ARTIFICIAL INTELLIGENCE E-BOOK 

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About the Author

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor’s Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Faceto-Face Dialog and Intelligent Help Systems for UNIX.

 CONTENTS

I Artificial Intelligence
1 Introduction 1
1.1 What IsAI? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 The Foundations of Artificial Intelligence . . . . . . . . . . . . . . . . . . 5
1.3 The History of Artificial Intelligence . . . . . . . . . . . . . . . . . . . . 16
1.4 TheState of theArt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28
1.5 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 29
2 Intelligent Agents 34
2.1 Agents andEnvironments . . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2 Good Behavior: The Concept of Rationality . . . . . . . . . . . . . . . . 36
2.3 TheNature ofEnvironments . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.4 TheStructure ofAgents . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
2.5 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 59
II Problem-solving
3 Solving Problems by Searching 64
3.1 Problem-SolvingAgents . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
3.2 ExampleProblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69
3.3 Searching forSolutions . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
3.4 Uninformed SearchStrategies . . . . . . . . . . . . . . . . . . . . . . . . 81
3.5 Informed (Heuristic) Search Strategies . . . . . . . . . . . . . . . . . . . 92
3.6 Heuristic Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
3.7 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 108
4 Beyond Classical Search 120
4.1 LocalSearchAlgorithms andOptimization Problems . . . . . . . . . . . 120
4.2 Local Search in Continuous Spaces . . . . . . . . . . . . . . . . . . . . . 129
4.3 Searching with Nondeterministic Actions . . . . . . . . . . . . . . . . . . 133
4.4 SearchingwithPartialObservations . . . . . . . . . . . . . . . . . . . . . 138
4.5 Online Search Agents and Unknown Environments . . . . . . . . . . . . 147
4.6 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 153
5 Adversarial Search 161
5.1 Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
5.2 OptimalDecisions inGames . . . . . . . . . . . . . . . . . . . . . . . . 163
5.3 Alpha–Beta Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
5.4 ImperfectReal-TimeDecisions . . . . . . . . . . . . . . . . . . . . . . . 171
5.5 StochasticGames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177 

5.6 PartiallyObservableGames . . . . . . . . . . . . . . . . . . . . . . . . . 180
5.7 State-of-the-Art Game Programs . . . . . . . . . . . . . . . . . . . . . . 185
5.8 AlternativeApproaches . . . . . . . . . . . . . . . . . . . . . . . . . . . 187
5.9 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 189
6 Constraint Satisfaction Problems 202
6.1 DefiningConstraint Satisfaction Problems . . . . . . . . . . . . . . . . . 202
6.2 Constraint Propagation: Inference inCSPs . . . . . . . . . . . . . . . . . 208
6.3 Backtracking Search forCSPs . . . . . . . . . . . . . . . . . . . . . . . . 214
6.4 LocalSearch forCSPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 220
6.5 TheStructure ofProblems . . . . . . . . . . . . . . . . . . . . . . . . . . 222
6.6 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 227
III Knowledge, reasoning, and planning
7 Logical Agents 234
7.1 Knowledge-Based Agents . . . . . . . . . . . . . . . . . . . . . . . . . . 235
7.2 TheWumpusWorld . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 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 andHistoricalNotes,Exercises . . . . . . . . . 274
8 First-Order Logic 285
8.1 Representation Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . 285
8.2 Syntax andSemantics ofFirst-OrderLogic . . . . . . . . . . . . . . . . . 290
8.3 UsingFirst-OrderLogic . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
8.4 KnowledgeEngineering inFirst-OrderLogic . . . . . . . . . . . . . . . . 307
8.5 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 313
9 Inference in First-Order Logic 322
9.1 Propositional vs. First-Order Inference . . . . . . . . . . . . . . . . . . . 322
9.2 Unification and Lifting . . . . . . . . . . . . . . . . . . . . . . . . . . . 325
9.3 ForwardChaining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
9.4 BackwardChaining . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337
9.5 Resolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 345
9.6 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 357
10 Classical Planning 366
10.1 Definition of Classical Planning . . . . . . . . . . . . . . . . . . . . . . . 366
10.2 Algorithms forPlanning asState-Space Search . . . . . . . . . . . . . . . 373
10.3 PlanningGraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 379

