This book explains how it is possible for computers to reason and perceive, thus introducing the field called artificial intelligence. From the book, you learn why the field is important, both as a branch of engineering and as a science.

If you are a computer scientist or an engineer, you will enjoy the book, because it provides a cornucopia of new ideas for representing knowledge, using knowledge, and building practical systems. If you are a psychologist, biologist, linguist, or philosopher, you will enjoy the book because it provides an exciting computational perspective on the mystery of intelligence.

The Knowledge You Need

This completely rewritten and updated edition of Artificial Intelligence reflects the revolutionary progress made since the previous edition was published.

Part I is about representing knowledge and about reasoning methods that make use of knowledge. The material covered includes the semantic-net family of representations, describe and match, generate and test, means-ends analysis, problem reduction, basic search, optimal search, adversarial search, rule chaining, the rete algorithm, frame inheritance, topological sorting, constraint propagation, logic, truth maintenance, planning, and cognitive modeling.

Part II is about learning, the sine qua non of intelligence. Some methods involve much reasoning; others just extract regularity from data. The material covered includes near-miss analysis, explanation-based learning, knowledge repair, case recording, version-space convergence, identification-tree construction, neural-net training, perceptron convergence, approximation-net construction, and simulated evolution.

Part III is about visual perception and language understanding. You learn not only about perception and language, but also about ideas that have been a major source of inspiration for people working in other subfields of artificial intelligence. The material covered includes object identification, stereo vision, shape from shading, a glimpse of modern linguistic theory, and transition-tree methods for building practical natural-language interfaces.

Table of Contents

1. The Intelligent Computer
2. Semantic Nets and Description Matching
3. Generate and Test, Means-Ends Analysis,and Problem Reduction
4. Nets and Basic Search
5. Nets and Optimal Search
6. Trees and Adversarial Search
7. Rules and Rule Chaining
8. Rules, Substrates, and Cognitive Modeling
9. Frames and Inheritance
10. Frames and Commonsense
11. Numeric Constraints and Propagation
12. Symbolic Constraints and Propagation
13. Logic and Resolution Proof
14. Backtracking and Truth Maintenance
15. Planning
16. Learning by Analyzing Differences
17. Learning by Explaining Experience
18. Learning by Correcting Mistakes
19. Learning by Recording Cases
20. Learning by Managing Multiple Models
21. Learning by Building Identification Trees
22. Learning by Training Neural Nets
23. Learning by Training Perceptrons
24. Learning by Training Approximation Nets
25. Learning by Simulating Evolution
26. Recognizing Objects
27. Describing Images
28. Expressing Language Constraints
29. Responding to Questions and Commands

Special Features of this Edition

• Based on extensive teaching experience
• Semiformal representation and procedure specifications bring the ideas to within a step or two of implementation and highlight unifying themes.
• Application examples provide a glimpse of the ideas at work in real-world systems.
• Powerful ideas and principles are identified and emphasized.

About the Author

Patrick Henry Winston teaches computer science and directs the Artificial Intelligence Laboratory at theMassachusetts Institute of Technology.