Investigating Explanation-Based Learning

By: Gerald DeJong (Editor)

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Hardcover | 31 October 1992

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452 Pages

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Explanation-Based Learning (EBL) can generally be viewed as substituting background knowledge for the large training set of exemplars needed by conventional or empirical machine learning systems. The background knowledge is used automatically to construct an explanation of a few training exemplars. The learned concept is generalized directly from this explanation.
The first EBL systems of the modern era were Mitchell's LEX2, Silver's LP, and De Jong's KIDNAP natural language system. Two of these systems, Mitchell's and De Jong's, have led to extensive follow-up research in EBL. This book outlines the significant steps in EBL research of the Illinois group under De Jong.
This volume describes theoretical research and computer systems that use a broad range of formalisms: schemas, production systems, qualitative reasoning models, non-monotonic logic, situation calculus, and some home-grown ad hoc representations. This has been done consciously to avoid sacrificing the ultimate research significance in favor of the expediency of any particular formalism.
The ultimate goal, of course, is to adopt (or devise) the right formalism.

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Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Psychology Cognition & Cognitive Psychology Artificial Intelligence Natural Language & Machine Translation

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Published: 4th October 2012

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Preface
Foreword
A Perspective on Explanation-Based Learning	p. 1
Explanation Generalization in EGGS	p. 20
Generalizing Explanation Structures	p. 60
Recoverable Simplifications and the Intractable Domain Theory Problem	p. 128
Designing Experiments to Extend the Domain Theory	p. 166
Some Aspects of Operationality	p. 190
Empirically Evaluating EBL	p. 222
Psychological Studies of Explanation-Based Learning	p. 295
Case Study 1 - ARMS: Acquiring Robotic Assembly Plans	p. 317
Case Study 2 - GENESIS: Learning Schemata for Narrative Text Understanding	p. 343
Case Study 3 - PHYSICS 101: Learning in Mathematically Based Domains	p. 372
Case Study 4 - ADEPT: Extending the Domain Theory	p. 395
Case Study 5 - NONMON: Learning with Recoverable Simplifications	p. 410
Case Study 6 - GRASPER: Learning to Manipulate an Uncertain World	p. 424
Index	p. 435
Table of Contents provided by Blackwell. All Rights Reserved.

Investigating Explanation-Based Learning

At a Glance

Hardcover

Shipping

How to return your order

Defective items

You Can Find This Book In

Other Editions and Formats

Paperback

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