend on analysis of Knowledge representation and reasoning. There are mainly four approaches to knowledge representation, which are givenbelow: Example: The following is the simple relational knowledge representation. A good knowledge representation system must possess the following properties. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

AI Techniques of Knowledge Representation - Javatpoint

Techniques of knowledge representation Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials 1. Logical Representation 2. Semantic Network Representation 3. Frame Representation 4. Production Rules Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Syntax: Semantics: Advantages of logical representation: Disadvantages of logical Representation: Statements: Drawbacks in Semantic representation: Advantages of Semantic network: Example: 1 Example 2: Advantages of frame representation: Disadvantages of frame representation: Example: Advantages of Production rule: Disadvantages of Production rule: Note: We will discuss Prepositional Logics and Predicate logics in later chapters. Note: Do not be confused with logical representation and logical reasoning as logical representation is a representation language and reasoning is a process of thinking logically. Contact info Follow us Tutorials Interview Questions Online Compiler There are mainly four ways of knowledge representation which are given as follows: Logical representation is a language with some concrete rules which deals with propositions and has no ambiguity in representation. Logical representation means drawing a conclusion based on various conditions. This representation lays down some important communication rules. It consists of precisely defined syntax and semantics which supports the sound inference. Each sentence can be translated into logics using syntax and semantics. Logical representation can be categorised into mainly two logics: Semantic networks are alternative of predicate logic for knowledge representation. In Semantic networks, we can represent our knowledge in the form of graphical networks. This network consists of nodes representing objects and arcs which describe the relationship between those objects. Semantic networks can categorize the object in different forms and can also link those objects. Semantic networks are easy to understand and can be easily extended. This representation consist of mainly two types of relations: Example: Following are some statements which we need to represent in the form of nodes and arcs. In the above diagram, we have represented the different type of knowledge in the form of nodes and arcs. Each object is connected with another object by some relation. A frame is a record like structure which consists of a collection of attributes and its values to describe an entity in the world. Frames are the AI data structure which divides knowledge into substructures by representing stereotypes situations. It consists of a collection of slots and slot values. These slots may be of any type and sizes. Slots have names and values which are called facets. Facets: The various aspects of a slot is known as Facets. Facets are features of frames which enable us to put constraints on the frames. Example: IF-NEEDED facts are called when data of any particular slot is needed. A frame may consist of any number of slots, and a slot may include any number of facets and facets may have any number of values. A frame is also known as slot-filter knowledge representation in artificial intelligence. Frames are derived from semantic networks and later evolved into our modern-day classes and objects. A single frame is not much useful. Frames system consist of a collection of frames which are connected. In the frame, knowledge about an object or event can be stored together in the knowledge base. The frame is a type of technology which is widely used in various applications including Natural language processing and machine visions. Let's take an example of a frame for a book Let's suppose we are taking an entity, Peter. Peter is an engineer as a profession, and his age is 25, he lives in city London, and the country is England. So following is the frame representation for this: Production rules system consist of (condition, action) pairs which mean, "If condition then action". It has mainly three parts: In production rules agent checks for the condition and if the condition exists then production rule fires and corresponding action is carried out. The condition part of the rule determines which rule may be applied to a problem. And the action part carries out the associated problem-solving steps. This complete process is called a recognize-act cycle. The working memory contains the description of the current state of problems-solving and rule can write knowledge to the working memory. This knowledge match and may fire other rules. If there is a new situation (state) generates, then multiple production rules will be fired together, this is called conflict set. In this situation, the agent needs to select a rule from these sets, and it is called a conflict resolution. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Propositional Logic in Artificial Intelligence - Javatpoint

Propositional logic in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Logical Connectives: Truth Table: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Syntax of propositional logic: Following is the summarized table for Propositional Logic Connectives: Truth table with three propositions: Precedence of connectives: Logical equivalence: Properties of Operators: Limitations of Propositional logic: Note: For better understanding use parenthesis to make sure of the correct interpretations. Such as ¬R∨ Q, It can be interpreted as (¬R) ∨ Q. Contact info Follow us Tutorials Interview Questions Online Compiler Propositional logic (PL) is the simplest form of logic where all the statements are made by propositions. A proposition is a declarative statement which is either true or false. It is a technique of knowledge representation in logical and mathematical form. Following are some basic facts about propositional logic: The syntax of propositional logic defines the allowable sentences for the knowledge representation. There are two types of Propositions: Example: Example: Logical connectives are used to connect two simpler propositions or representing a sentence logically. We can create compound propositions with the help of logical connectives. There are mainly five connectives, which are given as follows: In propositional logic, we need to know the truth values of propositions in all possible scenarios. We can combine all the possible combination with logical connectives, and the representation of these combinations in a tabular format is called Truth table. Following are the truth table for all logical connectives: We can build a proposition composing three propositions P, Q, and R. This truth table is made-up of 8n Tuples as we have taken three proposition symbols. Just like arithmetic operators, there is a precedence order for propositional connectors or logical operators. This order should be followed while evaluating a propositional problem. Following is the list of the precedence order for operators: Logical equivalence is one of the features of propositional logic. Two propositions are said to be logically equivalent if and only if the columns in the truth table are identical to each other. Let's take two propositions A and B, so for logical equivalence, we can write it as A⇔B. In below truth table we can see that column for ¬A∨ B and A→B, are identical hence A is Equivalent to B We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Rules of Inference in Artificial Intelligence - Javatpoint

Rules of Inference in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Inference: Inference rules: Types of Inference rules: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Modus Ponens: 2. Modus Tollens: 3. Hypothetical Syllogism: 4. Disjunctive Syllogism: 5. Addition: 6. Simplification: 7. Resolution: Contact info Follow us Tutorials Interview Questions Online Compiler In artificial intelligence, we need intelligent computers which can create new logic from old logic or by evidence, so generating the conclusions from evidence and facts is termed as Inference. Inference rules are the templates for generating valid arguments. Inference rules are applied to derive proofs in artificial intelligence, and the proof is a sequence of the conclusion that leads to the desired goal. In inference rules, the implication among all the connectives plays an important role. Following are some terminologies related to inference rules: From the above term some of the compound statements are equivalent to each other, which we can prove using truth table: Hence from the above truth table, we can prove that P → Q is equivalent to ¬ Q → ¬ P, and Q→ P is equivalent to ¬ P → ¬ Q. The Modus Ponens rule is one of the most important rules of inference, and it states that if P and P → Q is true, then we can infer that Q will be true. It can be represented as: Example: Statement-1: "If I am sleepy then I go to bed" ==> P→ QStatement-2: "I am sleepy" ==> PConclusion: "I go to bed." ==> Q.Hence, we can say that, if P→ Q is true and P is true then Q will be true. Proof by Truth table: The Modus Tollens rule state that if P→ Q is true and ¬ Q is true, then ¬ P will also true. It can be represented as: Statement-1: "If I am sleepy then I go to bed" ==> P→ QStatement-2: "I do not go to the bed."==> ~QStatement-3: Which infers that "I am not sleepy" => ~P Proof by Truth table: The Hypothetical Syllogism rule state that if P→R is true whenever P→Q is true, and Q→R is true. It can be represented as the following notation: Example: Statement-1: If you have my home key then you can unlock my home. P→QStatement-2: If you can unlock my home then you can take my money. Q→RConclusion: If you have my home key then you can take my money. P→R Proof by truth table: The Disjunctive syllogism rule state that if P∨Q is true, and ¬P is true, then Q will be true. It can be represented as: Example: Statement-1: Today is Sunday or Monday. ==>P∨QStatement-2: Today is not Sunday. ==> ¬PConclusion: Today is Monday. ==> Q Proof by truth-table: The Addition rule is one the common inference rule, and it states that If P is true, then P∨Q will be true. Example: Statement: I have a vanilla ice-cream. ==> PStatement-2: I have Chocolate ice-cream.Conclusion: I have vanilla or chocolate ice-cream. ==> (P∨Q) Proof by Truth-Table: The simplification rule state that if P∧ Q is true, then Q or P will also be true. It can be represented as: Proof by Truth-Table: The Resolution rule state that if P∨Q and ¬ P∧R is true, then Q∨R will also be true. It can be represented as Proof by Truth-Table: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

