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Provides a simple overview of major concepts, uses a nontechnical language to help increase understanding. Makes the book accessible to a broader range of students.
Promotes student interest with interesting, relevant exercises.
Brings students up to date on the latest technologies, and presents concepts in a more unified manner.
Allows many more opportunities for student projects on the web.
Shows students how the various subfields of AI fit together to build actual, useful programs.
Makes the text adaptable for varying instructors' preferences.
Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.
Gives instructors and students a choice of projects; reading and running the code increases understanding.
Author Maintained Website
visit http://aima.cs.berkeley.edu/ to access text-related Comments and Discussions, AI Resources on the Web, and Online Code Repository, Instructor Resources, and more!
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:
aima.cs.berkeley.edu
I. ARTIFICIAL INTELLIGENCE.
1. Introduction.II. PROBLEM-SOLVING.
3. Solving Problems by Searching.III. KNOWLEDGE AND REASONING.
7. Logical Agents.IV. PLANNING.
11. Planning.V. UNCERTAIN KNOWLEDGE AND REASONING.
13. Uncertainty.VI. LEARNING.
18. Learning from Observations.VII. COMMUNICATING, PERCEIVING, AND ACTING.
22. Agents that Communicate.VIII. CONCLUSIONS.
26. Philosophical Foundations.