Artificial Intelligence: An MIT Perspective: Understanding Vision, Manipulation, Computer Design, Symbol Manipulation (Volume 2)

Watson, P.H. and Brown, R.H. (eds)

ISBN 10: 0262230976 / ISBN 13: 9780262230971

Edité par The MIT Press, 1982

A propos de cet article

Volume 2. This is an ex-library book and may have the usual library/used-book markings inside.This book has hardback covers. In good all round condition. No dust jacket. Please note the Image in this listing is a stock photo and may not match the covers of the actual item,1000grams, ISBN:0262230976. N° de réf. du vendeur 9382201

Détails bibliographiques

Titre : Artificial Intelligence: An MIT Perspective:...

Éditeur : The MIT Press

Date d'édition : 1982

Reliure : Couverture rigide

Etat : Good

A propos de ce titre

Synopsis :

Book by None

Quatrième de couverture:

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.

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.

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