The Wumpus world in Artificial Intelligence - Javatpoint

The Wumpus World in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Wumpus world: PEAS description of Wumpus world: The Wumpus world Properties: Exploring the Wumpus world: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Performance measure: Environment: Actuators: Sensors: Note: Here Wumpus is static and cannot move. Contact info Follow us Tutorials Interview Questions Online Compiler The Wumpus world is a simple world example to illustrate the worth of a knowledge-based agent and to represent knowledge representation. It was inspired by a video game Hunt the Wumpus by Gregory Yob in 1973. The Wumpus world is a cave which has 4/4 rooms connected with passageways. So there are total 16 rooms which are connected with each other. We have a knowledge-based agent who will go forward in this world. The cave has a room with a beast which is called Wumpus, who eats anyone who enters the room. The Wumpus can be shot by the agent, but the agent has a single arrow. In the Wumpus world, there are some Pits rooms which are bottomless, and if agent falls in Pits, then he will be stuck there forever. The exciting thing with this cave is that in one room there is a possibility of finding a heap of gold. So the agent goal is to find the gold and climb out the cave without fallen into Pits or eaten by Wumpus. The agent will get a reward if he comes out with gold, and he will get a penalty if eaten by Wumpus or falls in the pit. Following is a sample diagram for representing the Wumpus world. It is showing some rooms with Pits, one room with Wumpus and one agent at (1, 1) square location of the world. There are also some components which can help the agent to navigate the cave. These components are given as follows: To explain the Wumpus world we have given PEAS description as below: Now we will explore the Wumpus world and will determine how the agent will find its goal by applying logical reasoning. Agent's First step: Initially, the agent is in the first room or on the square [1,1], and we already know that this room is safe for the agent, so to represent on the below diagram (a) that room is safe we will add symbol OK. Symbol A is used to represent agent, symbol B for the breeze, G for Glitter or gold, V for the visited room, P for pits, W for Wumpus. At Room [1,1] agent does not feel any breeze or any Stench which means the adjacent squares are also OK. Agent's second Step: Now agent needs to move forward, so it will either move to [1, 2], or [2,1]. Let's suppose agent moves to the room [2, 1], at this room agent perceives some breeze which means Pit is around this room. The pit can be in [3, 1], or [2,2], so we will add symbol P? to say that, is this Pit room? Now agent will stop and think and will not make any harmful move. The agent will go back to the [1, 1] room. The room [1,1], and [2,1] are visited by the agent, so we will use symbol V to represent the visited squares. Agent's third step: At the third step, now agent will move to the room [1,2] which is OK. In the room [1,2] agent perceives a stench which means there must be a Wumpus nearby. But Wumpus cannot be in the room [1,1] as by rules of the game, and also not in [2,2] (Agent had not detected any stench when he was at [2,1]). Therefore agent infers that Wumpus is in the room [1,3], and in current state, there is no breeze which means in [2,2] there is no Pit and no Wumpus. So it is safe, and we will mark it OK, and the agent moves further in [2,2]. Agent's fourth step: At room [2,2], here no stench and no breezes present so let's suppose agent decides to move to [2,3]. At room [2,3] agent perceives glitter, so it should grab the gold and climb out of the cave. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

knowledge-base for Wumpus World - Javatpoint

Knowledge-base for Wumpus world Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Prove that Wumpus is in the room (1, 3) Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Atomic proposition variable for Wumpus world: Some Propositional Rules for the wumpus world: Representation of Knowledgebase for Wumpus world: Note: For a 4 * 4 square board, there will be 7*4*4= 122 propositional variables. Note: lack of variables gives us similar rules for each cell. Contact info Follow us Tutorials Interview Questions Online Compiler As in the previous topic we have learned about the wumpus world and how a knowledge-based agent evolves the world. Now in this topic, we will create a knowledge base for the wumpus world, and will derive some proves for the Wumpus-world using propositional logic. The agent starts visiting from first square [1, 1], and we already know that this room is safe for the agent. To build a knowledge base for wumpus world, we will use some rules and atomic propositions. We need symbol [i, j] for each location in the wumpus world, where i is for the location of rows, and j for column location. Following is the Simple KB for wumpus world when an agent moves from room [1, 1], to room [2,1]: Here in the first row, we have mentioned propositional variables for room[1,1], which is showing that room does not have wumpus(¬ W11), no stench (¬S11), no Pit(¬P11), no breeze(¬B11), no gold (¬G11), visited (V11), and the room is Safe(OK11). In the second row, we have mentioned propositional variables for room [1,2], which is showing that there is no wumpus, stench and breeze are unknown as an agent has not visited room [1,2], no Pit, not visited yet, and the room is safe. In the third row we have mentioned propositional variable for room[2,1], which is showing that there is no wumpus(¬ W21), no stench (¬S21), no Pit (¬P21), Perceives breeze(B21), no glitter(¬G21), visited (V21), and room is safe (OK21). We can prove that wumpus is in the room (1, 3) using propositional rules which we have derived for the wumpus world and using inference rule. We will firstly apply MP rule with R1 which is ¬S11 → ¬ W11 ^ ¬ W12 ^ ¬ W21, and ¬S11 which will give the output ¬ W11 ^ W12 ^ W12. After applying And-elimination rule to ¬ W11 ∧ ¬ W12 ∧ ¬ W21, we will get three statements:¬ W11, ¬ W12, and ¬W21. Now we will apply Modus Ponens to ¬S21 and R2 which is ¬S21 → ¬ W21 ∧¬ W22 ∧ ¬ W31, which will give the Output as ¬ W21 ∧ ¬ W22 ∧¬ W31 Now again apply And-elimination rule to ¬ W21 ∧ ¬ W22 ∧¬ W31, We will get three statements:¬ W21, ¬ W22, and ¬ W31. Apply Modus Ponens to S12 and R4 which is S12 → W13 ∨. W12 ∨. W22 ∨.W11, we will get the output as W13∨ W12 ∨ W22 ∨.W11. After applying Unit resolution formula on W13 ∨ W12 ∨ W22 ∨W11 and ¬ W11 we will get W13 ∨ W12 ∨ W22. After applying Unit resolution on W13 ∨ W12 ∨ W22, and ¬W22, we will get W13 ∨ W12 as output. After Applying Unit resolution on W13 ∨ W12 and ¬ W12, we will get W13 as an output, hence it is proved that the Wumpus is in the room [1, 3]. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

First-order logic in Artificial Intelligence - Javatpoint

First-Order Logic in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials First-Order logic: Syntax of First-Order logic: Quantifiers in First-order logic: Existential Quantifier: Points to remember: Properties of Quantifiers: Free and Bound Variables: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Basic Elements of First-order logic: Atomic sentences: Complex Sentences: Universal Quantifier: Example: Example: Note: In universal quantifier we use implication "→". Note: In Existential quantifier we always use AND or Conjunction symbol (∧). Contact info Follow us Tutorials Interview Questions Online Compiler In the topic of Propositional logic, we have seen that how to represent statements using propositional logic. But unfortunately, in propositional logic, we can only represent the facts, which are either true or false. PL is not sufficient to represent the complex sentences or natural language statements. The propositional logic has very limited expressive power. Consider the following sentence, which we cannot represent using PL logic. To represent the above statements, PL logic is not sufficient, so we required some more powerful logic, such as first-order logic. The syntax of FOL determines which collection of symbols is a logical expression in first-order logic. The basic syntactic elements of first-order logic are symbols. We write statements in short-hand notation in FOL. Following are the basic elements of FOL syntax: Example: Ravi and Ajay are brothers: => Brothers(Ravi, Ajay).                Chinky is a cat: => cat (Chinky). First-order logic statements can be divided into two parts: Consider the statement: "x is an integer.", it consists of two parts, the first part x is the subject of the statement and second part "is an integer," is known as a predicate. Universal quantifier is a symbol of logical representation, which specifies that the statement within its range is true for everything or every instance of a particular thing. The Universal quantifier is represented by a symbol ∀, which resembles an inverted A. If x is a variable, then ∀x is read as: All man drink coffee. Let a variable x which refers to a cat so all x can be represented in UOD as below: ∀x man(x) → drink (x, coffee). It will be read as: There are all x where x is a man who drink coffee. Existential quantifiers are the type of quantifiers, which express that the statement within its scope is true for at least one instance of something. It is denoted by the logical operator ∃, which resembles as inverted E. When it is used with a predicate variable then it is called as an existential quantifier. If x is a variable, then existential quantifier will be ∃x or ∃(x). And it will be read as: Some boys are intelligent. ∃x: boys(x) ∧ intelligent(x) It will be read as: There are some x where x is a boy who is intelligent. Some Examples of FOL using quantifier: 1. All birds fly.In this question the predicate is "fly(bird)."And since there are all birds who fly so it will be represented as follows.              ∀x bird(x) →fly(x). 2. Every man respects his parent.In this question, the predicate is "respect(x, y)," where x=man, and y= parent.Since there is every man so will use ∀, and it will be represented as follows:              ∀x man(x) → respects (x, parent). 3. Some boys play cricket.In this question, the predicate is "play(x, y)," where x= boys, and y= game. Since there are some boys so we will use ∃, and it will be represented as:              ∃x boys(x) → play(x, cricket). 4. Not all students like both Mathematics and Science.In this question, the predicate is "like(x, y)," where x= student, and y= subject.Since there are not all students, so we will use ∀ with negation, so following representation for this:              ¬∀ (x) [ student(x) → like(x, Mathematics) ∧ like(x, Science)]. 5. Only one student failed in Mathematics.In this question, the predicate is "failed(x, y)," where x= student, and y= subject.Since there is only one student who failed in Mathematics, so we will use following representation for this:              ∃(x) [ student(x) → failed (x, Mathematics) ∧∀ (y) [¬(x==y) ∧ student(y) → ¬failed (x, Mathematics)]. The quantifiers interact with variables which appear in a suitable way. There are two types of variables in First-order logic which are given below: Free Variable: A variable is said to be a free variable in a formula if it occurs outside the scope of the quantifier. Example: ∀x ∃(y)[P (x, y, z)], where z is a free variable. Bound Variable: A variable is said to be a bound variable in a formula if it occurs within the scope of the quantifier. Example: ∀x [A (x) B( y)], here x and y are the bound variables. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Knowledge Engineering in First-order logic - Javatpoint

