- Hardcover: 1132 pages
- Publisher: Prentice Hall; 2 edition (December 30, 2002)
- Language: English
- ISBN-10: 0137903952
- ISBN-13: 978-0137903955
- Product Dimensions: 8.3 x 1.8 x 10.1 inches
- Shipping Weight: 4.8 pounds
- Average Customer Review: 70 customer reviews
- Amazon Best Sellers Rank: #135,891 in Books (See Top 100 in Books)
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Artificial Intelligence: A Modern Approach (2nd Edition) 2nd Edition
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"The publication of this textbook was a major step forward, not only for the teaching of AI, but for the unified view of the field that this book introduces. Even for experts in the field, there are important insights in almost every chapter." Prof. Thomas Dietterich, Oregon State
"Just terrific. The book I've always been waiting for...the AI bible for the next decade." Prof. Gerd Brewka (Vienna)
"A marvelous achievement, a truly beautiful book!" Prof. Selmer Bringsjord, RPI
"It's a great book, with incredible breadth and depth, and very well-written. Everyone I know who has used it in their class has loved it." Prof. Haym Hirsh, Rutgers
"I am deeply impressed by its unprecedented quality in presenting a coherent, balanced, broad and deep, enjoyable picture of the field of AI. It will become tire standard text for the years to come." Prof. Wolfgang Bibel, Darmstadt
"Terrific! Well-written and well-organised, with comprehensive coverage of the material that every AI student should know." Prof. Martha Pollack (Michigan)
"Outstanding ...Its descriptions are extremely clear and readable; its organization is excellent; its examples are motivating; and its coverage is scholarly and throughout! ...will deservedly dominate the field for some time." Prof. Nils Nilsson, Stanford
"The best book available now...It's almost as good as the book Charniak and I wrote, but more up to date. (Okay I'll admit it, it may even be better than our book.)" Prof. Drew McDermott, Yale
"A magisterial wide scope account of the entire field of Artificial Intelligence that will enlighten professors as well as students." Dr. Alan Kay
"This is the book that made me love AI." Student (Indonesia)
From the Back Cover
The first edition of Artificial Intelligence: A Modern Approach has become a classic in the AI literature. It has been adopted by over 600 universities in 60 countries, and has been praised as the definitive synthesis of the field.
In the second edition, every chapter has been extensively rewritten. Significant new material has been introduced to cover areas such as constraint satisfaction, fast propositional inference, planning graphs, internet agents, exact probabilistic inference, Markov Chain Monte Carlo techniques, Kalman filters, ensemble learning methods, statistical learning, probabilistic natural language models, probabilistic robotics, and ethical aspects of AI.
The book is supported by a suite of online resources including source code, figures, lecture slides, a directory of over 800 links to "AI on the Web," and an online discussion group. All of this is available at:
Top customer reviews
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Otherwise this is a great CS book. Yes there is some math in it, but don't be scared - there is an appendix with all necessary mathematical background you'll need (and you don't need much). I was surprised to see so much historical references in this book, it teaches you not just about most major branches of AI, but also about how they started and where originated from in a "problem -> solution" form. For instance when they talk about genetic algorithms they actually go ahead and write a comprehensive comparison of analogies between biological evolution, genes and their computer-generated counterparts referencing the original work of Darwin and others.
If you're into AI, applied mathematics or computer science, I have no doubt you'll enjoy this book: it's not too focused on something specific (and something you'd need a PhD to understand) while not too shallow and covers fairly wide spectrum of AI problems, including (!) ethical and philosophical issues like "what happens if we succeed?"
My only complaint so far (not having finished the entire book) is that some of the definitions in chapter 17's whirlwind introduction to game theory were a little vague. But, a quick look at some other sources clarified things immensely.
It is rare to find a textbook as interesting and clear as this one. If a professor is requiring that you read it, consider yourself fortunate. If you are thinking of reading it yourself, you also are blessed. Look forward to many pleasant evenings.
As a student, I am often tempted to find ways to not purchase textbooks as you only end up using them for a semester but this is definitely one that you should spring for if you plan on doing AI work in the future.
1. No answer key for any problems. This feature has been standard in textbooks for decades as a way for students to self-check their understanding of the material.
2. Examples are scant and sometimes stop in the middle. For example, in Chapter 13, the example of applying Bayes' Rule gives one approach and indicates that it will discuss an alternative approach, but then the text just goes off on another path and never completes the example.
3. Inconsistent and (sometimes) convoluted pseudocode for the algorithms. Pseudocode should be a fairly-close-to-English approximation of the algorithm, but this book seems to mix RTL, English, and any other notation. Though the appendix includes an attempt at explaining their rationale behind their own brand of pseudocode, it's incomplete at best. Also, the function names don't follow any convention I've ever seen (I have 30+ years experience in software), and aren't even consistent within the book.
4. Condescending language. This should never occur in a textbook. In far too many places, the authors tell us that "the sharp-eyed reader will have noticed" or similar phrases, which basically implies, "if you didn't get our explanation and find the hidden subtext, you are not sharp-eyed". All such language should have been edited out.
The authors came so close to writing a classic, but sadly missed the mark. I think that any professors who claim that their students "universally love this book" are deluding themselves. Still, if your professor is good at explicating the material, it's worth going through it once, then switching to other materials, maybe primary source materials in the subfield(s) that grab your interest.
Most recent customer reviews
the previous owner did not have enough care with the book. But the book did arrive which is good!