The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice of artificial intelligence. Intelligent Agents. Solving Problems by Searching. Informed Search Methods. Game Playing. Agents that Reason Logically. First-order Logic. Building a Knowledge Base. Inference in First-Order Logic. Logical Reasoning Systems. Practical Planning. Planning and Acting. Uncertainty. Probabilistic Reasoning Systems. Making Simple Decisions. Making Complex Decisions. Learning from Observations. Learning with Neural Networks. Reinforcement Learning. Knowledge in Learning. Agents that Communicate. Practical Communication in English. Perception. Robotics. For those interested in artificial intelligence.
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.
Customer Reviews:
Avg. Customer Rating: 4.0 / 5.0
Excellent AI book:
Recommended for people looking for a good, but not that mathematical, summary of the field.
Fantastic Textbook:
As a student with a very strong background in technical fields I am no stranger to heavy studying, reference, and in some cases even total reliance on textbooks. I have encountered many kinds of textbooks, some which get the job done, some which do the same painfully, and unfortunately a select few that were simply inscrutable and probably inhibited my learning more than anything else. This textbook however, is definitely a cut above all the rest. It's very likely that it's the best textbook I've ever... more info
A landmark:
I never took a course in AI, but I've been reading and rereading this book, with pleasure, since the mid-nineties. This book is deep (as well as broad), tells a coherent story, and is very well-written and amusing. It is much more than a textbook or an encyclopedia; it's two of the smartest people around sharing years of study and reflection on some of the hardest and most interesting questions around. It is not something to grok in a semester or two, and it should not be your only information source... more info
Superficial, not clear, not a good choice:
I'm currently teaching AI. Since it's the standard textbook for AI courses, I decided to use Russel&Norvig's book, and I am really disappointed. The book is too superficial, trying to cover too much, and their notation and explanations are not always clear. For example, try to understand the Viterbi algorithm for HMMs. It's perfectly clear if you read an introductory article, but this book gives a very confusing idea of how it works. In several other parts of the book the same thing happens.
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