Knowledge Engineering in First-order logic Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is knowledge-engineering? The knowledge-engineering process: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Identify the task: 2. Assemble the relevant knowledge: 3. Decide on vocabulary: 4. Encode general knowledge about the domain: 5. Encode a description of the problem instance: 6. Pose queries to the inference procedure and get answers: 7. Debug the knowledge base: Note: Ontology defines a particular theory of the nature of existence. Contact info Follow us Tutorials Interview Questions Online Compiler The process of constructing a knowledge-base in first-order logic is called as knowledge- engineering. In knowledge-engineering, someone who investigates a particular domain, learns important concept of that domain, and generates a formal representation of the objects, is known as knowledge engineer. In this topic, we will understand the Knowledge engineering process in an electronic circuit domain, which is already familiar. This approach is mainly suitable for creating special-purpose knowledge base. Following are some main steps of the knowledge-engineering process. Using these steps, we will develop a knowledge base which will allow us to reason about digital circuit (One-bit full adder) which is given below The first step of the process is to identify the task, and for the digital circuit, there are various reasoning tasks. At the first level or highest level, we will examine the functionality of the circuit: At the second level, we will examine the circuit structure details such as: In the second step, we will assemble the relevant knowledge which is required for digital circuits. So for digital circuits, we have the following required knowledge: The next step of the process is to select functions, predicate, and constants to represent the circuits, terminals, signals, and gates. Firstly we will distinguish the gates from each other and from other objects. Each gate is represented as an object which is named by a constant, such as, Gate(X1). The functionality of each gate is determined by its type, which is taken as constants such as AND, OR, XOR, or NOT. Circuits will be identified by a predicate: Circuit (C1). For the terminal, we will use predicate: Terminal(x). For gate input, we will use the function In(1, X1) for denoting the first input terminal of the gate, and for output terminal we will use Out (1, X1). The function Arity(c, i, j) is used to denote that circuit c has i input, j output. The connectivity between gates can be represented by predicate Connect(Out(1, X1), In(1, X1)). We use a unary predicate On (t), which is true if the signal at a terminal is on. To encode the general knowledge about the logic circuit, we need some following rules: Now we encode problem of circuit C1, firstly we categorize the circuit and its gate components. This step is easy if ontology about the problem is already thought. This step involves the writing simple atomics sentences of instances of concepts, which is known as ontology. For the given circuit C1, we can encode the problem instance in atomic sentences as below: Since in the circuit there are two XOR, two AND, and one OR gate so atomic sentences for these gates will be: And then represent the connections between all the gates. In this step, we will find all the possible set of values of all the terminal for the adder circuit. The first query will be: What should be the combination of input which would generate the first output of circuit C1, as 0 and a second output to be 1? Now we will debug the knowledge base, and this is the last step of the complete process. In this step, we will try to debug the issues of knowledge base. In the knowledge base, we may have omitted assertions like 1 ≠ 0. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Inference in First-Order Logic - Javatpoint

Inference in First-Order Logic Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials FOL inference rules for quantifier: Generalized Modus Ponens Rule: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: First-order logic is capable of expressing facts about some or all objects in the universe. Contact info Follow us Tutorials Interview Questions Online Compiler Inference in First-Order Logic is used to deduce new facts or sentences from existing sentences. Before understanding the FOL inference rule, let's understand some basic terminologies used in FOL. Substitution: Substitution is a fundamental operation performed on terms and formulas. It occurs in all inference systems in first-order logic. The substitution is complex in the presence of quantifiers in FOL. If we write F[a/x], so it refers to substitute a constant "a" in place of variable "x". Equality: First-Order logic does not only use predicate and terms for making atomic sentences but also uses another way, which is equality in FOL. For this, we can use equality symbols which specify that the two terms refer to the same object. Example: Brother (John) = Smith. As in the above example, the object referred by the Brother (John) is similar to the object referred by Smith. The equality symbol can also be used with negation to represent that two terms are not the same objects. Example: ￢(x=y) which is equivalent to x ≠y. As propositional logic we also have inference rules in first-order logic, so following are some basic inference rules in FOL: 1. Universal Generalization: Example: Let's represent, P(c): "A byte contains 8 bits", so for ∀ x P(x) "All bytes contain 8 bits.", it will also be true. 2. Universal Instantiation: Example:1. IF "Every person like ice-cream"=> ∀x P(x) so we can infer that"John likes ice-cream" => P(c) Example: 2. Let's take a famous example, "All kings who are greedy are Evil." So let our knowledge base contains this detail as in the form of FOL: ∀x king(x) ∧ greedy (x) → Evil (x), So from this information, we can infer any of the following statements using Universal Instantiation: 3. Existential Instantiation: Example: From the given sentence: ∃x Crown(x) ∧ OnHead(x, John), So we can infer: Crown(K) ∧ OnHead( K, John), as long as K does not appear in the knowledge base. 4. Existential introduction For the inference process in FOL, we have a single inference rule which is called Generalized Modus Ponens. It is lifted version of Modus ponens. Generalized Modus Ponens can be summarized as, " P implies Q and P is asserted to be true, therefore Q must be True." According to Modus Ponens, for atomic sentences pi, pi', q. Where there is a substitution θ such that SUBST (θ, pi',) = SUBST(θ, pi), it can be represented as: Example: We will use this rule for Kings are evil, so we will find some x such that x is king, and x is greedy so we can infer that x is evil. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Unification in First-order logic - Javatpoint

What is Unification? Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Conditions for Unification: Unification Algorithm: Implementation of the Algorithm Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Let Ψ1 = King(x), Ψ2 = King(John), Substitution θ = {John/x} is a unifier for these atoms and applying this substitution, and both expressions will be identical. E.g. Let's say there are two different expressions, P(x, y), and P(a, f(z)). In this example, we need to make both above statements identical to each other. For this, we will perform the substitution. P(x, y)......... (i)            P(a, f(z))......... (ii) Following are some basic conditions for unification: Algorithm: Unify(Ψ1, Ψ2) Step.1: Initialize the substitution set to be empty. Step.2: Recursively unify atomic sentences: For each pair of the following atomic sentences find the most general unifier (If exist). 1. Find the MGU of {p(f(a), g(Y)) and p(X, X)} Sol: S0 => Here, Ψ1 = p(f(a), g(Y)), and Ψ2 = p(X, X)                  SUBST θ= {f(a) / X}                  S1 => Ψ1 = p(f(a), g(Y)), and Ψ2 = p(f(a), f(a))                  SUBST θ= {f(a) / g(y)}, Unification failed. Unification is not possible for these expressions. 2. Find the MGU of {p(b, X, f(g(Z))) and p(Z, f(Y), f(Y))} Here, Ψ1 = p(b, X, f(g(Z))) , and Ψ2 = p(Z, f(Y), f(Y))S0 => { p(b, X, f(g(Z))); p(Z, f(Y), f(Y))}SUBST θ={b/Z} S1 => { p(b, X, f(g(b))); p(b, f(Y), f(Y))}SUBST θ={f(Y) /X} S2 => { p(b, f(Y), f(g(b))); p(b, f(Y), f(Y))}SUBST θ= {g(b) /Y} S2 => { p(b, f(g(b)), f(g(b)); p(b, f(g(b)), f(g(b))} Unified Successfully.And Unifier = { b/Z, f(Y) /X , g(b) /Y}. 3. Find the MGU of {p (X, X), and p (Z, f(Z))} Here, Ψ1 = {p (X, X), and Ψ2 = p (Z, f(Z))S0 => {p (X, X), p (Z, f(Z))}SUBST θ= {X/Z}              S1 => {p (Z, Z), p (Z, f(Z))}SUBST θ= {f(Z) / Z}, Unification Failed. Hence, unification is not possible for these expressions. 4. Find the MGU of UNIFY(prime (11), prime(y)) Here, Ψ1 = {prime(11) , and Ψ2 = prime(y)}S0 => {prime(11) , prime(y)}SUBST θ= {11/y} S1 => {prime(11) , prime(11)} , Successfully unified.              Unifier: {11/y}. 5. Find the MGU of Q(a, g(x, a), f(y)), Q(a, g(f(b), a), x)} Here, Ψ1 = Q(a, g(x, a), f(y)), and Ψ2 = Q(a, g(f(b), a), x)S0 => {Q(a, g(x, a), f(y)); Q(a, g(f(b), a), x)}SUBST θ= {f(b)/x}S1 => {Q(a, g(f(b), a), f(y)); Q(a, g(f(b), a), f(b))} SUBST θ= {b/y}S1 => {Q(a, g(f(b), a), f(b)); Q(a, g(f(b), a), f(b))}, Successfully Unified. Unifier: [a/a, f(b)/x, b/y]. 6. UNIFY(knows(Richard, x), knows(Richard, John)) Here, Ψ1 = knows(Richard, x), and Ψ2 = knows(Richard, John)S0 => { knows(Richard, x); knows(Richard, John)}SUBST θ= {John/x}S1 => { knows(Richard, John); knows(Richard, John)}, Successfully Unified.Unifier: {John/x}. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Resolution in First-order logic - Javatpoint

Resolution in FOL Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Resolution The resolution inference rule: Steps for Resolution: Explanation of Resolution graph: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Example: Note: To better understand this topic, firstly learns the FOL in AI. Note: Statements "food(Apple) Λ food(vegetables)" and "eats (Anil, Peanuts) Λ alive(Anil)" can be written in two separate statements. Contact info Follow us Tutorials Interview Questions Online Compiler Resolution is a theorem proving technique that proceeds by building refutation proofs, i.e., proofs by contradictions. It was invented by a Mathematician John Alan Robinson in the year 1965. Resolution is used, if there are various statements are given, and we need to prove a conclusion of those statements. Unification is a key concept in proofs by resolutions. Resolution is a single inference rule which can efficiently operate on the conjunctive normal form or clausal form. Clause: Disjunction of literals (an atomic sentence) is called a clause. It is also known as a unit clause. Conjunctive Normal Form: A sentence represented as a conjunction of clauses is said to be conjunctive normal form or CNF. The resolution rule for first-order logic is simply a lifted version of the propositional rule. Resolution can resolve two clauses if they contain complementary literals, which are assumed to be standardized apart so that they share no variables. Where li and mj are complementary literals. This rule is also called the binary resolution rule because it only resolves exactly two literals. We can resolve two clauses which are given below: [Animal (g(x) V Loves (f(x), x)]       and       [￢ Loves(a, b) V ￢Kills(a, b)] Where two complimentary literals are: Loves (f(x), x) and ￢ Loves (a, b) These literals can be unified with unifier θ= [a/f(x), and b/x] , and it will generate a resolvent clause: [Animal (g(x) V ￢ Kills(f(x), x)]. To better understand all the above steps, we will take an example in which we will apply resolution. Step-1: Conversion of Facts into FOL In the first step we will convert all the given statements into its first order logic. Step-2: Conversion of FOL into CNF In First order logic resolution, it is required to convert the FOL into CNF as CNF form makes easier for resolution proofs. Step-3: Negate the statement to be proved In this statement, we will apply negation to the conclusion statements, which will be written as ¬likes(John, Peanuts) Step-4: Draw Resolution graph: Now in this step, we will solve the problem by resolution tree using substitution. For the above problem, it will be given as follows: Hence the negation of the conclusion has been proved as a complete contradiction with the given set of statements. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Forward Chaining and backward chaining in AI - Javatpoint

