Ebook: Investigating Explanation-Based Learning

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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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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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.

Content:
Front Matter....Pages i-ix
A Perspective on Explanation—Based Learning....Pages 1-19
Explanation Generalization in EGGS....Pages 20-59
Generalizing Explanation Structures....Pages 60-127
Recoverable Simplifications and the Intractable Domain Theory Problem....Pages 128-165
Designing Experiments to Extend the Domain Theory....Pages 166-189
Some Aspects of Operationality....Pages 190-221
Empirically Evaluating EBL....Pages 222-294
Psychological Studies of Explanation—Based Learning....Pages 295-316
Case Study 1 — ARMS: Acquiring Robotic Assembly Plans....Pages 317-342
Case Study 2 — GENESIS: Learning Schemata for Narrative Text Understanding....Pages 343-371
Case Study 3 — PHYSICS 101: Learning in Mathematically Based Domains....Pages 372-394
Case Study 4 — ADEPT: Extending the Domain Theory....Pages 395-409
Case Study 5 — NONMON: Learning with Recoverable Simplifications....Pages 410-423
Case Study 6 — GRASPER: Learning to Manipulate an Uncertain World....Pages 424-434
Back Matter....Pages 435-438

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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.

Content:
Front Matter....Pages i-ix
A Perspective on Explanation—Based Learning....Pages 1-19
Explanation Generalization in EGGS....Pages 20-59
Generalizing Explanation Structures....Pages 60-127
Recoverable Simplifications and the Intractable Domain Theory Problem....Pages 128-165
Designing Experiments to Extend the Domain Theory....Pages 166-189
Some Aspects of Operationality....Pages 190-221
Empirically Evaluating EBL....Pages 222-294
Psychological Studies of Explanation—Based Learning....Pages 295-316
Case Study 1 — ARMS: Acquiring Robotic Assembly Plans....Pages 317-342
Case Study 2 — GENESIS: Learning Schemata for Narrative Text Understanding....Pages 343-371
Case Study 3 — PHYSICS 101: Learning in Mathematically Based Domains....Pages 372-394
Case Study 4 — ADEPT: Extending the Domain Theory....Pages 395-409
Case Study 5 — NONMON: Learning with Recoverable Simplifications....Pages 410-423
Case Study 6 — GRASPER: Learning to Manipulate an Uncertain World....Pages 424-434
Back Matter....Pages 435-438
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