Artificial intelligence : a modern approach / Stuart Russell, Peter Norvig.
By: Russell, Stuart J. (Stuart Jonathan).
Contributor(s): Norvig, Peter.
Material type: BookCall no.: Q335 .R86 2014Publication: Harlow : Pearson, 2014Edition: 3rd ed., Pearson new international ed.Description: ii, 1091 p. : ill.ISBN: 1292024208 (pbk.); 9781292024202 (pbk.).Subject(s): Artificial intelligenceItem type  Current location  Collection  Call number  Status  Date due  Barcode  Item holds 

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Includes bibliographical references and index.
Artificial Intelligence:  Introduction:  What is AI?  Foundations of artificial intelligence  History of artificial intelligence  State of the art  Summary, bibliographical and historical notes, exercises  Intelligent agents:  Agents and environments  Good behavior: concept of rationality  Nature of environments  Structure of agents  Summary, bibliographical and historical notes, exercises  ProblemSolving:  Solving problems by searching:  Problemsolving agents  Example problems  Searching for solutions  Uniformed search strategies  Informed (heuristic) search strategies  Heuristic functions  Summary, bibliographical and historical notes, exercises  Beyond classical search:  Local search algorithms and optimization problems  Local search in continuous spaces  Searching with nondeterministic actions  Searching with partial observations  Online search agents and unknown environments  Summary, bibliographical and historical notes, exercises  Adversarial search:  Games  Optimal decisions in games  Alphabeta pruning  Imperfect realtime decisions  Stochastic games  Partially observable games  Stateoftheart game programs  Alternative approaches  Summary, bibliographical and historical notes, exercises  Constraint satisfaction problems:  Defining constraint satisfaction problems  Constraint propagation: inference in CSPs  Backtracking search for CSPs  Local search for CSPs  Structure of problems  Summary, bibliographical and historical notes, exercises  Knowledge, Reasoning, And Planning:  Logical agents:  Knowledgebased agents  Wumpus world  Logic  Propositional logic: a very simple logic  Propositional theorem proving  Effective propositional model checking  Agents based on propositional logic  Summary, bibliographical and historical notes, exercises  Firstorder logic:  Representation revisited  Syntax and semantics of firstorder logic  Using firstorder logic  Knowledge engineering in firstorder logic  Summary, bibliographical and historical notes, exercises  Inference in firstorder logic:  Propositional vs firstorder inference  Unification and lifting  Forward chaining  Backward chaining  Resolution  Summary, bibliographical and historical notes, exercises  Classical planning:  Definition of classical planning  Algorithms for planning as statespace search  Planning graphs  Other classical planning approaches  Analysis of planning approaches  Summary, bibliographical and historical notes, exercises  Planning and acting in the real world:  Time, schedules, and resources  Hierarchical planning  Planning and acting in nondeterministic domains  Multiagent planning  Summary, bibliographical and historical notes, exercises  Knowledge representation:  Ontological engineering  Categories and objects  Events  Mental events and mental objects  Reasoning systems for categories  Reasoning with default information  Internet shopping world  Summary, bibliographical and historical notes, exercises.
Uncertain Knowledge And Reasoning:  Quantifying uncertainty:  Acting under uncertainty  Basic probability notation  Inference using full joint distributions  Independence  Bayes' rule and its use  Wumpus world revisited  Summary, bibliographical and historical notes, exercises  Probabilistic reasoning:  Representing knowledge in an uncertain domain  Semantics of Bayesian networks  Efficient representation of conditional distributions  Exact inference in Bayesian networks  Approximate inference in Bayesian networks  Relational and firstorder probability models  Other approaches to uncertain reasoning  Summary, bibliographical and historical notes, exercises  Probabilistic reasoning over time:  Time an uncertainty  Inference in temporal models  Hidden markov models  Kalman filters  Dynamic Bayesian networks  Keeping track of many objects  Summary, bibliographical and historical notes, exercises  Making simple decisions:  Combining beliefs and desires under uncertainty  Basis of utility theory  Utility functions  Multiattribute utility functions  Decision networks  Value of information  Decisiontheoretic expert systems  Summary, bibliographical and historical notes, exercises  Making complex decisions:  Sequential decision problems  Value iteration  Policy iteration  Partially observable MDPs  Decisions with multiple agents: game theory  Mechanism design  Summary, bibliographical and historical notes, exercises  Learning:  Learning from examples:  Forms of learning  Supervised learning  Learning decision trees  Evaluating and choosing the best hypothesis  Theory of learning  Regression and classification with linear models  Artificial neural networks  Nonparametric models  Support vector machines  Ensemble learning  Practical machine learning  Summary, bibliographical and historical notes, exercises  Knowledge in learning:  Logical formulation of learning  Knowledge in learning  Explanationbased learning  Learning using relevance information  Inductive logic programming  Summary, bibliographical and historical notes, exercises  Learning probabilistic models:  Statistical learning  Learning with complete data  Learning with hidden variables: the EM algorithm  Summary, bibliographical and historical notes, exercises  Reinforcement learning:  Introduction  Passive reinforcement learning  Active reinforcement learning  Generalization in reinforcement learning  Policy search  Applications of reinforcement learning  Summary, bibliographical and historical notes, exercises  Communicating, Perceiving, And Acting:  Natural language processing:  Language models  Text classification  Information retrieval  Information extraction  Summary, bibliographical and historical notes, exercises  Natural language for communication:  Phrase structure grammars  Syntactic analysis (parsing)  Augmented grammars and semantic interpretation  Machine translation  Speech recognition  Summary, bibliographical and historical notes, exercises  Perception:  Image formation  Early imageprocessing operations  Object recognition by appearance  Reconstructing the 3D world  Object recognition for structural information  Using vision  Summary, bibliographical and historical notes, exercises  Robotics:  Introduction  Robot hardware  Robotic perception  Planning to move  Planning uncertain movements  Moving  Robotic software architectures  Application domains  Summary, bibliographical and historical notes, exercises  Conclusions:  Philosophical foundations:  Weak AI: can machines act intelligently?  Strong AI: can machines really think?  Ethics and risks of developing artificial intelligence  Summary, bibliographical and historical notes, exercises  AI: the present and future:  Agent components  Agent architectures  Are we going in the right direction?  What if AI does succeed?  Mathematical background:  Complexity analysis and O() notation  Vectors, matrices, and linear algebra  Probability distribution  Notes on languages and algorithms:  Defining languages with BackusNaur Form (BNF)  Describing algorithms with pseudocode  Online help  Bibliography  Index.
In this third edition, the authors have updated the treatment of all major areas. A new organizing principlethe representational dimension of atomic, factored, and structured modelshas been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and firstorder probability models, regularization and loss functions in machine learning, kernel methods, Web search engines, information extraction, and learning in vision and robotics. The book also includes hundreds of new exercises.
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