10.4 Other Classical Planning Approaches . . . . . . . . . . . . . . . . . . . . 387
10.5 Analysis ofPlanningApproaches . . . . . . . . . . . . . . . . . . . . . . 392
10.6 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 393
11 Planning and Acting in the Real World 401
11.1 Time,Schedules, andResources . . . . . . . . . . . . . . . . . . . . . . . 401
11.2 Hierarchical Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . 406
11.3 Planning and Acting in Nondeterministic Domains . . . . . . . . . . . . . 415
11.4 Multiagent Planning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 425
11.5 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 430
12 Knowledge Representation 437
12.1 Ontological Engineering . . . . . . . . . . . . . . . . . . . . . . . . . . . 437
12.2 Categories andObjects . . . . . . . . . . . . . . . . . . . . . . . . . . . 440
12.3 Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 446
12.4 MentalEvents andMentalObjects . . . . . . . . . . . . . . . . . . . . . 450
12.5 Reasoning Systems forCategories . . . . . . . . . . . . . . . . . . . . . 453
12.6 ReasoningwithDefault Information . . . . . . . . . . . . . . . . . . . . 458
12.7 The Internet Shopping World . . . . . . . . . . . . . . . . . . . . . . . . 462
12.8 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 467
IV Uncertain knowledge and reasoning
13 Quantifying Uncertainty 480
13.1 Acting under Uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . 480
13.2 Basic Probability Notation . . . . . . . . . . . . . . . . . . . . . . . . . . 483
13.3 InferenceUsingFull JointDistributions . . . . . . . . . . . . . . . . . . . 490
13.4 Independence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 494
13.5 Bayes’Rule and ItsUse . . . . . . . . . . . . . . . . . . . . . . . . . . . 495
13.6 TheWumpusWorldRevisited . . . . . . . . . . . . . . . . . . . . . . . . 499
13.7 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 503
14 Probabilistic Reasoning 510
14.1 Representing Knowledge in an Uncertain Domain . . . . . . . . . . . . . 510
14.2 TheSemantics ofBayesianNetworks . . . . . . . . . . . . . . . . . . . . 513
14.3 Efficient Representation of Conditional Distributions . . . . . . . . . . . . 518
14.4 Exact Inference inBayesianNetworks . . . . . . . . . . . . . . . . . . . 522
14.5 Approximate Inference inBayesianNetworks . . . . . . . . . . . . . . . 530
14.6 Relational and First-Order Probability Models . . . . . . . . . . . . . . . 539
14.7 OtherApproaches toUncertainReasoning . . . . . . . . . . . . . . . . . 546
14.8 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 551
15 Probabilistic Reasoning over Time 566
15.1 Time andUncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . 566

15.2 Inference inTemporalModels . . . . . . . . . . . . . . . . . . . . . . . . 570
15.3 HiddenMarkovModels . . . . . . . . . . . . . . . . . . . . . . . . . . . 578
15.4 Kalman Filters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 584
15.5 DynamicBayesianNetworks . . . . . . . . . . . . . . . . . . . . . . . . 590
15.6 KeepingTrack ofManyObjects . . . . . . . . . . . . . . . . . . . . . . . 599
15.7 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 603
16 Making Simple Decisions 610
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 DecisionNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 626
16.6 TheValue of Information . . . . . . . . . . . . . . . . . . . . . . . . . . 628
16.7 Decision-Theoretic Expert Systems . . . . . . . . . . . . . . . . . . . . . 633
16.8 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 636
17 Making Complex Decisions 645
17.1 SequentialDecision Problems . . . . . . . . . . . . . . . . . . . . . . . . 645
17.2 Value Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 652
17.3 Policy Iteration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 656
17.4 PartiallyObservableMDPs . . . . . . . . . . . . . . . . . . . . . . . . . 658
17.5 Decisions with Multiple Agents: Game Theory . . . . . . . . . . . . . . . 666
17.6 MechanismDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 679
17.7 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 684
V Learning
18 Learning from Examples 693
18.1 FormsofLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 693
18.2 Supervised Learning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 695
18.3 LearningDecisionTrees . . . . . . . . . . . . . . . . . . . . . . . . . . . 697
18.4 Evaluating and Choosing the Best Hypothesis . . . . . . . . . . . . . . . 708
18.5 TheTheory ofLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . 713
18.6 Regression and Classification with Linear Models . . . . . . . . . . . . . 717
18.7 ArtificialNeuralNetworks . . . . . . . . . . . . . . . . . . . . . . . . . 727
18.8 Nonparametric Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 737
18.9 Support Vector Machines . . . . . . . . . . . . . . . . . . . . . . . . . . 744
18.10 EnsembleLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 748
18.11 PracticalMachineLearning . . . . . . . . . . . . . . . . . . . . . . . . . 753
18.12 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 757
19 Knowledge in Learning 768
19.1 ALogical Formulation ofLearning . . . . . . . . . . . . . . . . . . . . . 768

