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on February 3, 2010
- With AIMA 1st Edition, I had relearned AI anew from a fresh, insightful and wonderfully pedagogical perspective.
Best computer science textbook ever.
- With AIMA 2nd Edition, I got a lot of recent advances in AI brought to me in the same way, even if presented at times in a way that was too concise for a textbook, and read more like an encyclopedia.
Yet, great 2nd Edition.

- This 3rd Edition is alas AIMA 2.1 and not the AIMA 3.0 that I was waiting for. The new material and new insightful way to organize past material are both scant. Certainly not worth the price for those who own the 2nd Edition.

Don't get me wrong, if you are about to buy your first AI textbook, this is a great buy as it is still light years ahead of the competition. But some chapters that were getting really thin and outdated in 2009 did not get significant updating.

This is particularly true for knowledge representation. Missing are all the recent yet already consolidated advances brought about by the new solutions to the frame problem (such as the fluent calculus), default reasoning, abduction-based and case-based diagnosis, rule-based reasoning (such as constraint handling rules, answer sets, object-oriented logic programming etc.), in short, all forms of reasoning that are neither pure deduction, nor probabilistic. Advances on multi-agent reasoning are also not covered. I understand that to summarize AI in 1000 pages many important topics will not make the cut, but I feel, as a researcher on the topic for the past 25 years and lecturer on it for the past 15 years, that this 3rd edition contains obsolete stuff from the 80s (like frames, semantic networks, production systems, situation calculus, etc.) instead of their modern substitute listed above.

In short, after two Herculean efforts, it seems like the authors put far less work in this one. As a result, we are left without an truly comprehensive and up-to-date text to teach AI and agents. I hope the incoming text by David Poole will cover some of the weaknesses of this AIMA 2.1.
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on September 18, 2011
This is an excellent book however I cannot recommend purchasing the Kindle version of this text. It is atrocious. There are subject headings inserted after the subject is spoken about and, quite often, many heading stacked up on top of each other taking up almost an entire page with useless titles that are in the wrong order anyway. There are no page numbers, which is unacceptable for a text that is used by many college AI programs across the country. There are tons of hyphenation errors. The delineations between figure notes and the text are almost imperceptible so it is difficult to tell what text goes where. In general it is difficult to read and navigate due to this horrible Kindle conversion.
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on January 22, 2010
Artificial Intelligence: A Modern approach is a very good book which explores concepts in the area of AI. It covers most of the techniques in the area (there are some important AI techniques missing such as KDD and Data Mining), however it doesn't go deep in any concept so if you're looking for a specialized reference this is not the one.

The third edition of this book offers a few changes:
- a very updated list of references
- some (not many) new exercises
- they rewrote concepts in order to be up-to-date with the state of the art
- they changed the order of some chapters

All in all, it is still a very good introductory book, it is well-written and very easy to understand. If you are new in the field this is the first textbook to read.
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on November 13, 2011
This is a reasonable overview of AI - and an amazing achievement to have so much material in one book - but it is increasingly out of date. A lot of the techniques described at length could be fairly described as "Good Old Fashioned AI" and could have been shortened to make way for more powerful modern techniques. Other reviews have given specific details, but machine learning techniques in particular deserve more than one chapter. There is no mention in the index of "bias/variance tradeoffs", an important topic in which good progress has been made lately.

The changes in the third edition mostly amount to shuffling things around a bit. Only one chapter (Chapter 20) was substantially changed. Given the high price of the new edition it is probably not worth the money if you have an older edition. You would be better off to search out a specialised text or material on the web on the new techniques.

Reading the book superficially, it is quite informative and enjoyable. The reference list is very good. However I found when I tried to use and implement the algorithms described I ran into problems. Concretely:

* The pseudo-code is a strange mix of mathematical notation, Python-like code and prose. I found it very hard to turn it into real code, though I did succeed eventually in some cases. Apart from the undefined nature of the 'language', the variable names and function names chosen are often very uninformative and terse. You might have P (in bold) as one variable, and other p (in italics). Variable names like "var" and "value" abound. The pseudo-code does not follow the conventions described in the appendix. You need to have a high tolerance for frustration.

* The writing style is terse and mathematical. New notations are introduced freely and used hundreds of pages later without explanation e.g. the use of alpha as a normalizing factor in Bayesian calculations. There is no glossary of symbols that you can refer to. It is necessary to undertake a tedious search of the previous sections of the book, hoping for enlightenment.

Also there is much use of phrases such as "we therefore see". Often it is very unclear how we do "see" that the conclusion is true. Again the reason may be something that was covered several chapters ago (or in at least one case, in later pages). Perhaps this reflects the terse mathematical approach where you are presumed to have memorized the prior portions of the text, it is assumed you are used to absorbing new notations at a high rate, and that the greatest sin of all is to repeat yourself or to state the obvious.

My suggestion would be to borrow this book from a friend or a library to get an overview of Good Old Fashioned AI. Then read some course notes on machine learning (eg Stanford CS229) to get an update on machine learning. Then purchase specialized texts for areas you actually want to use. A lot of good material is legitimately available online eg Sutton's book on Reinforcement Learning but you are going to have to buy some books.
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on April 21, 2010
I have worked with this book during two courses I have had on AI, and I must say that this is definitely one of the best textbooks I have read in the field of computer science and algorithms. The book thoroughly covers subjects from search algorithms, reducing problems to search problems, working with logic, planning, and more advanced topics in AI such as reasoning with partial observability, machine learning and language processing. I have not yet had time to study the more advanced topics, but I can say that the first half of the book dealing with searching, logic and planning are very well written and understandable by most students who know basic programming. Algorithms and data structures are mostly introduced along the way, but some prior knowledge, such as knowing the basics of graph theory etc., is probably an advantage.

