Product 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.

Product Details

• Amazon Sales Rank: #162038 in Books
• Published on: 2003-01-13
• Original language: English
• Binding: Paperback
• 1132 pages

Review

"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)

From the Author
Visit the home page for the book:
The home page is http://www.cs.berkeley.edu/~russell/aima.html

From the Back Cover

The first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field.

In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI.

The book is supported by a suite of online resources including source code, figures, lecture slides, a directory of over 800 links to "AI on the Web," and an online discussion group. All of this is available at:
aima.cs.berkeley.edu

Customer Reviews

First port of call for everything AI
An essential core text for anyone interested in artificial intelligence with a particular emphasis on the practical use and implementation of intelligent agents. Thorough and comprehensive, yet highly suitable as an entry-level text and written with a light touch that actually had me chuckling out loud at a couple of points (believe it or not)!

Excellent, but not light reading
This is my favourite AI textbook, which I strongly recommend. But be warned: it's long and detailed and thorough, so don't expect a lightweight overview - it's where to go when you really want to understand the stuff. It's heavily biased towards First Order Logic as the way to do knowledge representation, and is especially good on Bayesian networks.

If you're interested in the philosophical issues involved in "real artificial intelligence" rather than (or as well as) the technical stuff, chapter 26 is a superb introduction.

Excellent book for Lecture notes
Have surveyed various AI books for lecturing of AI (Foundation) and found that this is the best book as far as the syllabus is concerned: Introduction, Intelligent Agents (v good), Search, Knowledge Representation, Machine Learning, Planning, NLP and Philosophy of AI. The rich set of examples are also useful and the students enjoyed them. Having said that, specific areas such as NLP, Neural Networks, Machine Vision are better off in specialised book. Would be great if there are references to specific websites for more further details.