- Hardcover: 1152 pages
- Publisher: Pearson; 3 edition (December 11, 2009)
- Language: English
- ISBN-10: 0136042597
- ISBN-13: 978-0136042594
- Product Dimensions: 8 x 1.7 x 10.1 inches
- Shipping Weight: 4.8 pounds (View shipping rates and policies)
- Average Customer Review: 206 customer reviews
- Amazon Best Sellers Rank: #23,926 in Books (See Top 100 in Books)
Enter your mobile number or email address below and we'll send you a link to download the free Kindle App. Then you can start reading Kindle books on your smartphone, tablet, or computer - no Kindle device required.
To get the free app, enter your mobile phone number.
Artificial Intelligence: A Modern Approach (3rd Edition) 3rd Edition
Use the Amazon App to scan ISBNs and compare prices.
All Books, All the Time
Read author interviews, book reviews, editors picks, and more at the Amazon Book Review. Read it now
Frequently bought together
Customers who bought this item also bought
Customers who viewed this item also viewed
From the Back Cover
The long-anticipated revision of this #1 selling book offers the most comprehensive, state of the art introduction to the theory and practice of artificial intelligence for modern applications.Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics.For computer professionals, linguists, and cognitive scientists interested in artificial intelligence.
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.
Author interviews, book reviews, editors picks, and more. Read it now
Top customer reviews
There was a problem filtering reviews right now. Please try again later.
The biggest difference is the absence of the last two chapters. The U.S. edition includes: Chapter 26, "Philosophical Foundations", which covers arguments over consciousness in machines and the possibility of robot uprisings; and Chapter 27, "AI: The Present and Future", which *briefly* describes some things AI researchers need to work on before we can build a "general-purpose intelligent agent" (a.k.a. one single AI that will be good enough at a lot of different tasks). These two chapters are interesting, but I wouldn't call them core material, so I'm not surprised they got left out.
Other than that, the differences are astonishingly minor. The chapters are unnumbered, and some of them swapped places for no reason, but all the content from chapters 1 through 25 is here, plus the two appendixes. The ONLY edits to the text are removals of cross-chapter references. The U.S. version will say something like, "When we discussed whatzits in Chapter 4, we mentioned that they come in two flavors, X and Y", while the same line in this book will say, "Whatzits come in two flavors, X and Y". Again, those are the ONLY edits. All the other sentences are the same. The equations are the same. The diagrams are the same. The exercises at the end of each chapter are the same.
In short, if you can do without those last two chapters, buy this version and save your money.
However, it's big for a reason. This book is filled with every possible thing you might want to know in the field of AI. It is all-encompassing from what I've seen. It also explains things very well in my opinion. Things are clear. You can clearly go through it, and although the math might need some note-taking, the book generally makes it easy for you to understand with examples. If you want to start delving into AI, machine learning, etc, but feel daunted about all the different crazy terms they use (adverserial networks, loss functions, overfitting, etc.), this is a good book to introduce to all this. It certainly did for me (Chapter 18: Machine Learning)
Most of the negative comments about this book come from people stating that it's not a big enough update from the prior edition. While it's true that the entire field of AI has not been completely updated since the last edition, it's also not the case that this book comes out with new editions with the frequency of some Calculus or Economics textbooks where new editions seem to come out purely to ensure students can't buy a used book for the course. The updates are substantial. Whether the new edition gives you enough extra to want to buy it if you own the old edition is a decision only you can make for yourself after spending some time at the website for the book aima.cs.uberkeley.edu.
The Kindle conversion of this book is absolutely horrendous. I prefer to buy electronic copies of books if possible so I don't have to carry a heavy hard copy around since I often read while commuting. I would not recommend that for this book, even though at a 1000+ textbook sized pages, it is a pretty substantial book. Fortunately a friend of mine had bought a Kindle copy of the book and I was able to see how bad it was and I bought the hard copy.
I recently got an email telling me Amazon was sending out an updated version of the Kindle version of Steve Job's biography because the conversion hadn't been done properly. They really need to do that for this book. Once done it may be a good idea to state on the website that the Kindle conversion has been fixed.
If I could return the ebook and get the paperback, I would.
- Missing many chapters
- Many blank pages
- Order jumbled up , so you can't read in sequence