The book is mostly written in a concise and easily digestible language, but some sections could probably have been written in fewer words.

Overall, this book is one of my favorite textbooks!
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on November 19, 2011
This is the best introductory review to Artificial Intelligence on the market. It's very well written and organized. There are other books that are better for focusing on one particular aspect of AI, but as a general book this is the best I've seen. If you are looking for a really good introductory textbook to AI that does not completely dumb things down, buy this book.

Most of the negative comments about this book come from people stating that it's not a big enough update from the prior edition. While it's true that the entire field of AI has not been completely updated since the last edition, it's also not the case that this book comes out with new editions with the frequency of some Calculus or Economics textbooks where new editions seem to come out purely to ensure students can't buy a used book for the course. The updates are substantial. Whether the new edition gives you enough extra to want to buy it if you own the old edition is a decision only you can make for yourself after spending some time at the website for the book aima.cs.uberkeley.edu.

The Kindle conversion of this book is absolutely horrendous. I prefer to buy electronic copies of books if possible so I don't have to carry a heavy hard copy around since I often read while commuting. I would not recommend that for this book, even though at a 1000+ textbook sized pages, it is a pretty substantial book. Fortunately a friend of mine had bought a Kindle copy of the book and I was able to see how bad it was and I bought the hard copy.

I recently got an email telling me Amazon was sending out an updated version of the Kindle version of Steve Job's biography because the conversion hadn't been done properly. They really need to do that for this book. Once done it may be a good idea to state on the website that the Kindle conversion has been fixed.
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on October 16, 2011
I loaded the Kindle version up on my iPad. Great. But I needed it on my laptop (Mac) for class. I tried to load it with Kindle for Mac and it claims it has exceeded the maximum allowed licenses. I am extremely pissed. I bought the hard back for a small fortune and only slightly smaller fortune for e-access and I can't even read it on a measly two devices? This is totally and completely unacceptable.
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on November 23, 2011
While this book is a solid reference text, I can't recommend it as an undergraduate text for an intro to AI.

In the machine learning class I took, my friends and I pretty much gave up trying to read it and just went with the professor's notes. The book is a very arduous read. It has a tendency to formalize each and every single last little idea, which becomes overwhelming and tedious really quick. Also some chapters could have been more compact. Chapter 2 is about 25 pages, yet I felt I walked away with about 2 pages of useful info--it was very wordy.

Teaching this subject matter is no doubt a huge challenge as the material covers vast territory. But what's needed at the undergraduate level is a more teaching-oriented friendly text.
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on September 7, 2011
Update Jan 2013
--------------
The new Kindle version '(3/e) [Print Replica]' fixes all the previous typesetting problems which were in the original '(3/e)' version. Make sure you get this new version.

Unfortunately for the owners of '(3/e)', Amazon has classified the update as a different product, even though the content is identical; currently no upgrade is available.

The new version gets 5 stars.

Original Review
-------------
This is not a review of the book contents, but of the Kindle format for this book.

1. I bought the Kindle version to read on both my iPhone (primarily) and computer. Unfortunately this book is not currently formatted for use on iPhone, but it is for other others. I now know to check the "available for these devices" section. Hopefully an iPhone format will be made available.

2. In the printed text, what are probably helpful concept tags in margin are in the Kindle version a complete nuisance. These are formatted in-line (not in the margin), making for some confusing reading and rendering the tags completely ineffective. It would be better to strip them altogether, especially since these terms are already in bold-face in the text. For example:

<quote>
3.3 SEARCHING FOR SOLUTIONS
Having formulated some problems, we now need to solve them. A solution is an action sequence, so search algorithms work by considering various possible action sequences. The possible action sequences starting at the initial state form a search tree with the initial state at the root; the branches are actions and the nodes correspond to states in the state space of the problem. Figure 3.6 shows the first few steps in growing the search tree for finding a route from Arad to Bucharest. The root node of the tree corresponds to the initial state, In(Arad). The first step is to test whether this is a goal state. (Clearly it is not, but it is important to check so that we can solve trick problems like "starting in Arad, get to Arad.") Then we need to consider taking various actions. We do this by expanding the current state; that is, applying each legal action to the current state, thereby generating a new set of states. In this case, we add three branches from the parent node In(Arad) leading to three new child nodes: In(Sibiu), In(Timisoara), and In(Zerind). Now we must choose which of these three possibilities to consider further.
______________
SEARCH TREE
______________
NODE
______________
EXPANDING
______________
GENERATING
______________
PARENT NODE
______________
CHILD NODE
______________
</quote>

You get the picture. In sections with many new terms this nonsense can consume half the page.

3. Same problem as #2, but with the 'pointed finger' symbol to draw attention to an important point. Having the symbol show up between paragraphs, rather than in the margin pointing at the actual text is useless. Besides the text is already italicized.

4. Text underneath figures needs a line break before continuing with the normal text, as it blends in.

No sure if Amazon or the publisher does the electronic conversion, but please fix these! Clearly an editor or better conversion AI is required. As much as I like electronic books, I would have gone with the printed copy for this one given the current state.

Update 2/23/2012:
-----------------
There is now an iOS compatible format, but the same formatting problems remain.
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on February 24, 2011
I just recently took a course on AI using this as text book and just read about half of its chapters. Awesome introduction covering most of the AI topics, with very easy to understand description of the concepts.

While the book doesn't go in depth in any of the the major topics -- there are whole books dedicated to each (NLP, probabilistic models, machine learning, computer vision, etc.) -- it's enough to grasp what those topics are about. At the end of each chapter it provides a very interesting and entertaining discussion of the current state of the art.
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