Rating: 
Summary: Good general overview
Review: The field of artificial intelligence has an interesting history, both in terms of its content and the philosophical debate it has provoked. The field could also be loosely described as divided into two camps, those who view it as a collection of highly sophisticated algorithms, and those who view it as an attempt to create machines that exhibit human-level intelligence. Ironically, in the latter camp, it is difficult to assess the progress that has been made, since criteria for measuring machine intelligence are never explicitly given. Instead, dependence has been made on the "Turing test" for intelligence, a test that is difficult to apply, and in fact can be said to be too vague for a practical, objective assessment of machine intelligence. This book is written more in the context of the latter camp, than in the former. However, in-depth discussion of the Turing test is not given, and this actually is one of the main virtues of the book, although the author clearly believes that the purpose of doing research in artificial intelligence is to achieve human-level intelligence. As he remarks in the last paragraph in the book, it was written to overview the techniques that he believes are required to achieve human-level intelligence. Although he does not explicitly give the reader tests for machine intelligence that will allow progress to be measured, he devotes a small portion of the book to various ideas on just what constitutes intelligence. The book also gives a general (and sometimes very brief) overview of the algorithms used in artificial intelligence. Search heuristics, neural networks, and genetic programming are some of the topics that are covered. The influence of the "intelligent agent" paradigm, that is now taking the AI community by storm, is very apparent throughout the book. The author though does not neglect some of the topics in "good-ole-fashioned" artificial intelligence that arose decades ago and is still applicable today, especially in the field of logic programming. These topics include resolution in both the propositional and predicate calculus, and in expert systems. By far the best discussion in the book is on knowledge-based systems and evolving knowledge bases. This topic has taken on considerable importance in recent years due to the importance of data mining and business intelligence. Readers who are considering artificial intelligence as a career choice will find good motivation by reading this book. The field also is quite different than most others in that it respects a high degree of individual creativity and ingenuity, and has a high bandwidth for new ideas. Beginning with its origins in the 1950s, the field has grown by leaps and bounds, but its applications have exploded in the last five years, fueled mainly by business and financial applications. Concerned not only with achieving human-level capabilities, but also with other forms of intelligence and how they can be useful, artificial intelligence has become one of the predominant forces in the twenty-first century. One can only be excited and optimistic about its further advances.
Rating: 
Summary: Russell and Norvig Exponentially Better
Review: The title of this review speeks for itself. Unfortunately I had to buy this book (Nilsson) for a course. I still refer to it at times, but only as a last resort. If you have a choice go with Russell-Norvig text.
Rating:
Summary: Russell and Norvig Exponentially Better
Review: The title of this review speeks for itself. Unfortunately I had to buy this book (Nilsson) for a course. I still refer to it at times, but only as a last resort. If you have a choice go with Russell-Norvig text.
Rating:
Summary: teases, but never delivers
Review: This book was frustrating in some ways. It start giving details about parts of AI, then it stops. It covers to much material, and with not enough detail. It does list the references, so you can always go there and get more detail. If you have a decent background in calculus, then the mathematics in this book should not be that daunting. If you are a philosophy major trying to learn about AI, then you might find this book difficult. I really don't see much difference between this book and some of the other intro to AI books on the market. They try to cover to much material and something is bound to be left uncovered or superficially covered.
Rating: 
Summary: Terse, Compact and Fast
Review: This is generally a good book with an effective unifying theme. However, each chapter is very dense, terse and presents complex material very quickly. This would not be my recommendation for a first book on AI. Author assumes the reader has a lot of preliminary informatin.
Rating: 
Summary: confusing, poorly written
Review: This is one of the worst textbooks I have ever had to use. It is confusing, poorly written, and incomplete. Russell and Norvig contains virtually all this material (plus much more) but present it in a much clearer way.
Rating:
Summary: Not bad but mis-titled
Review: Where is the synthesis? I didn't see it. The material is too dense for beginners, too scattered for the non-beginner.
There are several places where the author states something absolutely intriguing and moves on in the next sentence. For example, after much discussion of handling probability, he states that Bayesian networks are probably the way to go. And that's all that was said, "Wait, wait" I wanted to shout. "Tell me more about why you believe that!" But alas that's all there is.
Also, this book is probably too detailed for non-programmers, not detailed enough for programmers.
Rating:
Summary: Not a good intro to AI
Review: While the book is well organised and number of topics covered is substantial, this was the worst intro-to-anything book I had to suffer through. If calculus is something you are very comfortable with, then go ahead, read it. :-)
| 