Forward Chaining and backward chaining in AI Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Inference engine: A. Forward Chaining Forward chaining proof: B. Backward Chaining: Backward-Chaining proof: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Example: Facts Conversion into FOL: Example: Contact info Follow us Tutorials Interview Questions Online Compiler In artificial intelligence, forward and backward chaining is one of the important topics, but before understanding forward and backward chaining lets first understand that from where these two terms came. The inference engine is the component of the intelligent system in artificial intelligence, which applies logical rules to the knowledge base to infer new information from known facts. The first inference engine was part of the expert system. Inference engine commonly proceeds in two modes, which are: Horn Clause and Definite clause: Horn clause and definite clause are the forms of sentences, which enables knowledge base to use a more restricted and efficient inference algorithm. Logical inference algorithms use forward and backward chaining approaches, which require KB in the form of the first-order definite clause. Definite clause: A clause which is a disjunction of literals with exactly one positive literal is known as a definite clause or strict horn clause. Horn clause: A clause which is a disjunction of literals with at most one positive literal is known as horn clause. Hence all the definite clauses are horn clauses. Example: (¬ p V ¬ q V k). It has only one positive literal k. Forward chaining is also known as a forward deduction or forward reasoning method when using an inference engine. Forward chaining is a form of reasoning which start with atomic sentences in the knowledge base and applies inference rules (Modus Ponens) in the forward direction to extract more data until a goal is reached. The Forward-chaining algorithm starts from known facts, triggers all rules whose premises are satisfied, and add their conclusion to the known facts. This process repeats until the problem is solved. Properties of Forward-Chaining: Consider the following famous example which we will use in both approaches: "As per the law, it is a crime for an American to sell weapons to hostile nations. Country A, an enemy of America, has some missiles, and all the missiles were sold to it by Robert, who is an American citizen." Prove that "Robert is criminal." To solve the above problem, first, we will convert all the above facts into first-order definite clauses, and then we will use a forward-chaining algorithm to reach the goal. Step-1: In the first step we will start with the known facts and will choose the sentences which do not have implications, such as: American(Robert), Enemy(A, America), Owns(A, T1), and Missile(T1). All these facts will be represented as below. Step-2: At the second step, we will see those facts which infer from available facts and with satisfied premises. Rule-(1) does not satisfy premises, so it will not be added in the first iteration. Rule-(2) and (3) are already added. Rule-(4) satisfy with the substitution {p/T1}, so Sells (Robert, T1, A) is added, which infers from the conjunction of Rule (2) and (3). Rule-(6) is satisfied with the substitution(p/A), so Hostile(A) is added and which infers from Rule-(7). Step-3: At step-3, as we can check Rule-(1) is satisfied with the substitution {p/Robert, q/T1, r/A}, so we can add Criminal(Robert) which infers all the available facts. And hence we reached our goal statement. Hence it is proved that Robert is Criminal using forward chaining approach. Backward-chaining is also known as a backward deduction or backward reasoning method when using an inference engine. A backward chaining algorithm is a form of reasoning, which starts with the goal and works backward, chaining through rules to find known facts that support the goal. Properties of backward chaining: In backward-chaining, we will use the same above example, and will rewrite all the rules. In Backward chaining, we will start with our goal predicate, which is Criminal(Robert), and then infer further rules. Step-1: At the first step, we will take the goal fact. And from the goal fact, we will infer other facts, and at last, we will prove those facts true. So our goal fact is "Robert is Criminal," so following is the predicate of it. Step-2: At the second step, we will infer other facts form goal fact which satisfies the rules. So as we can see in Rule-1, the goal predicate Criminal (Robert) is present with substitution {Robert/P}. So we will add all the conjunctive facts below the first level and will replace p with Robert. Here we can see American (Robert) is a fact, so it is proved here. Step-3:t At step-3, we will extract further fact Missile(q) which infer from Weapon(q), as it satisfies Rule-(5). Weapon (q) is also true with the substitution of a constant T1 at q. Step-4: At step-4, we can infer facts Missile(T1) and Owns(A, T1) form Sells(Robert, T1, r) which satisfies the Rule- 4, with the substitution of A in place of r. So these two statements are proved here. Step-5: At step-5, we can infer the fact Enemy(A, America) from Hostile(A) which satisfies Rule- 6. And hence all the statements are proved true using backward chaining. 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Knowledge Representation

Difference Between Backward Chaining and Forward Chaining - Javatpoint

Difference between backward chaining and forward chaining Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Following is the difference between the forward chaining and backward chaining: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Knowledge Representation

Reasoning in Artificial Intelligence - Javatpoint

Reasoning in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Reasoning: Types of Reasoning Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews 1. Deductive reasoning: 2. Inductive Reasoning: 3. Abductive reasoning: 4. Common Sense Reasoning 5. Monotonic Reasoning: Advantages of Monotonic Reasoning: Disadvantages of Monotonic Reasoning: 6. Non-monotonic Reasoning Advantages of Non-monotonic reasoning: Disadvantages of Non-monotonic Reasoning: Note: Inductive and deductive reasoning are the forms of propositional logic. Contact info Follow us Tutorials Interview Questions Online Compiler In previous topics, we have learned various ways of knowledge representation in artificial intelligence. Now we will learn the various ways to reason on this knowledge using different logical schemes. The reasoning is the mental process of deriving logical conclusion and making predictions from available knowledge, facts, and beliefs. Or we can say, "Reasoning is a way to infer facts from existing data." It is a general process of thinking rationally, to find valid conclusions. In artificial intelligence, the reasoning is essential so that the machine can also think rationally as a human brain, and can perform like a human. In artificial intelligence, reasoning can be divided into the following categories: Deductive reasoning is deducing new information from logically related known information. It is the form of valid reasoning, which means the argument's conclusion must be true when the premises are true. Deductive reasoning is a type of propositional logic in AI, and it requires various rules and facts. It is sometimes referred to as top-down reasoning, and contradictory to inductive reasoning. In deductive reasoning, the truth of the premises guarantees the truth of the conclusion. Deductive reasoning mostly starts from the general premises to the specific conclusion, which can be explained as below example. Example: Premise-1: All the human eats veggies Premise-2: Suresh is human. Conclusion: Suresh eats veggies. The general process of deductive reasoning is given below: Inductive reasoning is a form of reasoning to arrive at a conclusion using limited sets of facts by the process of generalization. It starts with the series of specific facts or data and reaches to a general statement or conclusion. Inductive reasoning is a type of propositional logic, which is also known as cause-effect reasoning or bottom-up reasoning. In inductive reasoning, we use historical data or various premises to generate a generic rule, for which premises support the conclusion. In inductive reasoning, premises provide probable supports to the conclusion, so the truth of premises does not guarantee the truth of the conclusion. Example: Premise: All of the pigeons we have seen in the zoo are white. Conclusion: Therefore, we can expect all the pigeons to be white. Abductive reasoning is a form of logical reasoning which starts with single or multiple observations then seeks to find the most likely explanation or conclusion for the observation. Abductive reasoning is an extension of deductive reasoning, but in abductive reasoning, the premises do not guarantee the conclusion. Example: Implication: Cricket ground is wet if it is raining Axiom: Cricket ground is wet. Conclusion It is raining. Common sense reasoning is an informal form of reasoning, which can be gained through experiences. Common Sense reasoning simulates the human ability to make presumptions about events which occurs on every day. It relies on good judgment rather than exact logic and operates on heuristic knowledge and heuristic rules. Example: The above two statements are the examples of common sense reasoning which a human mind can easily understand and assume. In monotonic reasoning, once the conclusion is taken, then it will remain the same even if we add some other information to existing information in our knowledge base. In monotonic reasoning, adding knowledge does not decrease the set of prepositions that can be derived. To solve monotonic problems, we can derive the valid conclusion from the available facts only, and it will not be affected by new facts. Monotonic reasoning is not useful for the real-time systems, as in real time, facts get changed, so we cannot use monotonic reasoning. Monotonic reasoning is used in conventional reasoning systems, and a logic-based system is monotonic. Any theorem proving is an example of monotonic reasoning. Example: It is a true fact, and it cannot be changed even if we add another sentence in knowledge base like, "The moon revolves around the earth" Or "Earth is not round," etc. In Non-monotonic reasoning, some conclusions may be invalidated if we add some more information to our knowledge base. Logic will be said as non-monotonic if some conclusions can be invalidated by adding more knowledge into our knowledge base. Non-monotonic reasoning deals with incomplete and uncertain models. "Human perceptions for various things in daily life, "is a general example of non-monotonic reasoning. Example: Let suppose the knowledge base contains the following knowledge: So from the above sentences, we can conclude that Pitty can fly. However, if we add one another sentence into knowledge base "Pitty is a penguin", which concludes "Pitty cannot fly", so it invalidates the above conclusion. 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Knowledge Representation

