Please use this identifier to cite or link to this item: doi:10.22028/D291-40794
Title: Goal-Driven Similarity Assessment
Author(s): Wess, Stefan
Janetzko, Dietmar
Melis, Erica
Language: English
Year of Publication: 1992
Place of publication: Kaiserslautern
DDC notations: 004 Computer science, internet
Publikation type: Report
Abstract: While most approaches to similarity assessment are oblivious of knowledge and goals, there is ample evidence that these elements of problem solving play an important role in similarity judgements. This paper is concerned with an approach for integrating assessment of similarity into a framework of problem solving that embodies central notions of problem solving like goals, knowledge and learning. We review empirical findings that unravel characteristics of similarity assessment most of which have not been covered by purely syntactic models of similarity. A formal account of similarity assessment that allows for the integration of central ideas of problem solving is developed. Given a goal and a domain theory, an appropriate perspective is taken that brings into focus only goal-relevant features of a problem description as input to similarity assessment.
Link to this record: urn:nbn:de:bsz:291--ds-407940
hdl:20.500.11880/37678
http://dx.doi.org/10.22028/D291-40794
Series name: SEKI-Report / Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI [ISSN 1437-4447]
Series volume: 92,5
Date of registration: 22-May-2024
Faculty: SE - Sonstige Einrichtungen
Department: SE - DFKI Deutsches Forschungszentrum für Künstliche Intelligenz
Professorship: SE - Sonstige
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes

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