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Artificial Intelligence: A Modern Approach (2nd Edition)

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Author: Stuart J. Russell, Peter Norvig
Hardcover
Edition: 2nd
Number Of Pages: 1132

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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.

Book Description

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.

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Textbook for my class        Rating:

Tends to oversimplify example algorithms, other than that it's a great book.

Excellent Introduction and an excellent educational perspective        Rating:

This book is excellent for a novice in AI. Chapter by chapter - though chapters vary in thoroughness and detail - the book illustrates AI techniques by family by accessibly presenting the problems that motivate the techniques, and the logic applied to implement them (pseudo-code).

Arguably the book is written from the easier towards the harder methods. It's a good treatment on search strategy and basic logic, but notably somewhat lacking in pattern recognition, unbound optimization, and some machine learning topics, which is understandable considering how extensive the subjects are.

As a starting point towards learning about AI techniques, or as a course text-book the Russel and Norvig (this) book is an excellent resource. The book will set up a student with a mindset towards identifying search, optimization and reasoning problems that lend themselves towards AI solutions, and how to pick the appropriate technique to solve them.

Note that the book presents all of these techniques through a framework of thought around intelligent agents (which can be somewhat confusing considering you will later mostly hear the keyword 'agent' in AI techniques that apply social intelligence, or solve problems via interactions between somewhat independent intelligent constructs).

Follow up with Duda's "Pattern Classification", and Mitchell's "An Introduction to Genetic Algorithms (Complex Adaptive Systems)" for a more in depth treatment on Machine Learning problems and solutions and Genetic Algorithms. Maybe also some reading on swarm intelligence, and you'll have good referential knowledge and a decent tool-set of AI reasoning and problem solving skills.

wide range of techniques, including neural networks        Rating:

The authors furnish a voluminous and up to date survey of AI. Covering the gamut of ideas and computational techniques common to the field. Unsurprisingly, there is plenty of discussion about formal logic methods and how to develop inferences in a manner doable by a computer. This is expanded into describing ontologies and how to find knowledge in a learning process. All of these are hard topics that try to attack a fundamentally difficult problem.

Other ideas explored include neural networks. Very different from a formal deductive stance. The learning here is done in training sessions that can be very extensive. Interesting that the book includes prominent discussion of neural networks. I recall that in the 80s, this was considered by some AI people not to be a valid AI approach. But the authors have taken a more pragmatic and inclusive view.

The choice of "intelligent agent" in the subtitle is interesting. It can be seen as positioning the AI methods for application to such agents. These have been and are widely mooted as perhaps one promising future direction for deployment on the Web.

Wonderful for its overviews and historical notes        Rating:

This book was used in both my undergraduate and graduate courses in artificial intelligence. It is a very readable book, and an enormously useful reference. I believe they at least do a cursory overview of the most critical concepts in AI, and if they don't, there are usually great references at the end of the chapter.

That is actually my favorite features of this book. At the end of each chapter, Norvig and Russell will give something like an annotated bibliography, but with much more depth.

Worth every penny        Rating:

This is the book to buy if you are interested in any topic dealing with AI. If there is a topic in AI that you are interested in, then this book with have some information about it.

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