Difference between Inductive and Deductive Reasoning - Javatpoint

Difference between Inductive and Deductive reasoning Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Reasoning in artificial intelligence has two important forms, Inductive reasoning, and Deductive reasoning. Both reasoning forms have premises and conclusions, but both reasoning are contradictory to each other. Following is a list for comparison between inductive and deductive reasoning: The differences between inductive and deductive can be explained using the below diagram on the basis of arguments: Comparison Chart: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Uncertain Knowledge Representation

Probabilistic Reasoning in Artificial Intelligence - Javatpoint

Probabilistic reasoning in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Uncertainty: Causes of uncertainty: Probabilistic reasoning: Conditional probability: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: We will learn the above two rules in later chapters. Contact info Follow us Tutorials Interview Questions Online Compiler Till now, we have learned knowledge representation using first-order logic and propositional logic with certainty, which means we were sure about the predicates. With this knowledge representation, we might write A→B, which means if A is true then B is true, but consider a situation where we are not sure about whether A is true or not then we cannot express this statement, this situation is called uncertainty. So to represent uncertain knowledge, where we are not sure about the predicates, we need uncertain reasoning or probabilistic reasoning. Following are some leading causes of uncertainty to occur in the real world. Probabilistic reasoning is a way of knowledge representation where we apply the concept of probability to indicate the uncertainty in knowledge. In probabilistic reasoning, we combine probability theory with logic to handle the uncertainty. We use probability in probabilistic reasoning because it provides a way to handle the uncertainty that is the result of someone's laziness and ignorance. In the real world, there are lots of scenarios, where the certainty of something is not confirmed, such as "It will rain today," "behavior of someone for some situations," "A match between two teams or two players." These are probable sentences for which we can assume that it will happen but not sure about it, so here we use probabilistic reasoning. Need of probabilistic reasoning in AI: In probabilistic reasoning, there are two ways to solve problems with uncertain knowledge: As probabilistic reasoning uses probability and related terms, so before understanding probabilistic reasoning, let's understand some common terms: Probability: Probability can be defined as a chance that an uncertain event will occur. It is the numerical measure of the likelihood that an event will occur. The value of probability always remains between 0 and 1 that represent ideal uncertainties. We can find the probability of an uncertain event by using the below formula. Event: Each possible outcome of a variable is called an event. Sample space: The collection of all possible events is called sample space. Random variables: Random variables are used to represent the events and objects in the real world. Prior probability: The prior probability of an event is probability computed before observing new information. Posterior Probability: The probability that is calculated after all evidence or information has taken into account. It is a combination of prior probability and new information. Conditional probability is a probability of occurring an event when another event has already happened. Let's suppose, we want to calculate the event A when event B has already occurred, "the probability of A under the conditions of B", it can be written as: Where P(A⋀B)= Joint probability of a and B P(B)= Marginal probability of B. If the probability of A is given and we need to find the probability of B, then it will be given as: It can be explained by using the below Venn diagram, where B is occurred event, so sample space will be reduced to set B, and now we can only calculate event A when event B is already occurred by dividing the probability of P(A⋀B) by P( B ). Example: In a class, there are 70% of the students who like English and 40% of the students who likes English and mathematics, and then what is the percent of students those who like English also like mathematics? Solution: Let, A is an event that a student likes Mathematics B is an event that a student likes English. Hence, 57% are the students who like English also like Mathematics. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Uncertain Knowledge Representation

Bayes theorem in Artificial Intelligence - Javatpoint

Bayes' theorem in Artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Bayes' theorem: Applying Bayes' rule: Application of Bayes' theorem in Artificial intelligence: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Bayes' theorem is also known as Bayes' rule, Bayes' law, or Bayesian reasoning, which determines the probability of an event with uncertain knowledge. In probability theory, it relates the conditional probability and marginal probabilities of two random events. Bayes' theorem was named after the British mathematician Thomas Bayes. The Bayesian inference is an application of Bayes' theorem, which is fundamental to Bayesian statistics. It is a way to calculate the value of P(B|A) with the knowledge of P(A|B). Bayes' theorem allows updating the probability prediction of an event by observing new information of the real world. Example: If cancer corresponds to one's age then by using Bayes' theorem, we can determine the probability of cancer more accurately with the help of age. Bayes' theorem can be derived using product rule and conditional probability of event A with known event B: As from product rule we can write: Similarly, the probability of event B with known event A: Equating right hand side of both the equations, we will get: The above equation (a) is called as Bayes' rule or Bayes' theorem. This equation is basic of most modern AI systems for probabilistic inference. It shows the simple relationship between joint and conditional probabilities. Here, P(A|B) is known as posterior, which we need to calculate, and it will be read as Probability of hypothesis A when we have occurred an evidence B. P(B|A) is called the likelihood, in which we consider that hypothesis is true, then we calculate the probability of evidence. P(A) is called the prior probability, probability of hypothesis before considering the evidence P(B) is called marginal probability, pure probability of an evidence. In the equation (a), in general, we can write P (B) = P(A)*P(B|Ai), hence the Bayes' rule can be written as: Where A1, A2, A3,........, An is a set of mutually exclusive and exhaustive events. Bayes' rule allows us to compute the single term P(B|A) in terms of P(A|B), P(B), and P(A). This is very useful in cases where we have a good probability of these three terms and want to determine the fourth one. Suppose we want to perceive the effect of some unknown cause, and want to compute that cause, then the Bayes' rule becomes: Example-1: Question: what is the probability that a patient has diseases meningitis with a stiff neck? Given Data: A doctor is aware that disease meningitis causes a patient to have a stiff neck, and it occurs 80% of the time. He is also aware of some more facts, which are given as follows: Let a be the proposition that patient has stiff neck and b be the proposition that patient has meningitis. , so we can calculate the following as: P(a|b) = 0.8 P(b) = 1/30000 P(a)= .02 Hence, we can assume that 1 patient out of 750 patients has meningitis disease with a stiff neck. Example-2: Question: From a standard deck of playing cards, a single card is drawn. The probability that the card is king is 4/52, then calculate posterior probability P(King|Face), which means the drawn face card is a king card. Solution: P(king): probability that the card is King= 4/52= 1/13 P(face): probability that a card is a face card= 3/13 P(Face|King): probability of face card when we assume it is a king = 1 Putting all values in equation (i) we will get: Following are some applications of Bayes' theorem: We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Uncertain Knowledge Representation

Bayesian Belief Network in Artificial Intelligence - Javatpoint

Bayesian Belief Network in artificial intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Joint probability distribution: Explanation of Bayesian network: Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Note: The Bayesian network graph does not contain any cyclic graph. Hence, it is known as a directed acyclic graph or DAG. Contact info Follow us Tutorials Interview Questions Online Compiler Bayesian belief network is key computer technology for dealing with probabilistic events and to solve a problem which has uncertainty. We can define a Bayesian network as: "A Bayesian network is a probabilistic graphical model which represents a set of variables and their conditional dependencies using a directed acyclic graph." It is also called a Bayes network, belief network, decision network, or Bayesian model. Bayesian networks are probabilistic, because these networks are built from a probability distribution, and also use probability theory for prediction and anomaly detection. Real world applications are probabilistic in nature, and to represent the relationship between multiple events, we need a Bayesian network. It can also be used in various tasks including prediction, anomaly detection, diagnostics, automated insight, reasoning, time series prediction, and decision making under uncertainty. Bayesian Network can be used for building models from data and experts opinions, and it consists of two parts: The generalized form of Bayesian network that represents and solve decision problems under uncertain knowledge is known as an Influence diagram. A Bayesian network graph is made up of nodes and Arcs (directed links), where: The Bayesian network has mainly two components: Each node in the Bayesian network has condition probability distribution P(Xi |Parent(Xi) ), which determines the effect of the parent on that node. Bayesian network is based on Joint probability distribution and conditional probability. So let's first understand the joint probability distribution: If we have variables x1, x2, x3,....., xn, then the probabilities of a different combination of x1, x2, x3.. xn, are known as Joint probability distribution. P[x1, x2, x3,....., xn], it can be written as the following way in terms of the joint probability distribution. = P[x1| x2, x3,....., xn]P[x2, x3,....., xn] = P[x1| x2, x3,....., xn]P[x2|x3,....., xn]....P[xn-1|xn]P[xn]. In general for each variable Xi, we can write the equation as: Let's understand the Bayesian network through an example by creating a directed acyclic graph: Example: Harry installed a new burglar alarm at his home to detect burglary. The alarm reliably responds at detecting a burglary but also responds for minor earthquakes. Harry has two neighbors David and Sophia, who have taken a responsibility to inform Harry at work when they hear the alarm. David always calls Harry when he hears the alarm, but sometimes he got confused with the phone ringing and calls at that time too. On the other hand, Sophia likes to listen to high music, so sometimes she misses to hear the alarm. Here we would like to compute the probability of Burglary Alarm. Problem: Calculate the probability that alarm has sounded, but there is neither a burglary, nor an earthquake occurred, and David and Sophia both called the Harry. Solution: List of all events occurring in this network: We can write the events of problem statement in the form of probability: P[D, S, A, B, E], can rewrite the above probability statement using joint probability distribution: P[D, S, A, B, E]= P[D | S, A, B, E]. P[S, A, B, E] =P[D | S, A, B, E]. P[S | A, B, E]. P[A, B, E] = P [D| A]. P [ S| A, B, E]. P[ A, B, E] = P[D | A]. P[ S | A]. P[A| B, E]. P[B, E] = P[D | A ]. P[S | A]. P[A| B, E]. P[B |E]. P[E] Let's take the observed probability for the Burglary and earthquake component: P(B= True) = 0.002, which is the probability of burglary. P(B= False)= 0.998, which is the probability of no burglary. P(E= True)= 0.001, which is the probability of a minor earthquake P(E= False)= 0.999, Which is the probability that an earthquake not occurred. We can provide the conditional probabilities as per the below tables: Conditional probability table for Alarm A: The Conditional probability of Alarm A depends on Burglar and earthquake: Conditional probability table for David Calls: The Conditional probability of David that he will call depends on the probability of Alarm. Conditional probability table for Sophia Calls: The Conditional probability of Sophia that she calls is depending on its Parent Node "Alarm." From the formula of joint distribution, we can write the problem statement in the form of probability distribution: P(S, D, A, ¬B, ¬E) = P (S|A) *P (D|A)*P (A|¬B ^ ¬E) *P (¬B) *P (¬E). = 0.75* 0.91* 0.001* 0.998*0.999 = 0.00068045. Hence, a Bayesian network can answer any query about the domain by using Joint distribution. The semantics of Bayesian Network: There are two ways to understand the semantics of the Bayesian network, which is given below: 1. To understand the network as the representation of the Joint probability distribution. It is helpful to understand how to construct the network. 2. To understand the network as an encoding of a collection of conditional independence statements. It is helpful in designing inference procedure. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Examples of AI (Artificial Intelligence) - Javatpoint

