Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, uptodate introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or twosemester, undergraduate or graduatelevel courses in Artificial Intelligence. Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence. According to an article in The New York Times, the course on artificial intelligence is "one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world." One of the other two courses, an introduction to database software, is being taught by Pearson authorDr. Jennifer Widom. Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle(t), NOOK(t), and the iPhone(R)/iPad(R). To learn more about the course on artificial intelligence, visit http://www.aiclass.com. To read the full New York Timesarticle, click here.
ISBN  9780132071482   Pages  1152  ISBN13  9780132071482 (What's this?)   Weight (grammes)  1860  Publisher  Pearson Education (US)   Published in  US  Imprint  Pearson   Previous ISBN  9780130803023  Format  Paperback   Height (mm)  254  Publication date  01 Apr 2010   Width (mm)  201  DEWEY  006.3   Spine width (mm)  51  DEWEY edition  DC22   Academic level  Tertiary education 



I Artificial Intelligence 1 Introduction 1.1 What is AI? ... 1 1.2 The Foundations of Artificial Intelligence ... 5 1.3 The History of Artificial Intelligence ... 16 1.4 The State of the Art ... 28 1.5 Summary, Bibliographical and Historical Notes, Exercises ... 29 2 Intelligent Agents 2.1 Agents and Environments ... 34 2.2 Good Behavior: The Concept of Rationality ... 36 2.3 The Nature of Environments ... 40 2.4 The Structure of Agents ... 46 2.5 Summary, Bibliographical and Historical Notes, Exercises ... 59 II Problemsolving 3 Solving Problems by Searching 3.1 ProblemSolving Agents ... 64 3.2 Example Problems ... 69 3.3 Searching for Solutions ... 75 3.4 Uninformed Search Strategies ... 81 3.5 Informed (Heuristic) Search Strategies ... 92 3.6 Heuristic Functions ... 102 3.7 Summary, Bibliographical and Historical Notes, Exercises ... 108 4 Beyond Classical Search 4.1 Local Search Algorithms and Optimization Problems ... 120 4.2 Local Search in Continuous Spaces ... 129 4.3 Searching with Nondeterministic Actions ... 133 4.4 Searching with Partial Observations ... 138 4.5 Online Search Agents and Unknown Environments ... 147 4.6 Summary, Bibliographical and Historical Notes, Exercises ... 153 5 Adversarial Search 5.1 Games ... 161 5.2 Optimal Decisions in Games ... 163 5.3 AlphaBeta Pruning ... 167 5.4 Imperfect RealTime Decisions ... 171 5.5 Stochastic Games ... 177 5.6 Partially Observable Games ... 180 5.7 StateoftheArt Game Programs ... 185 5.8 Alternative Approaches ... 187 5.9 Summary, Bibliographical and Historical Notes, Exercises ... 189 6 Constraint Satisfaction Problems 6.1 Defining Constraint Satisfaction Problems ... 202 6.2 Constraint Propagation: Inference in CSPs ... 208 6.3 Backtracking Search for CSPs ... 214 6.4 Local Search for CSPs ... 220 6.5 The Structure of Problems ... 222 6.6 Summary, Bibliographical and Historical Notes, Exercises ... 227 III Knowledge, Reasoning, and Planning 7 Logical Agents 7.1 KnowledgeBased Agents ... 235 7.2 The Wumpus World ... 236 7.3 Logic ... 240 7.4 Propositional Logic: A Very Simple Logic ... 243 7.5 Propositional Theorem Proving ... 249 7.6 Effective Propositional Model Checking ... 259 7.7 Agents Based on Propositional Logic ... 265 7.8 Summary, Bibliographical and Historical Notes, Exercises ... 