*This ISBN is unavailable for sale outside the United States. Please refer to the international edition: 9780132071482*For one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.
The long-anticipated revision of this best-selling text offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.
New To This Edition
top
# Expanded coverage of the principles of representational expressiveness: atomic, propositional, and relational representations.
# Revised definitions for search, planning, and MDPs to make concepts and notations consistent.
# New application examples from video/computer games.
# Expanded treatment of search, game-playing, and planning for nondeterministic and partially observable environments.
# New theoretical framework and algorithms for hierarchical planning.
# New chapter on relational and full first-order probability models.
# Substantially updated treatment of machine learning and computer vision.
# Expanded sections on search engines and information retrieval, parsing, grammar learning, machine translation.
# Support Solutions include: Companion Website, Solutions Manual, and PowerPoint Slides.
Features and Benefits
top
* Nontechnical learning material.
o
Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students.
* The Internet as a sample application for intelligent systems-Examples of logical reasoning, planning, and natural language processing using Internet agents.
o
Promotes student interest with interesting, relevant exercises.
* Increased coverage of material-New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.
o
Brings students up to date on the latest technologies, and presents concepts in a more unified manner.
* Updated and expanded exercises-75% of the exercises are revised, with 100 new exercises.
* More Online Software.
o
Allows many more opportunities for student projects on the web.
* A unified, agent-based approach to AI-Organizes the material around the task of building intelligent agents.
o
Shows students how the various subfields of AI fit together to build actual, useful programs.
* Comprehensive, up-to-date coverage-Includes a unified view of the field organized around the rational decision making paradigm.
* A flexible format.
o
Makes the text adaptable for varying instructors' preferences.
* In-depth coverage of basic and advanced topics.
o
Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
* Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet.
o
Gives instructors and students a choice of projects; reading and running the code increases understanding.
*
Author Maintained Website
o
visit http://aima.cs.berkeley.edu/ to access text-related Comments and Discussions, AI Resources on the Web, and Online Code Repository, Instructor Resources, and more!
Table of Contents
top
About the Authors
top
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.
