Artificial Intelligence Pearson International Edition 3rd Edition

Stuart Russell was born in Portsmouth, England in 1962. 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 Professor and Chair of Electrical Engineering and Computer Sciences, 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 and in 2005 he received the ACM Karlstrom Outstanding Educator Award. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence and a Fellow of the Association for Computing Machinery. He has published over 150 papers on a wide range of topics in artificial intelligence. His books include The Use of Knowledge in Analogy and Induction (Pitman, 1989), Do the Right Thing: Studies in Limited Rationality (with Eric Wefald, MIT Press, 1991), and Artificial Intelligence: A Modern Approach (with Peter Norvig, Prentice Hall, 1995, 2003). Peter Norvig is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. At Google Inc he was Director of Search Quality, responsible for the core web search algorithms from 2002-2005, and has been Director of Research from 2005 on. Previously he was the head of the Computational Sciences Division at NASA Ames Research Center, making him NASA's senior computer scientist. He received the NASA Exceptional Achievement Award in 2001. He has served as an assistant professor at the University of Southern California and a research faculty member at the University of California at Berkeley Computer Science Department, from which he received a Ph.D. in 1986 and the distinguished alumni award in 2006. He has over fifty publications in Computer Science, concentrating on Artificial Intelligence, Natural Language Processing and Software Engineering, including the books Artificial Intelligence: A Modern Approach (the leading textbook in the field), Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX. He is also the author of the Gettysburg Powerpoint Presentation and the world's longest palindromic sentence.

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 AlphaBeta 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 . . . . . . . . . . . . . . . . . . ...