Artificial Intelligence
A Modern Approach: United States Edition
2nd Edition
Published on 6. January 2003
Book
Paperback/Softback
1132 pages
978-0-13-790395-5 (ISBN)
Description
View chapters 3 and 4 from the upcoming Third Edition.
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.
Click on "Features" tab below for more information
Resources:
Visit the author's website http://aima.cs.berkeley.edu/ to access both student and instructor resources including Power Point slides, syllabus. homework and exams, and solutions text problems.
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.
Click on "Features" tab below for more information
Resources:
Visit the author's website http://aima.cs.berkeley.edu/ to access both student and instructor resources including Power Point slides, syllabus. homework and exams, and solutions text problems.
Reviews / Votes
"The publication of this textbook was a major step forward, not only for the teaching of AI, but for the unified view of the field that this book introduces. Even for experts in the field, there are important insights in almost every chapter." - Prof. Thomas Dietterich, Oregon State"Just terrific. The book I've always been waiting for...the AI bible for the next decade." - Prof. Gerd Brewka (Vienna)
"A marvelous achievement, a truly beautiful book!" - Prof. Selmer Bringsjord, RPI
"It's a great book, with incredible breadth and depth, and very well-written. Everyone I know who has used it in their class has loved it." - Prof. Haym Hirsh, Rutgers
"I am deeply impressed by its unprecedented quality in presenting a coherent, balanced, broad and deep, enjoyable picture of the field of AI. It will become tire standard text for the years to come." - Prof. Wolfgang Bibel, Darmstadt
"Terrific! Well-written and well-organised, with comprehensive coverage of the material that every AI student should know." - Prof. Martha Pollack (Michigan)
"Outstanding ...Its descriptions are extremely clear and readable; its organization is excellent; its examples are motivating; and its coverage is scholarly and throughout! ...will deservedly dominate the field for some time." - Prof. Nils Nilsson, Stanford
"The best book available now...It's almost as good as the book Charniak and I wrote, but more up to date. (Okay I'll admit it, it may even be better than our book.)" - Prof. Drew McDermott, Yale
"A magisterial wide scope account of the entire field of Artificial Intelligence that will enlighten professors as well as students." - Dr. Alan Kay
"This is the book that made me love AI." - Student (Indonesia)
More details
Edition
2nd edition
Language
English
Place of publication
United States
Publishing group
Pearson Education (US)
Target group
College/higher education
Dimensions
Height: 254 mm
Width: 209 mm
Thickness: 43 mm
Weight
2218 gr
ISBN-13
978-0-13-790395-5 (9780137903955)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
New editions
Book
02/2010
3rd Edition
Pearson
€220.07
Shipment within 15-20 days
Persons
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 director of Search Quality at Google, Inc. He is a Fellow and Executive Council member of the American Association for Artificial Intelligence. 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. Before that he served as chief scientist at Junglee, where he helped develop one of the first Internet information extraction services, and as a senior scientist at Sun Microsystems Laboratories working on intelligent information retrieval. 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 has been a professor at the University of Southern California and a research faculty member at Berkeley. He has over 50 publications in computer science including the books Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
Peter Norvig is director of Search Quality at Google, Inc. He is a Fellow and Executive Council member of the American Association for Artificial Intelligence. 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. Before that he served as chief scientist at Junglee, where he helped develop one of the first Internet information extraction services, and as a senior scientist at Sun Microsystems Laboratories working on intelligent information retrieval. 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 has been a professor at the University of Southern California and a research faculty member at Berkeley. He has over 50 publications in computer science including the books Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.
Content
I. ARTIFICIAL INTELLIGENCE.
1. Introduction.
2. Intelligent Agents.
II. PROBLEM-SOLVING.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
IV. PLANNING.
11. Planning.
12. Planning and Acting in the Read World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
13. Uncertainty.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
VI. LEARNING.
18. Learning from Observations.
19. Knowledge in Learning.
20. Statistical Learning Methods.
21. Reinforcement Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
24. Perception.
25. Robotics.
VIII. CONCLUSIONS.
26. Philosophical Foundations.
27. AI: Present and Future.
1. Introduction.
2. Intelligent Agents.
II. PROBLEM-SOLVING.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
IV. PLANNING.
11. Planning.
12. Planning and Acting in the Read World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
13. Uncertainty.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
VI. LEARNING.
18. Learning from Observations.
19. Knowledge in Learning.
20. Statistical Learning Methods.
21. Reinforcement Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
24. Perception.
25. Robotics.
VIII. CONCLUSIONS.
26. Philosophical Foundations.
27. AI: Present and Future.