Artificial Intelligence: A Modern Approach (3rd Edition) 3rd Edition

by Stuart Russell (Author), Peter Norvig (Author)

126 customer reviews

ISBN-13: 978-0136042594

ISBN-10: 0136042597

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Artificial Intelligence: A Modern Approach, 3e offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Number one in its field, this textbook is ideal for one or two-semester, undergraduate or graduate-level courses in Artificial Intelligence.

Dr. Peter Norvig, contributing Artificial Intelligence author and Professor Sebastian Thrun, a Pearson author are offering a free online course at Stanford University on artificial intelligence.

According to an article in The New York Times , the course on artificial intelligence is “one of three being offered experimentally by the Stanford computer science department to extend technology knowledge and skills beyond this elite campus to the entire world.” One of the other two courses, an introduction to database software, is being taught by Pearson author Dr. Jennifer Widom.

Artificial Intelligence: A Modern Approach, 3e is available to purchase as an eText for your Kindle™, NOOK™, and the iPhone®/iPad®.

To learn more about the course on artificial intelligence, visit http://www.ai-class.com. To read the full New York Times article, click here.

Artificial Intelligence: A Modern Approach (3rd Edition)

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Product Details

Hardcover: 1152 pages
Publisher: Pearson; 3 edition (December 11, 2009)
Language: English
ISBN-10: 0136042597
ISBN-13: 978-0136042594
Product Dimensions: 8.3 x 1.7 x 10.3 inches
Shipping Weight: 4.8 pounds (View shipping rates and policies)
Average Customer Review: 3.9 out of 5 stars See all reviews (126 customer reviews)
Amazon Best Sellers Rank: #44,907 in Books (See Top 100 in Books)
- #14 in Books > Textbooks > Computer Science > Artificial Intelligence
- #49 in Books > Computers & Technology > Computer Science > AI & Machine Learning > Intelligence & Semantics

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Customer Reviews

126

3.9 out of 5 stars

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Top Customer Reviews

Great book, terrible Kindle conversion

BySean Walkeron September 18, 2011

Format: Kindle EditionVerified Purchase

This is an excellent book however I cannot recommend purchasing the Kindle version of this text. It is atrocious. There are subject headings inserted after the subject is spoken about and, quite often, many heading stacked up on top of each other taking up almost an entire page with useless titles that are in the wrong order anyway. There are no page numbers, which is unacceptable for a text that is used by many college AI programs across the country. There are tons of hyphenation errors. The delineations between figure notes and the text are almost imperceptible so it is difficult to tell what text goes where. In general it is difficult to read and navigate due to this horrible Kindle conversion.

1212 comments 134 people found this helpful. Was this review helpful to you?YesNo

A disappointment: minor update not worth the money

ByDamon Devilleon February 3, 2010

Format: HardcoverVerified Purchase

- With AIMA 1st Edition, I had relearned AI anew from a fresh, insightful and wonderfully pedagogical perspective.
Best computer science textbook ever.
- With AIMA 2nd Edition, I got a lot of recent advances in AI brought to me in the same way, even if presented at times in a way that was too concise for a textbook, and read more like an encyclopedia.
Yet, great 2nd Edition.

- This 3rd Edition is alas AIMA 2.1 and not the AIMA 3.0 that I was waiting for. The new material and new insightful way to organize past material are both scant. Certainly not worth the price for those who own the 2nd Edition.

Don't get me wrong, if you are about to buy your first AI textbook, this is a great buy as it is still light years ahead of the competition. But some chapters that were getting really thin and outdated in 2009 did not get significant updating.

This is particularly true for knowledge representation. Missing are all the recent yet already consolidated advances brought about by the new solutions to the frame problem (such as the fluent calculus), default reasoning, abduction-based and case-based diagnosis, rule-based reasoning (such as constraint handling rules, answer sets, object-oriented logic programming etc.), in short, all forms of reasoning that are neither pure deduction, nor probabilistic. Advances on multi-agent reasoning are also not covered. I understand that to summarize AI in 1000 pages many important topics will not make the cut, but I feel, as a researcher on the topic for the past 25 years and lecturer on it for the past 15 years, that this 3rd edition contains obsolete stuff from the 80s (like frames, semantic networks, production systems, situation calculus, etc.) instead of their modern substitute listed above.

In short, after two Herculean efforts, it seems like the authors put far less work in this one. As a result, we are left without an truly comprehensive and up-to-date text to teach AI and agents. I hope the incoming text by David Poole will cover some of the weaknesses of this AIMA 2.1.