274 8 FirstOrder Logic 8.1 Representation Revisited ... 285 8.2 Syntax and Semantics of FirstOrder Logic ... 290 8.3 Using FirstOrder Logic ... 300 8.4 Knowledge Engineering in FirstOrder Logic ... 307 8.5 Summary, Bibliographical and Historical Notes, Exercises ... 313 9 Inference in FirstOrder Logic 9.1 Propositional vs. FirstOrder Inference ... 322 9.2 Unification and Lifting ... 325 9.3 Forward Chaining ... 330 9.4 Backward Chaining ... 337 9.5 Resolution ... 345 9.6 Summary, Bibliographical and Historical Notes, Exercises ... 357 10 Classical Planning 10.1 Definition of Classical Planning ... 366 10.2 Algorithms for Planning as StateSpace Search ... 373 10.3 Planning Graphs ... 379 10.4 Other Classical Planning Approaches ... 387 10.5 Analysis of Planning Approaches ... 392 10.6 Summary, Bibliographical and Historical Notes, Exercises ... 393 11 Planning and Acting in the Real World 11.1 Time, Schedules, and Resources ... 401 11.2 Hierarchical Planning ... 406 11.3 Planning and Acting in Nondeterministic Domains ... 415 11.4 Multiagent Planning ... 425 11.5 Summary, Bibliographical and Historical Notes, Exercises ... 430 12 Knowledge Representation 12.1 Ontological Engineering ... 437 12.2 Categories and Objects ... 440 12.3 Events ... 446 12.4 Mental Events and Mental Objects ... 450 12.5 Reasoning Systems for Categories ... 453 12.6 Reasoning with Default Information ... 458 12.7 The Internet Shopping World ... 462 12.8 Summary, Bibliographical and Historical Notes, Exercises ... 467 IV Uncertain Knowledge and Reasoning 13 Quantifying Uncertainty 13.1 Acting under Uncertainty ... 480 13.2 Basic Probability Notation ... 483 13.3 Inference Using Full Joint Distributions ... 490 13.4 Independence ... 494 13.5 Bayes' Rule and Its Use ... 495 13.6 The Wumpus World Revisited ... 499 13.7 Summary, Bibliographical and Historical Notes, Exercises ... 503 14 Probabilistic Reasoning 14.1 Representing Knowledge in an Uncertain Domain ... 510 14.2 The Semantics of Bayesian Networks ... 513 14.3 Efficient Representation of Conditional Distributions ... 518 14.4 Exact Inference in Bayesian Networks ... 522 14.5 Approximate Inference in Bayesian Networks ... 530 14.6 Relational and FirstOrder Probability Models ... 539 14.7 Other Approaches to Uncertain Reasoning ... 546 14.8 Summary, Bibliographical and Historical Notes, Exercises ... 551 15 Probabilistic Reasoning over Time 15.1 Time and Uncertainty ... 566 15.2 Inference in Temporal Models ... 570 15.3 Hidden Markov Models ... 578 15.4 Kalman Filters ... 584 15.5 Dynamic Bayesian Networks ... 590 15.6 Keeping Track of Many Objects ... 599 15.7 Summary, Bibliographical and Historical Notes, Exercises ... 603 16 Making Simple Decisions 16.1 Combining Beliefs and Desires under Uncertainty ... 610 16.2 The Basis of Utility Theory ... 611 16.3 Utility Functions ... 615 16.4 Multiattribute Utility Functions ... 622 16.5 Decision Networks ... 626 16.6 The Value of Information ... 628 16.7 DecisionTheoretic Expert Systems ... 633 16.8 Summary, Bibliographical and Historical Notes, Exercises ... 636 17 Making Complex Decisions 17.1 Sequential Decision Problems ... 645 17.2 Value Iteration ... 652 17.3 Policy Iteration ... 656 17.4 Partially Observable MDPs ... 658 17.5 Decisions with Multiple Agents: Game Theory ... 666 17.6 Mechanism Design ... 679 17.7 Summary, Bibliographical and Historical Notes, Exercises ... 684 V Learning 18 Learning from Examples 18.1 Forms of Learning ... 