Rating: 
Summary: A Good Introduction, But Not the Whole Enchilada
Review: 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 expert) players at several different games with quite quick play. If you want to build a game opponent, or do some basic expert-system logic, or take a stab at a simple neural net classifier, this book can get you started.There's a great deal more to AI than this book presents, and if you want to do anything really sophisticated this book won't get you there. Everyone I have ever met with this book had problems with the binding coming apart. Complaints to the publisher have not produced books with any better bindings. It makes it difficult to use as a reference, because the pages keep falling out. If this was just one copy, I wouldn't mention it, but I have seen at least 30 copies with the same problem.
Rating: 
Summary: You could do better
Review: ...I can only relate my experience from using this as a textbook for an introductory AI class with assigned topics. I think that the choice of material covered in this book is good, but I don't really think it's very clear in most places and there are not enough (or any) examples in most chapters. I have also used Winston's textbook for a different class, and felt that it was much clearer, as well as provided better relevant examples and problems. After having used both, I prefer Winston's text (ISBN 0201533774) to this one.
Rating: 
Summary: The optimal learning algorithm for learning A.I.
Review: 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. The treatment of conditional programming via the conditional partial-order planner (CPOP). 10. Representing knowledge in an uncertain domain and the semantics and inference in belief networks. 11. The brief discussions on stochastic simulation methods and fuzzy logic. 12. The discussion on computational learning theory 13. The treatment of neural networks, especially the discussion of multilayer feed-forward networks and the comparison between belief networks and neural networks. 14. The brief discussion on genetic algorithms and evolutionary programming. 15. The discussion on explanation-based learning and the technique of memoization. 16. The (excellent) overview of inductive logic programming. This relatively recent area was new to me at the time of reading so I appreciated the discussion. The authors briefly mention the approach of discovery systems and the Automated Mathematician (AM). 17. The interesting discussion of telepathic communication between robots via the exchange of internal representations. 18. The discussion on a formal grammar for a subset of English and the extensive treatment of natural language processing. 19. The discussion of speech recognition and the use of hidden Markov models and the Viterbi algorithm. 20. The fascinating discussion on robotics, particularly the treatment of configuration spaces, which brings in some techniques from computational geometry and topology. 21. The discussion on the philosophical ramifications of A.I. Future developments in A.I. will provide a unique testing ground for philosophy, in a way that will be unparalleled in the history of philosophy. Philosophers critical of A.I. will have the opportunity to check whether their arguments against the possibility of "strong A.I.", are in fact true.
Rating:
Summary: A Mile Wide and an Inch Deep
Review: 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. My main dislike about the book was that after reading it, I felt neither capable nor particularly motivated to set out and do my own experimentation in any of the topics covered, and this is, in my opinion, what a textbook should strive to achieve.
Rating:
Summary: Fantistic Introduction to an Interesting Field
Review: 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. The first few chapters cover intelligent agents, searching, and various search algorithms such as the basics like Depth-First and Breadth-First, and then the book introduces some more intelligent algorithms like A*, SMA*, Iterative Deepening, and a few more. Other topics included in the book are planning, logic (if you're new to logic, I might recommend some supplimentary material; it's very important to understand everything if you're interested in AI). I've read several introductory books on AI, and I would definately rate this one as the best!
Rating:
Summary: Ahead of its time; comprehensive; still quite readable
Review: This book covers an amazing array of AI topics. Few books cover predicate logic as well as neural networks and other stoicastic processes. The coverage is usually quite in depth yet still manages to be readable. The bibliography section will be helpful for further reading. It is not for cover to cover reading due to the nature of the subject but could be done with a bit of determination. The rewards for doing so would be well worth it.
Rating:
Summary: Very well written very consise introductory book on AI
Review: This book is very well written makes the complex subject a little easier to comprehend. The best thing about this book however is probably the integration of historical accomplishments inside the text. You don't just get an explanation on why something is and thats it, you get to see how an idea originated and evolved over time. I wish more computer science books integrated the history of the genre like this book does.
Rating: 
Summary: A Good Introductory Book
Review: This book gives a good introduction to AI. It covers several fields in AI. For someone thinking that AI is all about robots, image recognitions etc., this book shows that there could be very powerfull commercial applications of AI. It is very condensed and suits well as an introductory book for beginners.
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Summary: The best book in the field - unfortunately
Review: Had this book for two courses (one undergrad, one grad) and, while it is the best book in its field, it's still less than ideal. The principles and methods of AI do not seem to lend themselves well to encapsulation in one tome. The book's presentation of search and game playing is fine, and its introduction to the basic concepts of AI are worthwhile, but for practical use it is a bit lacking (Mitchell's "Machine Learning", for instance, far surpasses the portion of this book dedicated to the same topic.) One might be better off choosing a few texts on more specific topics in AI, rather than opting for one all-encompassing text with little detail.
Rating:
Summary: A Review of Russell and Norvig's AI: A Modern Approach
Review: 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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