99 comments 338 people found this helpful. Was this review helpful to you?YesNo

Good in parts

ByTim Joslingon November 13, 2011

Format: Hardcover

This is a reasonable overview of AI - and an amazing achievement to have so much material in one book - but it is increasingly out of date. A lot of the techniques described at length could be fairly described as "Good Old Fashioned AI" and could have been shortened to make way for more powerful modern techniques. Other reviews have given specific details, but machine learning techniques in particular deserve more than one chapter. There is no mention in the index of "bias/variance tradeoffs", an important topic in which good progress has been made lately.

The changes in the third edition mostly amount to shuffling things around a bit. Only one chapter (Chapter 20) was substantially changed. Given the high price of the new edition it is probably not worth the money if you have an older edition. You would be better off to search out a specialised text or material on the web on the new techniques.

Reading the book superficially, it is quite informative and enjoyable. The reference list is very good. However I found when I tried to use and implement the algorithms described I ran into problems. Concretely:

* The pseudo-code is a strange mix of mathematical notation, Python-like code and prose. I found it very hard to turn it into real code, though I did succeed eventually in some cases. Apart from the undefined nature of the 'language', the variable names and function names chosen are often very uninformative and terse. You might have P (in bold) as one variable, and other p (in italics). Variable names like "var" and "value" abound. The pseudo-code does not follow the conventions described in the appendix. You need to have a high tolerance for frustration.

* The writing style is terse and mathematical. New notations are introduced freely and used hundreds of pages later without explanation e.g. the use of alpha as a normalizing factor in Bayesian calculations. There is no glossary of symbols that you can refer to. It is necessary to undertake a tedious search of the previous sections of the book, hoping for enlightenment.

Also there is much use of phrases such as "we therefore see". Often it is very unclear how we do "see" that the conclusion is true. Again the reason may be something that was covered several chapters ago (or in at least one case, in later pages). Perhaps this reflects the terse mathematical approach where you are presumed to have memorized the prior portions of the text, it is assumed you are used to absorbing new notations at a high rate, and that the greatest sin of all is to repeat yourself or to state the obvious.

My suggestion would be to borrow this book from a friend or a library to get an overview of Good Old Fashioned AI. Then read some course notes on machine learning (eg Stanford CS229) to get an update on machine learning. Then purchase specialized texts for areas you actually want to use. A lot of good material is legitimately available online eg Sutton's book on Reinforcement Learning but you are going to have to buy some books.

0Comment 49 people found this helpful. Was this review helpful to you?YesNo

Not big changes but still good

ByG. Sarriaon January 22, 2010

Format: HardcoverVerified Purchase

Artificial Intelligence: A Modern approach is a very good book which explores concepts in the area of AI. It covers most of the techniques in the area (there are some important AI techniques missing such as KDD and Data Mining), however it doesn't go deep in any concept so if you're looking for a specialized reference this is not the one.

The third edition of this book offers a few changes:
- a very updated list of references
- some (not many) new exercises
- they rewrote concepts in order to be up-to-date with the state of the art
- they changed the order of some chapters

All in all, it is still a very good introductory book, it is well-written and very easy to understand. If you are new in the field this is the first textbook to read.

11 comment 73 people found this helpful. Was this review helpful to you?YesNo

Most Recent Customer Reviews

Must for AI

Seminal work, fills in the core pieces of AI...

Published 7 days ago by Amazon Customer

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The contents of the book are great, but it is unfortunate how bad of a job they did in making the kindle version.

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A textbook for my graduate AI class. Easy to understand and contains a wide coverage of modern AI techniques. Must-read for anyone learning AI.

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Do not purchase

This is a terrible copy of the original book, it bears no resemblance to the "look inside preview" Formatting is atrocious and missing all figures and diagrams.

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The authors do a great job of explaining the concepts in language so easy ...

We use this book in our AI introductory class. The authors do a great job of explaining the concepts in language so easy to understand that complete newcomers to AI can...

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Three Stars

too heavy to take it with me

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awesome!!

Published 2 months ago by George

Five Stars

love it

Published 4 months ago by Pfau, Matthias

Great book !

Great book ! 1130 pages and yet to the point. I haven't read the first two editions so i cannot judge if there is a lot of new stuff in this 3th edition, but i feel like i am...Read more

Published 4 months ago by Henk Verhelle

Great intro to AI

Condensed knowledge on main topics in artificial intelligence, very easy to follow

Published 4 months ago by Isaac E.Gutierrez C.

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