Rating: 
Summary: nice, but with these errors
Review: A nice book. Especially the order in which the topics are covered is a good idea. However, you will not find the following errors reported in the book's webpage:Page 52: The "high-degree function" is not a function! Page 92: In Figure 6.6, the topmost pixels that get deleted as a result of the averaging operation should actually remain there, since both their sums are 4, which is greater than the threshold, which is 3. Page 100: In Fig. 6.13, the last row of the last image contains a spurious image boundary. Page 151: In Fig. 9.8, there are two nodes with name n; the one which is higher in the figure should have the subscript 1. Page 152, item 3 in the list: There is an implicit assumption that h-hat always returns 0 for goal states. I don't think that this assumption is stated earlier in the text. Page 165: In Figure 10.1, all arrows are supposed to be pointing away from the current state. Page 246: The last paragraph mentions ".. the two interpretations for Clear and On suggested by Fig. 15.2", but aren't actually THREE interpretations suggested for On? And in the current errata list in the book's website, something is clearly wrong with item 6, since it says n_i should be replaced by n_i. All in all, a good book.
Rating:
Summary: An interesting read for the advanced students
Review: According to my former AI prof, Nilsson suffers from "Physics envy." Given that AI is a fairly new, fairly splintered facet of Computer Science research, there is a relative absence of quantitative analyses of the subject to rival such other fields as chemistry, biology, or physics. As such, Nilsson resorts to quantifying most every piece of data or concept in the book. In some cases, his formulas can more lucidly be explained in words or simple algebra, rather than polynomic summations and calculus. Nevertheless, for the non-beginning student of computer science that has an interest in the subject, this book covers the gamut of AI subjects. Topics include neural networks, genetic programming, multi-agent programming, fuzzy logic, and machine vision. While no topic is covered in-depth, the broad scope of the book allows one entering the field to decide what areas, if any, are of paramount interest. I recommend this book for a 2nd or 3rd year CS undergraduate with a background in calculus and with a serious interest in artificial intelligence.
Rating: 
Summary: good job Nilsson
Review: According to my former AI prof, Nilsson suffers from "Physics envy." Given that AI is a fairly new, fairly splintered facet of Computer Science research, there is a relative absence of quantitative analyses of the subject to rival such other fields as chemistry, biology, or physics. As such, Nilsson resorts to quantifying most every piece of data or concept in the book. In some cases, his formulas can more lucidly be explained in words or simple algebra, rather than polynomic summations and calculus. Nevertheless, for the non-beginning student of computer science that has an interest in the subject, this book covers the gamut of AI subjects. Topics include neural networks, genetic programming, multi-agent programming, fuzzy logic, and machine vision. While no topic is covered in-depth, the broad scope of the book allows one entering the field to decide what areas, if any, are of paramount interest. I recommend this book for a 2nd or 3rd year CS undergraduate with a background in calculus and with a serious interest in artificial intelligence.
Rating:
Summary: An interesting read for the advanced students
Review: According to my former AI prof, Nilsson suffers from "Physics envy." Given that AI is a fairly new, fairly splintered facet of Computer Science research, there is a relative absence of quantitative analyses of the subject to rival such other fields as chemistry, biology, or physics. As such, Nilsson resorts to quantifying most every piece of data or concept in the book. In some cases, his formulas can more lucidly be explained in words or simple algebra, rather than polynomic summations and calculus. Nevertheless, for the non-beginning student of computer science that has an interest in the subject, this book covers the gamut of AI subjects. Topics include neural networks, genetic programming, multi-agent programming, fuzzy logic, and machine vision. While no topic is covered in-depth, the broad scope of the book allows one entering the field to decide what areas, if any, are of paramount interest. I recommend this book for a 2nd or 3rd year CS undergraduate with a background in calculus and with a serious interest in artificial intelligence.
Rating: 
Summary: Varies between being superficial and incomprehendable
Review: After having borrowed and read part of Nilsson's previous book "Principles of Artificial Intelligence" at the library some years back I was quite positive about the prospect of reading this one. However, it falls short on many of my expectations and can therefore not be recommended for neither the beginner nor the expert. The book covers all the major areas of artificial intelligence but does so in a very superficial manner. There isn't actually enough information in the book at allow to to implement some of the techniques available - it is mostly teasers. Also many of the subjects are - and even some of the subjects that I already knew about beforehand - incomprehendable and I often got more confused about a subject than before I began reading it. I very rarely give a book one star, but this one deserves it in the light of the many better books on AI. I recommend that you read "Russell and Norvig: Artificial Intelligence - A Modern Approach" instead. Jacob Marner, M.Sc.
Rating:
Summary: good job Nilsson
Review: AI is the future and I believe Nilsson is one of the best experts of AI.. thank you....
Rating:
Summary: It might be called "THE" book
Review: Although I do not recommend this book as a first one (because the material is too much dense for a begginer), it undoubtedly is a "must" in any set of books if you intend seriously working inside this area. Nilsson has a clear writing and (if you have some background) it's a pleasure to follow him even through hard topics.
Rating:
Summary: Run Forrest Run
Review: In general avoid this book.
I purchased this book for a course, and unfortunately this is my first book. Its 95% maths, of course AI is a lot of math, but the book is so abstract and nothing related to practical stuff. Take convolution filters, it gives integrals and all that stuff, but what exactly does it do, how does it perform it on images, and where the heck are sample images, and sample matricies.
I bet this author must have sent this book out to teachers so that 50 students would have to buy this over priced book with no practicle use and so hard to read/understand and extremely dense.
Rating:
Summary: AI from the "intelligent agent" perspective
Review: Nilsson's book presents all major areas of artificial intelligence from the unifying perspective of the problem of constructing an intelligent agent. Many of the important subfields of AI are introduced, including machine learning. The book is clearly written and can be understood if you have a good knowledge of computer science. (Yon certainly don't need a doctorate degree in mathematics, as claimed by anothe reviewer).
Rating:
Summary: It sucks! DON'T BUY IT
Review: Over priced for such an useless book, no places on internet to help you find anything for help with this book, Poor examples and not enough of them, badly written, too confusing for the normal student, you must have a doctorate degree in math to understand the material, The Bible is easier to read and understand, not for intro courses, if your professor requires the book -> drop the course! Why do you think amazon lists the other books students bought in addition to this book? You been warned!
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