Examples of AI-Artificial Intelligence Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials What is AI-Artificial Intelligence? Examples of AI-Artificial Intelligence Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Learning Processes Reasoning Processes Self-Correction Processes 1. Google Maps and Ride-Hailing Applications 2. Face Detection and Recognition 3. Text Editors or Autocorrect 4. Chatbots 5. Online-Payments 6. Search and Recommendation Algorithms 7. Digital Assistants 8. Social Media 9. Healthcare 10. Gaming 11. Online Ads Network 12. Banking and Finance 13. Smart Home Devices 14. curity and Surveillance 15. Smart Keyboard Apps 16. Smart Speakers 17. E-Commerce 18. Smart Email Apps 19. Music and Media Streaming Service 20. Space Exploration Contact info Follow us Tutorials Interview Questions Online Compiler The term "Artificial Intelligence" refers to the simulation of human intelligence processes by machines, especially computer systems. It also includes Expert systems, voice recognition, machine vision, and natural language processing (NLP). AI programming focuses on three cognitive aspects, such as learning, reasoning, and self-correction. This part of AI programming is concerned with gathering data and creating rules for transforming it into useful information. The rules, which are also called algorithms, offer computing devices with step-by-step instructions for accomplishing a particular job. This part of AI programming is concerned with selecting the best algorithm to achieve the desired result. This part of AI programming aims to fine-tune algorithms regularly in order to ensure that they offer the most reliable results possible. Artificial Intelligence is an extensive field of computer science which focuses on developing intelligent machines capable of doing activities that would normally require human intelligence. While AI is a multidisciplinary science with numerous methodologies, advances in deep learning and machine learning create a paradigm shift in almost every aspect of technology. The following are the examples of AI-Artificial Intelligence: Let's discuss the above examples in detail. Travelling to a new destination does not require much thought any longer. Rather than relying on confusing address directions, we can now easily open our phone's map app and type in our destination. So how does the app know about the appropriate directions, best way, and even the presence of roadblocks and traffic jams? A few years ago, only GPS (satellite-based navigation) was used as a navigation guide. However, artificial intelligence (AI) now provides users with a much better experience in their unique surroundings. The app algorithm uses machine learning to recall the building's edges that are supplied into the system after the person has manually acknowledged them. This enables the map to provide simple visuals of buildings. Another feature is identifying and understanding handwritten house numbers, which assists travelers in finding the exact house they need. Their outline or handwritten label can also recognize locations that lack formal street signs. The application has been trained to recognize and understand traffic. As a result, it suggests the best way to avoid traffic congestion and bottlenecks. The AI-based algorithm also informs users about the precise distance and time it will take them to arrive at their destination. It has been trained to calculate this based on the traffic situations. Several ride-hailing applications have emerged as a result of the use of similar AI technology. So, whenever you need to book a cab via an app by putting your location on a map, this is how it works. Utilizing face ID for unlocking our phones and using virtual filters on our faces while taking pictures are two uses of AI that are presently essential for our day-by-day lives. Face recognition is used in the former, which means that every human face can be recognized. Face recognition is used in the above, which recognizes a particular face. How does it work? Intelligent machines often match-and some cases, even exceed human performance! - Human potential. Human babies begin to identifying facial features such as eyes, lips, nose, and face shapes. A face, though, is more than just that. A number of characteristics distinguish human faces. Smart machines are trained in order to recognize facial coordinates (x, y, w, and h; which form a square around the face as an area of interest), landmarks (nose, eyes, etc.), and alignment (geometric structures). This improves the human ability to identify faces by several factors. Face recognition is also used by government facilities or at the airport for monitoring, and security. When typing a document, there are inbuilt or downloadable auto-correcting tools for editors of spelling errors, readability, mistakes, and plagiarism based on their difficulty level. It should have taken a long time for us to master our language and become fluent in it. Artificially intelligent algorithms often used deep learning, machine learning, and natural language in order to detect inappropriate language use and recommend improvements. Linguists and computer scientists collaborate in teaching machines grammar in the same way that we learned it in school. Machines are fed large volumes of high-quality data that has been structured in a way that machines can understand. Thus, when we misspell a single comma, the editor will highlight it in red and offer suggestions. Answering a customer's inquiries can take a long time. The use of algorithms to train machines to meet customer needs through chatbots is an artificially intelligent solution to this problem. This allows machines to answer as well as take and track orders. We used Natural Language Processing (NLP) to train chatbots to impersonate customer service agents' conversational approaches. Advanced chatbots do not require complex input formats (such as yes/o questions). They are capable of responding to complex questions that necessitate comprehensive answers. They will appear to be a customer representative, in fact, another example of artificial intelligence (AI). If you give a negative rating to a response, the bot will figure out what went wrong and fix it the next time, ensuring that you get the best possible service. It can be a time-consuming errand to rush to the bank for any transaction. Good news! Artificial Intelligence is now being used by banks to support customers by simplifying the process of payment. Artificial intelligence has enabled you to deposit checks from the convenience of our own home. Since AI is capable of deciphering handwriting and making online cheque processing practicable. Artificial Intelligence can potentially be utilized to detect fraud by observing consumers' credit card spending patterns. For example, the algorithms are aware of what items User X purchases, when and where they are purchased, and in what price range they are purchased. If there is some suspicious behaviour that does not match the user's profile, then the system immediately signals user X. When we wish to listen to our favorite songs or watch our favorite movie or shop online, we have ever found that the things recommended to us perfectly match our interests? This is the beauty of artificial intelligence. These intelligent recommendation systems analyze our online activity and preferences to provide us with similar content. Continuous training allows us to have a customized experience. The data is obtained from the front-end, saved as big data, and analysed using machine learning and deep learning. Then it can predict your preferences and make suggestions to keep you amused without having to look for something else. Artificial intelligence can also be utilized to improve the user experience of a search engine. Generally, the answer we are searching for is found in the top search results. What cause this? Data is fed into a quality control algorithm to identify high-quality content from SEO-spammed, low-quality content. This aids in creating an ascending order of search results on the basis of the quality for the greatest user experience. Since search engines are made up of codes, natural language processing technology aids in understanding humans by these applications. In reality, they can predict what a person wants to ask by compiling top-ranked searches and guessing their questions when they begin to type. Machines are constantly being updated with new features such as image search and voice search. If we need to find out a song that is playing at a mall, all we have to do is hold the phone up to it, and a music-identifying app will tell us what it is within a few seconds. The machine will also offer you song details after searching through an extensive collection of tunes. When our hands are full, we often enlist the help of digital assistants to complete tasks on our behalf. We might ask the assistant to call our father while we are driving with a cup of tea in one hand. For instance, Siri would look at our contacts, recognize the word "father," and dial the number. Siri is an example of a lower-tier model which can only respond to voice commands and cannot deliver complex responses. The new digital assistant is fluent in human language and uses advanced NLP (Natural Language Processing) and ML (Machine Learning) techniques. They are capable of understanding complex command inputs and providing acceptable results. They have adaptive abilities which can examine preferences, habits, and schedules. It enables them to use prompts, schedules, and reminders to help us systemize, coordinate, and plan things. The advent of social media gave the world a new narrative with immense freedom of speech. Although, it brought certain social ills like cyberbullying, cybercrime, and abuse of language. Several social media apps are using AI to help solve these issues while also providing users with other enjoyable features. AI algorithms are much quicker than humans at detecting and removing hate speech-containing messages. It is made possible by their ability to recognize hostile terms, keywords, and symbols in a variety of languages. These have been entered into the system, which can also contribute neologisms to its dictionary. Deep learning's neural network architecture is a vital part of the process. Emojis have become the most common way to express a wide range of emotions. This digital language is also understood by AI technology because it can understand the meaning of a certain piece of text and guess the exact emoji. Social networking, a perfect example of artificial intelligence, may even figure out what kind of content a user likes and recommends similar content. Facial recognition is also used in social media profiles, assisting users in tagging their friends via automatic suggestions. Smart filters can recognize spam and undesirable messages and automatically filter them out. Users may also take advantage of smart answers. The social media sector could use artificial intelligence to detect mental health issues such as suicidal thoughts by analyzing the information published and consumed. This information can be shared with mental health professionals. Infervision is using artificial intelligence and deep learning to save lives. In China, where there are insufficient radiologists to keep up with the demand for checking 1.4 billion CT scans each year for early symptoms of lung cancer. Radiologists essential to review many scans every day, which isn't just dreary, yet human weariness can prompt errors. Infervision trained and instructed algorithms to expand the work of radiologists in order to permit them to diagnose cancer more proficiently and correctly. The inspiration and foundation for Google's DeepMind are Neuroscience, which aims to create a machine that can replicate the thinking processes in our own brains. While DeepMind has effectively beaten people at games, what are truly captivating are the opportunities for medical care applications. For example, lessening the time it takes to plan treatments and utilizing machines to help diagnose ailments. Artificial Intelligence has been an important part of the gaming industry in recent years. In reality, one of AI's most significant achievements is in the gaming industry. One of the most important achievements in the field of AI is DeepMind's AI-based AlphaGo software, which is famous for defeating Lee Sedol, the world champion in the game of GO. Shortly after the win, DeepMind released AlphaGo, which trounced its predecessor in an AI-AI face off. The advanced machine, AlphaGo Zero, taught itself to master the game, unlike the original AlphaGo, which DeepMind learned over time using a vast amount of data and supervision. Not at all like the first AlphaGo, which DeepMind prepared over the long run by utilizing a lot of information and oversight, the high-level framework, AlphaGo Zero instructed itself to dominate the game. Another example of Artificial Intelligence in gaming comprises the First Encounter Assault Recon, also known as F.E.A.R that is the first-person shooter video game. The online advertising industry is the most significant user of artificial intelligence that uses AI (Artificial Intelligence) to not only monitor user statistics but also to advertise us on the basis of the statistics. The online advertising industry will struggle if AI is not implemented, as users will be shown random advertisements that have no relation to their interests. Since AI has been so good at determining our preferences and serving us ads, the worldwide digital ad industry has crossed 250 billion US dollars, with the business projected to cross the 300 billion mark in 2019. So, the next time remembers that AI is changing your life while you browse the internet and encounter adverts or product recommendations. The banking and finance industry has a major impact on our daily lives which means the world runs on liquidity, and banks are the gatekeepers who control the flow. Did you know that artificial intelligence is heavily used in the banking and finance industry for things such as customer service, investment, fraud protection, and so on? The automatic emails we get from banks if we make an ordinary transaction, are a simple example. That's AI keeping an eye on our account and trying to alert us regarding any potential fraud. AI is now being trained to examine vast samples of fraud data in order to identify patterns so that we can be alerted before it happens to us. If we run into a snag and contact our bank's customer service, we are probably speaking with an AI bot. Even the largest financial industry use AI to analyse data in order to find the best ways to invest capital in order to maximize returns while minimizing risk. Not only that, but AI is set to play an even larger role in the industry, with major banks around the world investing billions of dollars in AI technology, and we will be able to see the results sooner rather than later. Another popular example of AI (Artificial Intelligence) is smart home devices. Artificial intelligence is even being welcomed into our homes. Most of the smart home gadgets we purchase use artificial intelligence to learn our habits and automatically change settings to make our experience as seamless as possible. We have effectively examined how we utilize savvy voice assistants to control these smart home gadgets. We probably are aware that it is a great example of AI's impact on our lives. That is to say, there are smart thermostats that change the temperature-dependent on our preferences, smart lights which change the colour and intensity of lights dependent on time, and much more. This will not happen when our primary interaction with all our smart home devices is only through AI. Although we all can debate about the ethics of using a large surveillance system, there's no denying that it's being used, and AI is playing a significant role in it. It isn't workable for people to keep monitoring many monitors simultaneously, and thus, utilizing AI makes well. With technologies such as facial recognition and object recognition improving every day, it won't be long when all the security camera deals with are being checked by an AI and not a human. Right now, before AI can be completely implemented, this is going to be our future. Smart keyboard apps are another example of AI (Artificial Intelligence). In all actuality, not every person loves managing on-screen keyboards. Although, they have become far more intuitive, permitting clients to type comfortably and quickly. What has likely ended up being a catalyst for them is the integration of AI. The smart keyboard applications keep a tab on the composing style of a client and predict words and emojis based on that. Consequently, typing on the touchscreen has gotten quicker and more advantageous. Not to mention that artificial intelligence is crucial in detecting misspellings and typos. Not in vain, many thinks that smart speakers are good to go for a major blast in technology. Besides controlling smart home gadgets, they are likewise capable of various things like sending fast messages, setting updates, checking the climate, and getting the most recent news. Also, it's this flexibility that is ending up being a conclusive factor for them. Driven by the hugely popular Amazon Echo series, the worldwide brilliant speaker market arrived at an exceptional high in 2019 with sales of 149.9 million units, which is a huge increment of 70% in 2018. Additionally, the sales in Q4 2019 also saw another record with an incredible 55.7 million units. Smart speakers are likely the most unmistakable instances of the utilization of AI in our reality. Artificial intelligence algorithms have given the necessary vital impulse to web-based businesses to give a more customized insight. According to many sources, its use has significantly improved sales and has also aided in developing long-term consumer relationships. Thus, organizations take advantage of AI to deploy chatbots to gather urgent information and predict purchases to make a client-centric experience. On the way across this shift of technique? Simply invest some time on websites such as Amazon, and eBay and we will soon see how quickly the scene around you is improving rapidly! In the event that you actually find your inbox cluttered with an excessive number of undesirable messages, the possibility is quite high that we can yet stay with an old-fashioned email application. Present-day email applications such as Spark make several AI to filter out spam messages and furthermore arrange messages so you can rapidly get to the significant ones. Likewise, it additionally provides smart answers dependent on the messages we get to help us answer to any email rapidly. The "Smart Reply" highlight of Gmail is an extraordinary illustration of this. It utilizes AI to filter the content of the email and gives you context-oriented answers. Another amazing illustration of how AI affects our lives is the music and media streaming features that we utilize reliably. Whether or not you are utilizing Spotify, Netflix, or YouTube, AI is making the decisions for you. All things considered as everything, once in a while is great and some of the time is awful. For instance, I enjoy Spotify's Discover Weekly playlist since it has acquainted me with a few new artists who I would not have known about if it weren't for Spotify's AI divine beings. Then again, I additionally remember going down the YouTube rabbit hole, wasting uncountable hours simply watching the suggested videos. That suggested videos section has become so great at knowing my taste that it's alarming. Thus, keep in mind that AI is at work whenever you are watching a suggested video on YouTube, viewing a suggested show on Netflix, listening to a pre-made playlist on Spotify, or using any other media and music streaming service. Space expeditions and discoveries consistently require investigating immense measures of information. Artificial Intelligence and Machine learning are the best approach for dealing with and measure information on this scale. After thorough astronomers, and research utilized Artificial Intelligence to filter through long periods of information got by the Kepler telescope to distinguish an inaccessible eight-planet solar system.' We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence Essay - Javatpoint

