Artificial Intelligence: A Modern Approach, 2nd Edition

About

Features

• NEW - Nontechnical learning material.

  • 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.

• NEW - The Internet as a sample application for intelligent systems–Examples of logical reasoning, planning, and natural language processing using Internet agents.

  • Promotes student interest with interesting, relevant exercises.

• NEW - Increased coverage of material–New or expanded coverage of constraint satisfaction, local search planning methods, multi-agent systems, game theory, statistical natural language processing and uncertain reasoning over time. More detailed descriptions of algorithms for probabilistic inference, fast propositional inference, probabilistic learning approaches including EM, and other topics.

  • Brings students up to date on the latest technologies, and presents concepts in a more unified manner.

• NEW - Updated and expanded exercises–75% of the exercises are revised, with 100 new exercises.

• NEW - More Online Software.

  • Allows many more opportunities for student projects on the web.

• A unified, agent-based approach to AI–Organizes the material around the task of building intelligent agents.

  • Shows students how the various subfields of AI fit together to build actual, useful programs.

• Comprehensive, up-to-date coverage–Includes a unified view of the field organized around the rational decision making paradigm.

• A flexible format.

  • Makes the text adaptable for varying instructors' preferences.

• In-depth coverage of basic and advanced topics.

  • Provides students with a basic understanding of the frontiers of AI without compromising complexity and depth.

• Pseudo-code versions of the major AI algorithms are presented in a uniform fashion, and Actual Common Lisp and Python implementations of the presented algorithms are available via the Internet.

  • 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!

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Description

• Copyright 2003
• Edition: 2nd

• Book
• ISBN-10: 0-13-790395-2
• ISBN-13: 978-0-13-790395-5

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

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Sample Content

Table of Contents

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 Read 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.

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