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.
|Author:||Ralph Bergmann, Wolfgang Wilke, Gerd Pews|
|URN (permanent link):||urn:nbn:de:hbz:386-kluedo-1679|
|Language of publication:||English|
|Year of Completion:||1999|
|Year of Publication:||1999|
|Publishing Institute:||Technische Universität Kaiserslautern|
|Tag:||Case-based problem solving ; explanation-based learning; learning system|
PARIS is a fully implemented system which integrates learning and problem solving (planning).
|Faculties / Organisational entities:||Fachbereich Informatik|
|DDC-Cassification:||004 Datenverarbeitung; Informatik|