693 18.2 Supervised Learning ... 695 18.3 Learning Decision Trees ... 697 18.4 Evaluating and Choosing the Best Hypothesis ... 708 18.5 The Theory of Learning ... 713 18.6 Regression and Classification with Linear Models ... 717 18.7 Artificial Neural Networks ... 727 18.8 Nonparametric Models ... 737 18.9 Support Vector Machines ... 744 18.10 Ensemble Learning ... 748 18.11 Practical Machine Learning ... 753 18.12 Summary, Bibliographical and Historical Notes, Exercises ... 757 19 Knowledge in Learning 19.1 A Logical Formulation of Learning ... 768 19.2 Knowledge in Learning ... 777 19.3 ExplanationBased Learning ... 780 19.4 Learning Using Relevance Information ... 784 19.5 Inductive Logic Programming ... 788 19.6 Summary, Bibliographical and Historical Notes, Exercises ... 797 20 Learning Probabilistic Models 20.1 Statistical Learning ... 802 20.2 Learning with Complete Data ... 806 20.3 Learning with Hidden Variables: The EM Algorithm ... 816 20.4 Summary, Bibliographical and Historical Notes, Exercises ... 825 21 Reinforcement Learning 21.1 Introduction ... 830 21.2 Passive Reinforcement Learning ... 832 21.3 Active Reinforcement Learning ... 839 21.4 Generalization in Reinforcement Learning ... 845 21.5 Policy Search ... 848 21.6 Applications of Reinforcement Learning ... 850 21.7 Summary, Bibliographical and Historical Notes, Exercises ... 853 VI Communicating, Perceiving, and Acting 22 Natural Language Processing 22.1 Language Models ... 860 22.2 Text Classification ... 865 22.3 Information Retrieval ... 867 22.4 Information Extraction ... 873 22.5 Summary, Bibliographical and Historical Notes, Exercises ... 882 23 Natural Language for Communication 23.1 Phrase Structure Grammars ... 888 23.2 Syntactic Analysis (Parsing) ... 892 23.3 Augmented Grammars and Semantic Interpretation ... 897 23.4 Machine Translation ... 907 23.5 Speech Recognition ... 912 23.6 Summary, Bibliographical and Historical Notes, Exercises ... 918 24 Perception 24.1 Image Formation ... 929 24.2 Early ImageProcessing Operations ... 935 24.3 Object Recognition by Appearance ... 942 24.4 Reconstructing the 3D World ... 947 24.5 Object Recognition from Structural Information ... 957 24.6 Using Vision ... 961 24.7 Summary, Bibliographical and Historical Notes, Exercises ... 965 25 Robotics 25.1 Introduction ... 971 25.2 Robot Hardware ... 973 25.3 Robotic Perception ... 978 25.4 Planning to Move ... 986 25.5 Planning Uncertain Movements ... 993 25.6 Moving ... 997 25.7 Robotic Software Architectures ... 1003 25.8 Application Domains ... 1006 25.9 Summary, Bibliographical and Historical Notes, Exercises ... 1010 VII Conclusions 26 Philosophical Foundations 26.1 Weak AI: Can Machines Act Intelligently? ... 1020 26.2 Strong AI: Can Machines Really Think? ... 1026 26.3 The Ethics and Risks of Developing Artificial Intelligence ... 1034 26.4 Summary, Bibliographical and Historical Notes, Exercises ... 1040 27 AI: The Present and Future 1044 27.1 Agent Components ... 1044 27.2 Agent Architectures ... 1047 27.3 Are We Going in the Right Direction? ... 1049 27.4 What If AI Does Succeed? ... 1051 A Mathematical Background A.1 Complexity Analysis and O() Notation ... 1053 A.2 Vectors, Matrices, and Linear Algebra ... 1055 A.3 Probability Distributions ... 1057 B Notes on Languages and Algorithms B.1 Defining Languages with BackusNaur Form (BNF) ... 1060 B.2 Describing Algorithms with Pseudocode ... 1061 B.3 Online Help ... 1062 Bibliography 1063 Index 1095