000			05719 a2200337 4500
999			_c34441 _d34441
001			nam a22 7a 4500
020			_a9781292153964
082			_a006.3 RU AR
100			_aRussell, Stuart J. _920415
245			_aArtificial intelligence : _ba modern approach _cStuart J. Russell and Peter Norvig
250			_a3rd ed.
260			_aEngland : _bPearson Education Limited, _cc2016.
300			_axviii, 1132 p. : _bill. ; _c26 cm.
490			_aPrentice Hall series in artificial intelligence
505			_aArtificial 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 Problem-Solving: Solving problems by searching: Problem-solving agents Example problems Searching for solutions Uniformed search strategies Informed (heuristic) search strategies Heuristic functions 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 Adversarial search: Games Optimal decisions in games Alpha-beta pruning Imperfect real-time decisions Stochastic games Partially observable games State-of-the-art game programs Alternative approaches Constraint satisfaction problems: Defining constraint satisfaction problems Constraint propagation: inference in CSPs Backtracking search for CSPs Local search for CSPs Structure of problems Knowledge, Reasoning, And Planning: Logical agents: Knowledge-based agents Wumpus world Logic Propositional logic: a very simple logic Propositional theorem proving Effective propositional model checking Agents based on propositional logic First-order logic: Representation revisited Syntax and semantics of first-order logic Using first-order logic Knowledge engineering in first-order logic Decisions with multiple agents: game theory Mechanism design 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 Knowledge in learning: Logical formulation of learning Knowledge in learning Explanation-based learning Learning using relevance information Inductive logic programming Learning probabilistic models: Statistical learning Learning with complete data Learning with hidden variables: the EM algorithm Reinforcement learning: Introduction Passive reinforcement learning Active reinforcement learning Generalization in reinforcement learning Policy search Applications of reinforcement learning Communicating, Perceiving, And Acting: Natural language processing: Language models Text classification Information retrieval Information extraction Natural language for communication: Phrase structure grammars Syntactic analysis (parsing) Augmented grammars and semantic interpretation Machine translation Speech recognition Perception: Image formation Early image-processing operations Object recognition by appearance Reconstructing the 3D world Object recognition for structural information Using vision Robotics: Robot hardware Robotic perception Planning to move Planning uncertain movements Moving Robotic software architectures Application domains Conclusions: Philosophical foundations: Weak AI: can machines act intelligently? Strong AI: can machines really think? Ethics and risks of developing artificial intelligence 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 Backus-Naur Form (BNF) Describing algorithms with pseudocode Online help Bibliography Index.
520			_aIn this third edition, the authors have updated the treatment of all major areas. A new organizing principle--the representational dimension of atomic, factored, and structured models--has been added. Significant new material has been provided in areas such as partially observable search, contingency planning, hierarchical planning, relational and first-order 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.
526			_aCSCI323
650			_aArtificial intelligence _9370
700			_aNorvig, Peter _920416
700			_aDavis, Earnest, _eContributing Writer _951693
700			_aEdwards, Douglas D., _eContributing Writer _951694
700			_aForsyth, David, _eContributing Writer _939160
700			_aHay, Nicholas J., _eContributing Writer _951695
700			_aMalik, Jitendra M., _eContributing Writer _951696
700			_aMittal, Vibhu, _eContributing Writer _951697
700			_aSahami, Mehran, _eContributing Writer _951698
700			_aThrun, Sebastian, _eContributing Writer _951699
856			_uhttps://uow.primo.exlibrisgroup.com/permalink/61UOW_INST/otb3u8/cdi_askewsholts_vlebooks_9781292153971 _zEbook
856			_uhttps://uowd.box.com/s/5tfcyofz1iagzl63whqgic1sfxmdzuij _zLocation Map
942			_c3DAY