Artificial Intelligence: A Modern Approach Hardcover – January 1, 1995

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.

Well printed, excellent resource on fundamental methods for artificial intelligence and machine learning. Easy to locate specific methods. Easy to understand. Part of my permanent reference bookshelf.

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 pseudo code.

GREAT work

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 abstract and meaningless jargon and expressions of the AI.Why can't we find clear allegories in this book? why can't we find clear examples, flow-charts and other simple graphic illustrations on how to code an AI algorithm while suffering the minimum.

Great book on artificial intelligence with many exercise problems. It goes into details but not design for beginners neeed prior knowledge of discrete math also need to know proofs . I will update as i finish the book

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.

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.