Artificial Intelligence: A Modern Approach (2nd Edition)

Artificial Intelligence: A Modern Approach introduces basic ideas in artificial intelligence from the perspective of building intelligent agents, which the authors define as `anything that can be viewed as perceiving its environment through sensors and acting upon the environment through effectors.` This textbook is up-to-date and is organized using the latest principles of good textbook design. It includes historical notes at the end of every chapter, exercises, margin notes, a bibliography, and a competent index. Artificial Intelligence: A Modern Approach covers a wide array of material, including first-order logic, game playing, knowledge representation, planning, and reinforcement learning.

User review
Could have been great, but ,,.
As some reviewers have said, this is probably the most comprehensive AI textbook on the market. The `pros` of the book have been covered pretty well by other reviewers, so I'll limit my review to some of the things that bug me about the book.

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.

User review
Nice introductory text on AI
I used this book for a graduate course in Artificial Intelligence. We covered about half of the book in class. The book is very comprehensive and I found that parts of the book have heavy notations including calculus (and I needed to review some of those concepts in order to follow completely). Overall I liked the book very much and I highly recommend to those who are looking for an introductory and broad view of AI.

User review
cheating
This seller is cheating on the item he is selling. The book he sold to me is an international edition and does not have the same cover as list above. Do not buy things from the seller.

User review
Good book so far
I've started using this book for my AI class and it's been pretty useful so far.

User review
Probably the best known book on artificial intelligence
This is the text that was used in the Artificial Intelligence (AI) computer science course at Wisconsin while I was an undergraduate. It's widely considered one of the best AI references available today. This book was my *sole resource* in implementing decision trees, neural networks, and search and other algoriths such as GSAT, WALKSAT, and Simulated Annealing. It was also very useful for propositional logic programming in PROLOG.

Some reviewers have complained that the text is too long and complicated. To that point I would say that AI is a complex field and to fully understand it you need to go into a good bit of depth. For me personally, it was easier to learn by looking at real-life applications rather than mathematical explanations. Many of my programs were what I would consider to be some of the coolest programs I have ever written -- to me, machine learning is such an incredible concept and the applications are limitless. Futher, there's a TON of information in this text that you can only scratch the surface over the course of a semester. That doesn't make it a bad book,,,,it makes it a great reference.