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Artificial Intelligence: A Modern Approach (2nd Edition) 2nd Edition

4.3 out of 5 stars 64 customer reviews
ISBN-13: 978-0137903955
ISBN-10: 0137903952
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Editorial Reviews

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 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

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Product Details

  • Hardcover: 1132 pages
  • Publisher: Prentice Hall; 2 edition (December 30, 2002)
  • Language: English
  • ISBN-10: 0137903952
  • ISBN-13: 978-0137903955
  • Product Dimensions: 8.3 x 1.8 x 10.1 inches
  • Shipping Weight: 4.8 pounds
  • Average Customer Review: 4.3 out of 5 stars  See all reviews (64 customer reviews)
  • Amazon Best Sellers Rank: #59,947 in Books (See Top 100 in Books)

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Customer Reviews

Top Customer Reviews

Format: Hardcover
I didn't think that the first edition of this book was as bad as some of the reviewers said, but the second edition is definitely a vast improvement. It's not just some obligatory 2nd edition that some authors release to say that they are staying actively published. The first edition was somewhat confusing in its explanations and the exercises were really blurry on what was being asked. All of that has now been resolved.
The book is a comprehensive and insightful introduction to artificial intelligence with an academic tone. It provides a unified view of the field organized around the rational decision making paradigm, which focuses on the selection of the "best" solution to a problem. The book's overall theme is that the purpose of AI is to solve problems via intelligent agents, and then goes about specifying the features such an agent or agents should have. Pseudocode is provided for all of the major AI algorithms. Being about the broadest book in terms of coverage of AI, you should therefore not expect it to be the deepest in coverage. However, each topic is covered to the extent that the reader should understand its essence. Sections one through six are absolutely wonderful, and comprise the "meat" of AI. Section seven is rather weak since it tries to cover both robotics and text processing in their own individual chapters, and entire books have a hard time covering this material. Section eight is different from the others, since it talks about the philosophy and future of AI.
Another plus for this book is that there is a great deal of extra material that deals with standard AI curriculum.
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Format: Hardcover
Most of the reviews here refer to the first edition, not the second. There have been significant changes to the second edition. Amazon should consider using a display that clarifies which edition each review refers to.
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Format: Hardcover
The book offers mathematically based coverage of the most important topics in AI. It has a very pragmatic view and does not spend too much time with the over-hyped topics, such as fuzzy logic, genetic algorithms. Instead, it provides clear and easy to understand examples for most of the introduced concepts.

The text requires some knowledge of computer science, mathematics, and statistics. Sections on topics that require extensive outside knowledge, such as computational learning theory, are not covered very deeply, but usually offer nice insight to the concepts and basic principles.

I recommend this book both for beginners to exact AI as an introductory text and to more experienced researchers as an invaluable reference. I found it to be the most useful book I own.
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Format: Hardcover Verified Purchase
Until recently, my Algorithms book was my favorite text book ever. However, AI: A Modern Approach has supplanted it. This book is the most thoughtfully designed, easily understandable, clear text I've ever used in over 28 years of attending schools. I really knew nothing about AI when I took my first grad class in AI, but this book, along with a pretty great instructor, has been a wonderful resource, more than any other book I've used. I have not need to google for more information or speak to the professor. The answers are here--clear and concrete.

Have no fear and trust this book!
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Format: Hardcover
This is currently the best general purpose book on AI (the field). It is by no means the best book on any individual topic though... but I guess that wasn't the point of the book to begin with.

I'm not particularly happy with this book though. I think it's the language, or the examples, or something, but often it's just not `clear'---not as intuitive as I'd like. Many teachers seem to use it in a `general purpose course on AI' though (not anything particularly detailed, so it's `ok'---there are worse books on the subjects).

In short: as a general purpose AI book on everything, it does its job, but don't expect it to be anything other than a glorified field overview.
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Format: Hardcover
This is probably the most up-to-date single textbook introducing Artificial Intelligence. Unfortunately, it presents a limited view of the field by chosing not to cover the vast area of AI that deals with cognitive modeling. The authors are up front about this decision and define AI as the study of agents that act rationally [Preface and Introduction] and specifically choose not to address the schools of thought and ongoing research in AI that consider "Systems that think like humans" and "Systems that act like humans" [Introduction]. What's covered is done well. Students of AI and other readers will need to augment this text for exposure to the rest of the discipline.
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Format: Hardcover
This book is excellent for a novice in AI. Chapter by chapter - though chapters vary in thoroughness and detail - the book illustrates AI techniques by family by accessibly presenting the problems that motivate the techniques, and the logic applied to implement them (pseudo-code).

Arguably the book is written from the easier towards the harder methods. It's a good treatment on search strategy and basic logic, but notably somewhat lacking in pattern recognition, unbound optimization, and some machine learning topics, which is understandable considering how extensive the subjects are.

As a starting point towards learning about AI techniques, or as a course text-book the Russel and Norvig (this) book is an excellent resource. The book will set up a student with a mindset towards identifying search, optimization and reasoning problems that lend themselves towards AI solutions, and how to pick the appropriate technique to solve them.

Note that the book presents all of these techniques through a framework of thought around intelligent agents (which can be somewhat confusing considering you will later mostly hear the keyword 'agent' in AI techniques that apply social intelligence, or solve problems via interactions between somewhat independent intelligent constructs).

Follow up with Duda's "Pattern Classification", and Mitchell's "An Introduction to Genetic Algorithms (Complex Adaptive Systems)" for a more in depth treatment on Machine Learning problems and solutions and Genetic Algorithms. Maybe also some reading on swarm intelligence, and you'll have good referential knowledge and a decent tool-set of AI reasoning and problem solving skills.
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