Explanation-based Similarity: A Unifying Approach for Integrating Domain

  • Case-based problem solving can be significantly improved by applying domain knowledge (in opposition to problem solving knowledge), which can be acquired with reasonable effort, to derive explanations of the correctness of a case. Such explanations, constructed on several levels of abstraction, can be employed as the basis for similarity assessment as well as for adaptation by solution refinement. The general approach for explanation-based similarity can be applied to different real world problem solving tasks such as diagnosis and planning in technical areas. This paper presents the general idea as well as the two specific, completely implemented realizations for a diagnosis and a planning task.

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Metadaten
Verfasserangaben:Ralph Bergmann, Wolfgang Wilke, Gerd Pews
URN (Permalink):urn:nbn:de:hbz:386-kluedo-1679
Dokumentart:Preprint
Sprache der Veröffentlichung:Englisch
Jahr der Fertigstellung:1999
Jahr der Veröffentlichung:1999
Veröffentlichende Institution:Technische Universität Kaiserslautern
Datum der Publikation (Server):03.04.2000
Freies Schlagwort / Tag:Case-based problem solving ; explanation-based learning; learning system
Bemerkung:
PARIS is a fully implemented system which integrates learning and problem solving (planning).
Fachbereiche / Organisatorische Einheiten:Fachbereich Informatik
DDC-Sachgruppen:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Lizenz (Deutsch):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011

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