Examples of AI-Artificial Intelligence Essay Essay on Artificial Intelligence Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Advantages: Disadvantages: Contact info Follow us Tutorials Interview Questions Online Compiler In this topic, we are going to provide an essay on Artificial Intelligence. This long essay on Artificial Intelligence will cover more than 1000 words, including Introduction of AI, History of AI, Advantages and disadvantages, Types of AI, Applications of AI, Challenges with AI, and Conclusion. This long essay will be helpful for students and competitive exam aspirants. Artificial Intelligence is a combination of two words Artificial and Intelligence, which refers to man-made intelligence. Therefore, when machines are equipped with man-made intelligence to perform intelligent tasks similar to humans, it is known as Artificial Intelligence. It is all about developing intelligent machines that can simulate the human brain and work & behave like human beings. We can define AI as, "Artificial Intelligence is a branch of computer science that deals with developing intelligent machines which can behave like human, think like human, and has ability to take decisions by their own." With AI, machines can have human-based skills such as learning, reasoning, and solving logical problems. AI is one of the fastest-growing technology that is making human life much easier by providing solutions for complex problems. It has also brought different opportunities for everyone, and hence it is a very demanding technology in the market. Artificial intelligence is assumed a new technology, but in reality, it is not new. The researchers in the field of AI are much older. It is said that the concept of intelligent machines was found in Greek Mythology. Below are some keystones in the development of AI: 1. Narrow AI or Weak AI: Narrow AI or Weak AI is a basic kind of Artificial Intelligence, which is capable of completing dedicated tasks with intelligence. The current version of AI is narrow AI. Narrow AI can only perform the specific task and not beyond its limitation, as they are trained for one task only. It is programmed to do a specific task such as Play Chess, Checking Weather, etc. 2. General AI: Artificial General intelligence or "Strong" AI defines the machines that can show human intelligence. We can say, Machines with AGI can successfully perform any intellectual task that a human can do. This is the sort of AI that we see in movies like "Her" or other sci-fi movies in which humans interact with machines and operating systems that are conscious, sentient, and driven by emotion and self-awareness. Currently, this type of intelligence does not exist in the real world and only exist in researches and movies. However, researchers across the world are working to develop such machines, which is still a very difficult task. 3. Super AI Super AI refers to AI that is self-aware, with cognitive abilities that surpass that of humans. It is a level where machines are capable of doing any task that a human can do with cognitive properties. However, Super AI is still a hypothetical concept, and it is a challenging task to develop such AI-enabled machines. 1. Reactive Machines Reactive machines are the basic types of AI, which don't store memories or past experiences for their actions. These types of AI machines only focus on current scenarios and work as per the requirement with the best possible actions. IBM's Deep Blue is an example of a reactive machine. 2. Limited Memory Limited memory can store some memory or past experiences for a limited time period. Some examples of limited memory are Self-driving cars. 3. Theory of Mind Theory of Mind is the type of AI which are capable of understanding human emotions, and interact with the human in their way. However, such AI machines are yet not developed, and developers and researchers are making efforts for creating such AI-enabled machines. 4. Self-awareness Self-awareness AI is the future of Artificial Intelligence, which will have its own awareness, sentiments, and consciousness. This AI is only a hypothetical concept and will take a long journey and challenges to create such AI. 1. Game Playing: AI is widely used in Gaming. Different strategic games such as Chess, where the machine needs to think logically, and video games to provide real-time experiences use Artificial Intelligence. 2. Robotics: Artificial Intelligence is commonly used in the field of Robotics to develop intelligent robots. AI implemented robots use real-time updates to sense any obstacle in their path and can change the path instantly. AI robots can be used for carrying goods in hospitals and industries and can also be used for other different purposes. 3. Healthcare: In the healthcare sector, AI has diverse uses. In this field, AI can be used to detect diseases and cancer cells. It also helps in finding new drugs with the use of historical data and medical intelligence. 4. Computer Vision: Computer vision enables the computer system to understand and derive meaningful information from digital images, video, and other visual input with the help of AI. 5. Agriculture: AI is now widely used in Agriculture; for example, with the help of AI, we can easily identify defects and nutrient absences in the soil. To identify these defects, AI robots can be utilized. AI bots can also be used in crop harvesting at a higher speed than human workers. 6. E-commerce AI is one of the widely used and demanding technologies in the E-commerce industry. With AI, e-commerce businesses are gaining more profit and grow in business by recommending products as per the user requirement. 7. Social Media Different social media websites such as Facebook, Instagram, Twitter, etc., use AI to make the user experiences much better by providing different features. For example, Twitter uses AI to recommend tweets as per the user interest and search history. As a beginner, below are some of the prerequisites that will help to get started with AI technology. One of the big challenges with AI is that we don't have enough data to work with AI systems, or data we have is of poor quality or unstructured. AI depends on data for its working and requires a huge amount of data for a good result, but in the real world, data is available either in raw form or unstructured form that contains lots of impurities and missing values that cannot be processed or analyzed. Hence the processing of such data is a big task for organizations, and it takes lots of effort and is a time-consuming process. There is still a lack of IT infrastructures, mainly in start-ups, which is a big issue in AI researches and development. AI is growing continuously day by day with rapid speed, and more people are accepting the proven ideas of AI. The growing rate of AI also needs developers of AI tech. However, the professionals with full scales skills to develop high-level AI implementations are still lacking, which is also one of the big challenges with AI. Computing power has always been a big issue in the IT industry, but day by day, this issue has been resolved. However, with the development of AI, this issue has arisen again. Deep learning and the processing of neural networks, which are part of AI, require a high level of computing power, and are a major challenge for the tech industries. Mainly for start-ups, collecting money and such high computing power to process the data is a big deal. One of the latest challenges with AI is that now organizations need to be wary of AI. The legal issues are raised for concern that if AI collects sensitive data, that may be a violation of federal laws. Although it is not illegal, industries need to be careful of any supposed impact that might negatively affect their organization. Artificial Intelligence is undoubtedly a trending and emerging technology. It is growing very fast day by day, and it is enabling machines to mimic the human brain. Due to its high performance and as it is making human life easier, it is becoming a highly demanded technology among industries. However, there are also some challenges and problems with AI. Many people around the world are still thinking of it as a risky technology, because they feel that if it overtakes humans, it will be dangerous for humanity, as shown in various sci-fi movies. However, the day-to-day development of AI is making it a comfortable technology, and people are connecting with it more. Therefore, we can conclude that it is a great technology, but each technique must be used in a limited way in order to be used effectively, without any harm. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Healthcare - Javatpoint

