Springer Book Archives

1. A Perspective on Explanation-Based Learning.- 2. Explanation Generalization in EGGS.- 3. Generalizing Explanation Structures.- 4. Recoverable Simplifications and the Intractable Domain Theory Problem.- 5. Designing Experiments to Extend the Domain Theory.- 6. Some Aspects of Operationality.- 7. Empirically Evaluating EBL.- 8. Psychological Studies of Explanation-Based Learning.- 9. Case Study 1- ARMS: Acquiring Robotic Assembly Plans.- 10. Case Study 2 - GENESIS: Learning Schemata for Narrative Text Understanding.- 11. Case Study 3 - PHYSICS 101: Learning in Mathematically Based Domains.- 12. Case Study 4 - ADEPT: Extending the Domain Theory.- 13. Case Study 5 - NONMON: Learning with Recoverable Simplifications.- 14. Case Study 6 - GRASPER: Learning to Manipulate an Uncertain World.

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.