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Artificial Intelligence: A Modern Approach [Hardcover]

by Stuart J. Russell, Peter Norvig
3.9 out of 5 stars  See all reviews (31 customer reviews)

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Book Description

January 15, 1995 0131038052 978-0131038059 1st
This is an introduction to the theory and practice of artificial intelligence. It uses an intelligent agent as the unifying theme throughout, and covers areas that are sometimes underemphasized elsewhere. These include reasoning under uncertainty, learning, natural language, vision and robotics. The book also explains in detail some of the more recent ideas in the field, including simulated annealing, memory-bounded search, global ontologies, dynamic belief networks, neural nets, inductive logic programming, computational learning theory, and reinforcement learning.

Editorial Reviews 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.

Product Details

  • Hardcover: 960 pages
  • Publisher: Prentice Hall; 1st edition (January 15, 1995)
  • Language: English
  • ISBN-10: 0131038052
  • ISBN-13: 978-0131038059
  • Product Dimensions: 9.6 x 7.8 x 1.5 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: #530,009 in Books (See Top 100 in Books)

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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
5.0 out of 5 stars Second Edition has been published! January 9, 2003
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
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53 of 63 people found the following review helpful
3.0 out of 5 stars Encyclopedic December 12, 1999
By whywong
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
5.0 out of 5 stars A Review of Russell and Norvig's AI: A Modern Approach February 16, 2001
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
5.0 out of 5 stars The optimal learning algorithm for learning A.I. 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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Most Recent Customer Reviews
2.0 out of 5 stars Worst Textbook of the year!
I don't know why authors of AI and Machiner Learning books can not write their books explaining concepts in the most effective and efficient way, without using all the very... Read more
Published 14 months ago by Rafael Carvajal Díaz
4.0 out of 5 stars Artificial intelligence
Very good reference book on main trends in artificial intelligence. It's a bit outdated but all the basis are here with very clear explanations and implementation examples in... Read more
Published on December 16, 2011 by Gaël Waiche
3.0 out of 5 stars All theory but lacks concrete examples
The book explains well the theory behind many AI techniques, but you only reaches the pseudocode, so people that are new to programming must resort to Google to get some better... Read more
Published on October 22, 2010 by Daniel Mosquera
4.0 out of 5 stars A good book
The book gives a good introduction to the field and tries to explain the underlying problems and challenges and their possible solutions. Read more
Published on December 5, 2002
1.0 out of 5 stars Buyer Beware
I am a Computer Science major at UGA and I can honestly say that this book is the absolute worst of any that I have ever read. Read more
Published on September 17, 2002
3.0 out of 5 stars A Good Introduction, But Not the Whole Enchilada
This book is useful as an introductory textbook. After reading this book, I produced an game playing system with randomized behavior that was able to beat very good (but not... Read more
Published on July 1, 2002
2.0 out of 5 stars You could do better
...I can only relate my experience from using this as a textbook for an introductory AI class with assigned topics. Read more
Published on April 7, 2002 by Hilbert
4.0 out of 5 stars A good introduction to a cutting-edge field
Here is a fine primer for one of the most promising subdivisions of computer science. In it you will find all the conceptual foundations for the field, including most of what has... Read more
Published on February 13, 2002 by Yu-jin Chia
3.0 out of 5 stars A Mile Wide and an Inch Deep
Not being an expert in this field, what I liked about this book was the wide range of topics it covered, sketching a history of the central ideas in each. Read more
Published on September 20, 2001 by Amazon Customer
5.0 out of 5 stars Fantistic Introduction to an Interesting Field
AI: A Modern Approach is a great introduction to a good range of topics in the field of AI. Going into this book, I knew nothing of AI. Read more
Published on September 8, 2001 by B. Connelly
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Topic From this Discussion
Hi John,

The term "software engineer" has become almost meaningless, so I'll answer in terms of undergraduate CS subjects. To get the most out of this book, you need to know undergrad data structures and algorithms (including basic complexity theory), logic and proofs, basic... Read more
Jul 3, 2006 by Chris Simpkins |  See all 2 posts
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