Artificial Intelligence in Healthcare Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Introduction to Artificial Intelligence AI in Healthcare AI technologies used in healthcare AI-based healthcare system vs. Traditional healthcare system Roles of Artificial Intelligence (AI) in healthcare Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews AI-based Healthcare system: Traditional Healthcare System: Contact info Follow us Tutorials Interview Questions Online Compiler Artificial Intelligence (AI) is transforming industries around the world, and currently, its application is rapidly increasing in the healthcare sector. AI in healthcare describes the use of AI or machine-learning algorithms to mimic human cognition for gathering and understanding complex medical and health care data.AI does this by various Machine Learning algorithms, Computer Vision, Natural Language Processing, Robotics, and Deep Learning. These algorithms recognize a pattern in behaviour and then create their own logic to give well-defined output to end-users. Machine Learning helps to gain important insights and predictions using extensive amounts of input data. Further, they also instruct experts on how to build companions for expensive clinical preliminaries. In this topic, we are going to discuss the impact of Artificial Intelligence on the healthcare sector. But before starting, let's first understand the brief introduction of AI. Artificial Intelligence (AI) is defined as a branch of computer science that aims to enable computer systems to perform various tasks with intelligence similar to humans. It is also an ability of computers or machines to display intellectual processes and characteristics of humans such as reasoning, generalizing and learning from past experience, etc. Artificial Intelligence in Healthcare is used to analyze the treatment techniques of various diseases and to prevent them. AI is used in various areas of healthcare such as diagnosis processes, drug research sector, medicine, patient monitoring care centre, etc. In the healthcare industry, AI helps to gather past data through electronic health records for disease prevention and diagnosis. There are various medical institutes that have developed their own AI algorithms for their department, such as Memorial Sloan Kettering Cancer Center and The Mayo clinic, etc. Further, IBM and Google have also developed AI algorithms for the healthcare industry that help to support operational initiatives that increase cost-saving, improve patient satisfaction, and satisfy their staffing and workforce needs. Artificial Intelligence uses various technologies or algorithms in healthcare industries, and these are as follows: AI helps to predict and analyze data through electronic health records for disease prevention, diagnosis, and treatment of diseases, illness and other physical and mental impairments in human beings. Nowadays, AI is a widely used technology worldwide, which plays a very crucial role in each sector, such as gaming, banking, agriculture, etc. AI also plays a very important role in the healthcare sector, such as deceases prediction and prevention, Drug research and manufacturing, deceases treatments, surgery and patient monitoring, etc. Artificial Intelligence helps to analyze and predict the type of deceases, and it's a method of prevention based on gathering past data through electronic health records for disease prevention and diagnosis and later used in various decease prediction and their treatment. However, AI also gathers this data from the traditional approach of doctors, such as X-Ray. Further, AI uses robotics technology in the research and manufacturing of drugs and surgery. Current Research of AI in healthcare: AI has developed exponential growth in the research industry. The government of the United States of America is estimated to invest more than $2 billion in AI-related to healthcare sectors like Dermatology, Radiology, Screening, Psychiatry and Drug Interactions, etc., over the next five years. In the healthcare sector, Artificial Intelligence helps to decrease medication costs with a more accurate diagnosis, better prediction and treatment of diseases. The researchers are also working on an AI project that can be a boon for humans in the upcoming years. The brain-computer interface can help patients who are physically disabled or suffering spinal cord injury as well. Hence, the Healthcare industry is fully ripe for some major changes. From chronic disease and cancer to radiology and risk assessment, it can be deployed with new AI-based technologies with more precise, efficient, and cost-efficient inventions. The Healthcare industry is treated as a complicated science bound by legal, ethical, economical and social constraints and can be implemented with AI by making parallel changes in the environment. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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Artificial Intelligence in Education - Javatpoint

Artificial Intelligence in Education Artificial Intelligence Intelligent Agent Problem-solving Adversarial Search Knowledge Represent Uncertain Knowledge R. Misc Subsets of AI Artificial Intelligence MCQ Related Tutorials Overview Of AIED(Artificial Intelligence in Education) Applications/roles of Artificial Intelligence in Education Benefits of AI For Students Future of AI in Education Conclusion Latest Courses Python AI, ML and Data Science Java B.Tech and MCA Web Technology Software Testing Technical Interview Java Interview Web Interview Database Interview Company Interviews Contact info Follow us Tutorials Interview Questions Online Compiler Education is an important part of life for everyone, and a good education plays a vital role to have a successful life. In order to improve the education system for the students, there are always a lot of changes happening around the world, ranging from the way of teaching to the type of curriculum. Artificial Intelligence is a thriving technology that is being used in almost every field and is changing the world. One place where artificial intelligence is poised to make big changes is (and in some cases already is) in education Artificial Intelligence in Education is developing new solutions for teaching and learning for different situations. Nowadays, AI is being used by different schools and colleges across different countries. AI in education has given a completely new perspective of looking at education to teachers, students, parents, and of course, the educational institutions as well. AI in education is not about humanoid robots as a teacher to replace human teachers, but it is about using computer intelligence to help teachers and students and making the education system much better and effective. In future, the education system will have lots of AI tools that will shape the educational experience of the future. In this topic, we will discuss the impact and application of Artificial Intelligence on Education. To better understand this topic, let's first understand what AIED is? Artificial Intelligence (AI) is a simulation of human intelligence into a computer machine so that it can think and act like a human. It is a technology that helps a computer machine to think like a human. Artificial Intelligence aims to mimic human behaviour. AI has various uses and applications in different sectors, including education. In the 1970s, AIED has occurred as a specialist area to cover new technology to teaching & learning, specifically for higher education. The main aim of AIED is to facilitate the learners with flexible, personalized, and engaging learning along with the basic automated task. Some popular trends in AIED include Intelligent tutor systems, smart classroom technologies, adaptive learning, and pedagogical agents. Below diagram shows the relationship between all these trends: As per the researches, in the near future, AI in education will step in three main ways, which are: Artificial intelligence and its uses in our lives are growing day by day in many segments. In the field of education, AI has started showing its influences and working as a helping tool for both the students and teachers and supporting the learning process. But still, the use of AI in education is not adapted by all the colleges completely, and it will take a long journey to do this. However, studies show that in the near future, AI will have a good impact on the education sector. It is currently transforming the education industry but is yet to show its real potential in education. Further, learning from computer systems can be much helpful, but it is unlikely to fully replacing human teaching in schools and colleges. We provides tutorials and interview questions of all technology like java tutorial, android, java frameworks G-13, 2nd Floor, Sec-3, Noida, UP, 201301, India [email protected]. Latest Post PRIVACY POLICY

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