Artificial Intelligence: A Modern Approach (2nd Edition) 2nd Edition

I didn't think that the first edition of this book was as bad as some of the reviewers said, but the second edition is definitely a vast improvement. It's not just some obligatory 2nd edition that some authors release to say that they are staying actively published. The first edition was somewhat confusing in its explanations and the exercises were really blurry on what was being asked. All of that has now been resolved.
The book is a comprehensive and insightful introduction to artificial intelligence with an academic tone. It provides a unified view of the field organized around the rational decision making paradigm, which focuses on the selection of the "best" solution to a problem. The book's overall theme is that the purpose of AI is to solve problems via intelligent agents, and then goes about specifying the features such an agent or agents should have. Pseudocode is provided for all of the major AI algorithms. Being about the broadest book in terms of coverage of AI, you should therefore not expect it to be the deepest in coverage. However, each topic is covered to the extent that the reader should understand its essence. Sections one through six are absolutely wonderful, and comprise the "meat" of AI. Section seven is rather weak since it tries to cover both robotics and text processing in their own individual chapters, and entire books have a hard time covering this material. Section eight is different from the others, since it talks about the philosophy and future of AI.
Another plus for this book is that there is a great deal of extra material that deals with standard AI curriculum. For example, the chapters on logic not only include the typical introduction to propositional and first order logic together with the usual inference procedures, they also give many useful hints how to use first order logic to actually represent aspects of the real world such as measures, time, actions, mental objects, etc. These chapters also contain much information about how to implement efficient logical reasoners.
Finally, this second edition has an excellent website that can be found by going through the publisher's webpage for the book. This website contains four sample chapters, pseudocode, and actual code in Java, Python, and LISP.
I notice that Amazon shows the table of contents from the first edition, so I am showing what the actual table of contents is for the second edition for the purpose of completeness. Note that the book has been significantly reorganized.
I. ARTIFICIAL INTELLIGENCE.
1. Introduction.
2. Intelligent Agents.
II. PROBLEM-SOLVING.
3. Solving Problems by Searching.
4. Informed Search and Exploration.
5. Constraint Satisfaction Problems.
6. Adversarial Search.
III. KNOWLEDGE AND REASONING.
7. Logical Agents.
8. First-Order Logic.
9. Inference in First-Order Logic.
10. Knowledge Representation.
IV. PLANNING.
11. Planning.
12. Planning and Acting in the Real World.
V. UNCERTAIN KNOWLEDGE AND REASONING.
13. Uncertainty.
14. Probabilistic Reasoning Systems.
15. Probabilistic Reasoning Over Time.
16. Making Simple Decisions.
17. Making Complex Decisions.
VI. LEARNING.
18. Learning from Observations.
19. Knowledge in Learning.
20. Statistical Learning Methods.
21. Reinforcement Learning.
VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.
23. Text Processing in the Large.
24. Perception.
25. Robotics.
VIII. CONCLUSIONS.
26. Philosophical Foundations.
27. AI: Present and Future.

Most of the reviews here refer to the first edition, not the second. There have been significant changes to the second edition. Amazon should consider using a display that clarifies which edition each review refers to.

The book offers mathematically based coverage of the most important topics in AI. It has a very pragmatic view and does not spend too much time with the over-hyped topics, such as fuzzy logic, genetic algorithms. Instead, it provides clear and easy to understand examples for most of the introduced concepts.

The text requires some knowledge of computer science, mathematics, and statistics. Sections on topics that require extensive outside knowledge, such as computational learning theory, are not covered very deeply, but usually offer nice insight to the concepts and basic principles.

I recommend this book both for beginners to exact AI as an introductory text and to more experienced researchers as an invaluable reference. I found it to be the most useful book I own.

Until recently, my Algorithms book was my favorite text book ever. However, AI: A Modern Approach has supplanted it. This book is the most thoughtfully designed, easily understandable, clear text I've ever used in over 28 years of attending schools. I really knew nothing about AI when I took my first grad class in AI, but this book, along with a pretty great instructor, has been a wonderful resource, more than any other book I've used. I have not need to google for more information or speak to the professor. The answers are here--clear and concrete.

Have no fear and trust this book!

This is currently the best general purpose book on AI (the field). It is by no means the best book on any individual topic though... but I guess that wasn't the point of the book to begin with.

I'm not particularly happy with this book though. I think it's the language, or the examples, or something, but often it's just not `clear'---not as intuitive as I'd like. Many teachers seem to use it in a `general purpose course on AI' though (not anything particularly detailed, so it's `ok'---there are worse books on the subjects).

In short: as a general purpose AI book on everything, it does its job, but don't expect it to be anything other than a glorified field overview.

This is probably the most up-to-date single textbook introducing Artificial Intelligence. Unfortunately, it presents a limited view of the field by chosing not to cover the vast area of AI that deals with cognitive modeling. The authors are up front about this decision and define AI as the study of agents that act rationally [Preface and Introduction] and specifically choose not to address the schools of thought and ongoing research in AI that consider "Systems that think like humans" and "Systems that act like humans" [Introduction]. What's covered is done well. Students of AI and other readers will need to augment this text for exposure to the rest of the discipline.