19.2 Knowledge inLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . 777
19.3 Explanation-Based Learning . . . . . . . . . . . . . . . . . . . . . . . . 780
19.4 LearningUsingRelevance Information . . . . . . . . . . . . . . . . . . . 784
19.5 Inductive Logic Programming . . . . . . . . . . . . . . . . . . . . . . . . 788
19.6 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 797
20 Learning Probabilistic Models 802
20.1 StatisticalLearning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 802
20.2 LearningwithCompleteData . . . . . . . . . . . . . . . . . . . . . . . . 806
20.3 LearningwithHiddenVariables: TheEMAlgorithm . . . . . . . . . . . . 816
20.4 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 825
21 Reinforcement Learning 830
21.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 830
21.2 Passive Reinforcement Learning . . . . . . . . . . . . . . . . . . . . . . 832
21.3 ActiveReinforcement Learning . . . . . . . . . . . . . . . . . . . . . . . 839
21.4 Generalization inReinforcement Learning . . . . . . . . . . . . . . . . . 845
21.5 PolicySearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 848
21.6 Applications ofReinforcement Learning . . . . . . . . . . . . . . . . . . 850
21.7 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 853
VI Communicating, perceiving, and acting
22 Natural Language Processing 860
22.1 Language Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 860
22.2 Text Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 865
22.3 InformationRetrieval . . . . . . . . . . . . . . . . . . . . . . . . . . . . 867
22.4 Information Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 873
22.5 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 882
23 Natural Language for Communication 888
23.1 PhraseStructureGrammars . . . . . . . . . . . . . . . . . . . . . . . . . 888
23.2 SyntacticAnalysis (Parsing) . . . . . . . . . . . . . . . . . . . . . . . . . 892
23.3 AugmentedGrammars andSemantic Interpretation . . . . . . . . . . . . 897
23.4 MachineTranslation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907
23.5 Speech Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 912
23.6 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 918
24 Perception 928
24.1 ImageFormation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 929
24.2 Early Image-Processing Operations . . . . . . . . . . . . . . . . . . . . . 935
24.3 Object Recognition by Appearance . . . . . . . . . . . . . . . . . . . . . 942
24.4 Reconstructing the 3DWorld . . . . . . . . . . . . . . . . . . . . . . . . 947
24.5 Object Recognition from Structural Information . . . . . . . . . . . . . . 957

24.6 UsingVision . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 961
24.7 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 965
25 Robotics 971
25.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 971
25.2 Robot Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 973
25.3 Robotic Perception . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 978
25.4 Planning toMove . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 986
25.5 PlanningUncertainMovements . . . . . . . . . . . . . . . . . . . . . . . 993
25.6 Moving . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 997
25.7 Robotic SoftwareArchitectures . . . . . . . . . . . . . . . . . . . . . . . 1003
25.8 ApplicationDomains . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1006
25.9 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 1010
VII Conclusions
26 Philosophical Foundations 1020
26.1 Weak AI: Can Machines Act Intelligently? . . . . . . . . . . . . . . . . . 1020
26.2 StrongAI:CanMachinesReallyThink? . . . . . . . . . . . . . . . . . . 1026
26.3 The Ethics and Risks of Developing Artificial Intelligence . . . . . . . . . 1034
26.4 Summary,Bibliographical andHistoricalNotes,Exercises . . . . . . . . . 1040
27 AI: The Present and Future 1044
27.1 Agent Components . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1044
27.2 AgentArchitectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1047
27.3 AreWeGoing intheRightDirection? . . . . . . . . . . . . . . . . . . . 1049
27.4 What If AI Does Succeed? . . . . . . . . . . . . . . . . . . . . . . . . . 1051
A Mathematical background 1053
A.1 ComplexityAnalysis andO()Notation . . . . . . . . . . . . . . . . . . . 1053
A.2 Vectors, Matrices, and Linear Algebra . . . . . . . . . . . . . . . . . . . 1055
A.3 Probability Distributions . . . . . . . . . . . . . . . . . . . . . . . . . . . 1057
B Notes on Languages and Algorithms 1060
B.1 Defining Languages with Backus–Naur Form (BNF) . . . . . . . . . . . . 1060
B.2 Describing Algorithms with Pseudocode . . . . . . . . . . . . . . . . . . 1061
B.3 OnlineHelp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1062

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