Rating: 
Summary: Don't listen to those guys, this is a very good text
Review: I used this book in my AI class and found it to be a good text with excellent historical information and very approachable and understandable explanations of the material. I found it to be a competent treatment of all the major AI topics and I happily read the first 20 chapters. As it turned out, reading the book was a more rewarding experience than learning from the professor in lecture. :-)
Rating: 
Summary: Dont take a course with this book unless the teacher is good
Review: From a student perspective: A course using this book was taught for undergraduate seniors at the university I attend and I was thankful the teacher was knowledgeable in the subject. After our first homework assignment was turned in the teacher abandoned the questions in the book and generated his own. I thought an answer key for every odd question would have helped, but stating the questions more clearly would have worked much better. The ambiguity of the questions made it hard to solidify whether or not one has a grasp on the concepts presented in the book. END
Rating: 
Summary: OK book with poorly written problems ending each chapter
Review: The entire class has had difficulties with the problems in each chapter. It is difficult to determine what they want as an answer, the questions can be answered at many different levels and can get messy. Overall the biggest improvement they could make to this book would be the addition of more examples. The only saving grace I've seen so far in this book has been the interesting bits of history thrown in here and there. If you are a student and the book is required there isn't much you can do, but if not there has to be a better book out there.
Rating: 
Summary: Visit the home page for the book:
Review: The home page is http://www.cs.berkeley.edu/~russell/aima.htm
Rating:
Summary: Encyclopedic
Review: 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.
Rating:
Summary: the book is an excellent material
Review:
the book enters all the aspects of AI today and tomorow.
Rating: 
Summary: Stunning textbook--best I've ever used
Review: Until recently, my Algorithms book was my favorite text book ever. However, AI: A Modern Approach has supplanted it. This book is the most thoughtfully designed, easily understandable, clear text I've ever used in over 28 years of attending schools. I really knew nothing about AI when I took my first grad class in AI, but this book, along with a pretty great instructor, has been a wonderful resource, more than any other book I've used. I have not need to google for more information or speak to the professor. The answers are here--clear and concrete.
Have no fear and trust this book!
Rating:
Summary: Rigorous view of AI
Review: The book offers mathematically based coverage of the most important topics in AI. It has a very pragmatic view and does not spend too much time with the over-hyped topics, such as fuzzy logic, genetic algorithms. Instead, it provides clear and easy to understand examples for most of the introduced concepts.
The text requires some knowledge of computer science, mathematics, and statistics. Sections on topics that require extensive outside knowledge, such as computational learning theory, are not covered very deeply, but usually offer nice insight to the concepts and basic principles.
I recommend this book both for beginners to exact AI as an introductory text and to more experienced researchers as an invaluable reference. I found it to be the most useful book I own.
Rating:
Summary: TRIVIAL EXAMPLES
Review: While the substance of the material is definately present, beware the examples! This text is yet another example of how as the difficulty of the material increases, the complexity of the examples given decreases. Be prepared for trivial examples in the chapter and complex questions in the exercises - why are you wasting our time Russell and Norvig??? If you are an instructor thinking of using this book, PLEASE be prepared to create examples OF YOUR OWN. It will not be sufficient to lecture directly from the text. If you are a student taking a class that uses this text, GOOD LUCK! I hope you have a good TA, or a lot of time to waste solving problems without examples.
| 
