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The Case for Structure-based Representations

dc.contributor.authorSanders, Kathryn E.en_US
dc.contributor.authorKettler, Brian P.en_US
dc.contributor.authorHendler, Jamesen_US
dc.description.abstractCase-based reasoning involves reasoning from {\em cases}: specific pieces of experience, the reasoner's or another's, that can be used to solve problems. As a result, case representation is critical: an incomplete case representation limits the system's reasoning power. In this paper we argue for {\em structure-based} case representations, which express arbitrary relations among objects in a flexible way, over more limited or inflexible methods. We motivate the distinction between these kinds of representations with examples from information retrieval systems, CBR systems, and computational models of human analogical reasoning. Structure-based representations provide the benefits of greater expressivity and economy. We give examples of these benefits from two case-based planning systems we have developed, CaPER and CHIRON, and show how the case matching and case acquisition costs can be reduced through the use of massively parallel techniques. (Also cross-referenced as UMIACS-TR-95-56)en_US
dc.format.extent228264 bytes
dc.relation.ispartofseriesUM Computer Science Department; CS-TR-3468en_US
dc.relation.ispartofseriesUMIACS; UMIACS-TR-95-56en_US
dc.titleThe Case for Structure-based Representationsen_US
dc.typeTechnical Reporten_US
dc.relation.isAvailableAtDigital Repository at the University of Marylanden_US
dc.relation.isAvailableAtUniversity of Maryland (College Park, Md.)en_US
dc.relation.isAvailableAtTech Reports in Computer Science and Engineeringen_US
dc.relation.isAvailableAtUMIACS Technical Reportsen_US

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