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Artificial Intelligence: A Modern Approach Hardcover – January 15, 1995

ISBN-13: 978-0131038059 ISBN-10: 0131038052 Edition: 1st

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Product Details

  • Hardcover: 960 pages
  • Publisher: Prentice Hall; 1st edition (January 15, 1995)
  • Language: English
  • ISBN-10: 0131038052
  • ISBN-13: 978-0131038059
  • Product Dimensions: 9.3 x 7.5 x 1.6 inches
  • Shipping Weight: 3.6 pounds
  • Average Customer Review: 3.9 out of 5 stars  See all reviews (31 customer reviews)
  • Amazon Best Sellers Rank: #540,702 in Books (See Top 100 in Books)

Editorial Reviews

Amazon.com Review

Artificial Intelligence: A Modern Approach introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as "anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors." This textbook is up-to-date and is organized using the latest principles of good textbook design. It includes historical notes at the end of every chapter, exercises, margin notes, a bibliography, and a competent index. Artificial Intelligence: A Modern Approach covers a wide array of material, including first-order logic, game playing, knowledge representation, planning, and reinforcement learning.

From the Publisher

The most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence.

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Customer Reviews

3.9 out of 5 stars
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Most Helpful Customer Reviews

36 of 38 people found the following review helpful By Stuart Russell on January 9, 2003
Format: Hardcover
Thanks to all those who reviewed the first edition.
If you are reading this, you will probably want the
second edition instead. It was published Dec 20, 2002.
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.
For more information, see aima.cs.berkeley.edu
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53 of 63 people found the following review helpful By whywong on December 12, 1999
Format: Hardcover
While R&N's coverage of topics in Artificial Intelligence is no doubt encyclopedic, several problems exist with the book:
1) The textbook is awfully traditional and only mentions in passing newer trends in AI. For example, case-based reasoning (or the "Yale view of AI") is mentioned, but not covered. Because AI is a new and rapidly changing field, and because AI paradigms are usually based on a small set of ontological assumptions, I believe it would not be too difficult for students to understand new paradigms. Obviously this should be a high pedagogical priority.
2) The textbook is rather condescending, with the authors strongly imposing their viewpoints. In other words, the authors are a little too dogmatic and that is reflected in the text. For example, they sometimes go about ranking paradigms.
3) The textbook is sometimes rather ambiguous when explicating certain paradigms, and the end-of-chapter problems are very, very ambiguous. One of the justifications for unclear questions is to get people thinking, but when the theoretical explications are already ambiguous it defeats the whole purpose.
4) The philosophical sections in R&N are rather naive and superficial.
In spite of its obvious shortcomings, R&N has been tremendously useful to me, and I recommend it as a reference. The good news, I've heard, is that a new edition of R&N is coming out next year where these problems are eliminated.
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23 of 26 people found the following review helpful By Robert Jones on February 16, 2001
Format: Hardcover
Russell and Norvig's AI: A Modern Approach is THE best AI text out there. At 932 pages it is encyclopedic, it has nearly everything. So what is missing? How could it be improved? Probably the worst thing about the book is the binding. I am not sure that you can read it from cover to cover without some pages coming loose. Perhaps its the length. Perhaps it needs to be split into two volumes. I am not a fan of pseudocode and all the algorithms are in pseudocode. I think the right compromise between detailed practical code and tutorial compactness is something like the code in Jackson's text Expert Systems. I realize this might make a long book even longer but I still think some examples in Lisp, Prolog, etc. would be an improvement. There are a few things missing. Some detail on case-based reasoning is needed and some newer topics like hybrid systems and rough sets. Also, more on parallel computing for AI. Occasionally I was annoyed by the references. On page 27 the authors attribute a story to Heckerman's 1991 thesis. The thesis contains no such story. The reference should have been to a private communication. By now you might think I hate the book. No. I am suggesting improvements. I repeat. It is THE BEST SINGLE AI TEXT IN PRINT. But you will not be able to teach the whole book in a single AI course. Not even a two semester course.
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78 of 97 people found the following review helpful By Dr. Lee D. Carlson HALL OF FAMEVINE VOICE on September 23, 2001
Format: Hardcover Verified Purchase
Progress in the field of artificial intelligence has executed a random walk after establishing itself with a bang in the 1950s. Optimistic predictions of the future of A.I. in that decade only partially came true in the decades after that. Currently, the field is divided up into subfields going by the names data mining, computational intelligence, intelligent agent theory, expert systems, etc. This book is the best book available for learning about this fascinating and important subject. The applications of A.I. are enormous, and will increase dramatically in the decades ahead. Indeed the prospects are very exciting, and the authors themselves have been involved heavily in extending the frontiers of the subject. Some of the main points of the book that really stand out include:
1. The useful exercises at the end of each chapter. 2. The discussion of simple reflex and goal-based agents. 3. The treatment of constraint satisfaction problems and heuristics for these kinds of problems. 4. The overview of iterative improvement algorithms, particularly the discussion of simulated annealing. 5. The discussion of propositional logic and its limitations as an effective A.I. paradigm. 6. The treatment of first-order logic and its use in modeling simple reflex agents, change, and its use in situation calculus. There is a good overview of inference in first-order logic in chapter 9 of the book, including completeness and resolution. 7. The treatment of logic programming systems; the Prolog language is discussed as a logical programming language. Noting that Prolog cannot specify constraints on values, the authors discuss constraint logic programming (CLP) as an alternative logic programming language that allows constraints. 8. The discussion of semantic networks and description logics. 9.
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