"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 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: