Artificial Intelligence: A Modern Approach (3rd Edition) [Hardcover]

Stuart Russell (Author), Peter Norvig (Author)

Book Description

Publication Date: December 11, 2009 | ISBN-10: 0136042597 | ISBN-13: 978-0136042594 | Edition: 3

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.

Editorial Reviews

About the Author

Stuart Russell was born in 1962 in Portsmouth, England. He received his B.A. with first-class honours in physics from Oxford University in 1982, and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is a professor of computer science, director of the Center for Intelligent Systems, and holder of the Smith–Zadeh Chair in Engineering. In 1990, he received the Presidential Young Investigator Award of the National Science Foundation, and in 1995 he was cowinner of the Computers and Thought Award. He was a 1996 Miller Professor of the University of California and was appointed to a Chancellor’s Professorship in 2000. In 1998, he gave the Forsythe Memorial Lectures at Stanford University. He is a Fellow and former Executive Council member of the American Association for Artificial Intelligence. He has published over 100 papers on a wide range of topics in artificial intelligence. His other books include The Use of Knowledge in Analogy and Induction and (with Eric Wefald) Do the Right Thing: Studies in Limited Rationality.

Peter Norvig is currently Director of Research at Google, Inc., and was the director responsible for the core Web search algorithms from 2002 to 2005. He is a Fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. Previously, he was head of the Computational Sciences Division at NASA Ames Research Center, where he oversaw NASA’s research and development in artificial intelligence and robotics, and chief scientist at Junglee, where he helped develop one of the first Internet information extraction services. He received a B.S. in applied mathematics from Brown University and a Ph.D. in computer science from the University of California at Berkeley. He received the Distinguished Alumni and Engineering Innovation awards from Berkeley and the Exceptional Achievement Medal from NASA. He has been a professor at the University of Southern California and a research faculty member at Berkeley. His other books are Paradigms of AI Programming: Case Studies in Common Lisp and Verbmobil: A Translation System for Faceto-Face Dialog and Intelligent Help Systems for UNIX.

Product Details

• Hardcover: 1152 pages
• Publisher: Prentice Hall; 3 edition (December 11, 2009)
• Language: English
• ISBN-10: 0136042597
• ISBN-13: 978-0136042594
• Product Dimensions: 8 x 1.8 x 10 inches
• Shipping Weight: 4.4 pounds (View shipping rates and policies)
• Average Customer Review: 3.6 out of 5 stars  See all reviews (51 customer reviews)
• Amazon Best Sellers Rank: #15,595 in Books (See Top 100 in Books)
  • #7 in Books > Computers & Technology > Computer Science > Artificial Intelligence > Human Vision & Language Systems

Most Helpful Customer Reviews

250 of 268 people found the following review helpful

2.0 out of 5 stars A disappointment: minor update not worth the money February 3, 2010

By Damon Deville

Format:Hardcover|Amazon Verified 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. Read more ›

60 of 62 people found the following review helpful

2.0 out of 5 stars Great book, terrible Kindle conversion September 18, 2011

By Sean Walker

Format:Kindle Edition|Amazon Verified 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.

57 of 59 people found the following review helpful

4.0 out of 5 stars Not big changes but still good January 22, 2010

By G. Sarria

Format:Hardcover|Amazon Verified 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.

20 of 21 people found the following review helpful

4.0 out of 5 stars A classic, thoroughly covering a wide range of AI topics April 21, 2010

By Allan Riordan Boll

Format:Hardcover

I have worked with this book during two courses I have had on AI, and I must say that this is definitely one of the best textbooks I have read in the field of computer science and algorithms. The book thoroughly covers subjects from search algorithms, reducing problems to search problems, working with logic, planning, and more advanced topics in AI such as reasoning with partial observability, machine learning and language processing. I have not yet had time to study the more advanced topics, but I can say that the first half of the book dealing with searching, logic and planning are very well written and understandable by most students who know basic programming. Algorithms and data structures are mostly introduced along the way, but some prior knowledge, such as knowing the basics of graph theory etc., is probably an advantage.

The book is mostly written in a concise and easily digestible language, but some sections could probably have been written in fewer words.

Overall, this book is one of my favorite textbooks!

18 of 20 people found the following review helpful

1.0 out of 5 stars Warning: Extra Kindle DRM on this title October 16, 2011

By Samantha Atkins

Format:Kindle Edition|Amazon Verified Purchase

I loaded the Kindle version up on my iPad. Great. But I needed it on my laptop (Mac) for class. I tried to load it with Kindle for Mac and it claims it has exceeded the maximum allowed licenses. I am extremely pissed. I bought the hard back for a small fortune and only slightly smaller fortune for e-access and I can't even read it on a measly two devices? This is totally and completely unacceptable.

14 of 15 people found the following review helpful

3.0 out of 5 stars Good in parts November 13, 2011

By Tim Josling

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. Read more ›