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Learning and Clustering Plan Abstractions to Improve Hierarchical Planning

  • Hierachical planning can be improved by explanation-based learning (EBL) of abstract plans from detailed, successfully solved planning problems. Abstract plans, expressed in well-established terms of the domain, serve as useful problem decompositions which can drastically reduce the planning complexity. The learned plan abstraction must be valid for a class of planning cases rather than for a single case, to ensure their successful application in a larger spectrum of new situations. A hierarchical organization of the newly learned knowledge must be archieved to overcome the utility problem in EBL. This paper presents a new formal model of shared plan abstraction and the closely related explanation-based procedure S-PABS. Unlike other apporaches to plan abstraction, our model allows a total different terminology to be introduced at the abstract level. Finally, an unsupervised incremental procedure for constructing a hierachy of shared abstract plans is proposed, as a kind of concept formation over explanations.

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Metadaten
Author:Ralph Bergmann
URN (permanent link):urn:nbn:de:hbz:386-kluedo-1729
Document Type:Preprint
Language of publication:English
Year of Completion:1999
Year of Publication:1999
Publishing Institute:Technische Universität Kaiserslautern
Date of the Publication (Server):2000/04/03
Tag:Abstraction; Knowledge Acquisition; case-based problem solving; explanation-based learning
Faculties / Organisational entities:Fachbereich Informatik
DDC-Cassification:0 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik
Licence (German):Standard gemäß KLUEDO-Leitlinien vor dem 27.05.2011