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Artificial Intelligence: A Modern Approach / Edition 3

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Overview

Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level 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 author Dr. Jennifer Widom.

Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®.


To learn more about the course on artificial intelligence, visit http://www.ai-class.com. To read the full
New York Times article, click here.

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Editorial Reviews

Booknews
a text primarily intended for use in an undergraduate course or course sequence. It shows how intelligent agents can be built using AI methods and explains how different agent designs are appropriate depending on the nature of the task and environment. It uses examples and exercises to lead students from simple reactive agents to advanced planning agents with natural language capabilities. Annotation c. Book News, Inc., Portland, OR (booknews.com)
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Product Details

  • ISBN-13: 9780136042594
  • Publisher: Prentice Hall
  • Publication date: 12/14/2009
  • Series: Prentice Hall Series in Artificial Intelligence
  • Edition description: New Edition
  • Edition number: 3
  • Pages: 1132
  • Sales rank: 139,061
  • Product dimensions: 8.30 (w) x 10.10 (h) x 1.80 (d)

Meet 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.

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Table of Contents

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 Problem-solving
3 Solving Problems by Searching
3.1 Problem-Solving 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 Alpha—Beta Pruning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
5.4 Imperfect Real-Time Decisions . . . . . . . . . . . . . . . . . . . . . . . 171
5.5 Stochastic Games . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
5.6 Partially Observable Games . . . . . . . . . . . . . . . . . . . . . . . . . 180
5.7 State-of-the-Art 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 Knowledge-Based 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 First-Order Logic
8.1 Representation Revisited . . . . . . . . . . . . . . . . . . . . . . . . . . 285
8.2 Syntax and Semantics of First-Order Logic . . . . . . . . . . . . . . . . . 290
8.3 Using First-Order Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . 300
8.4 Knowledge Engineering in First-Order Logic . . . . . . . . . . . . . . . . 307
8.5 Summary, Bibliographical and Historical Notes, Exercises . . . . . . . . . 313

9 Inference in First-Order Logic
9.1 Propositional vs. First-Order 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 State-Space 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 First-Order 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 Decision-Theoretic 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 Explanation-Based 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 Image-Processing 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 Backus—Naur Form (BNF) . . . . . . . . . . . . 1060
B.2 Describing Algorithms with Pseudocode . . . . . . . . . . . . . . . . . . 1061
B.3 Online Help . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1062
Bibliography 1063
Index 1095

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