Relating case-based problem solving and learning methods to task and domain characteristics: towards an analytic framework

Issue title: ECAI-96 Budapest

Article type: Research Article

Authors: Althoff, Klaus-Dieter | Aamodt, Agnar

Affiliations: Centre for Learning Systems and Applications, Department of Computer Science, University of Kaiserslautern, D-67653 Kaiserslautern, Germany. E-mail: [email protected] | Department of Informatics, College of Arts and Science, University of Trondheim, N-7055 Dragvoll, Norway. E-mail: [email protected]

Abstract: A particular strength of Case-Based Reasoning (CBR) over most other methods is its inherent combination of problem solving with sustained learning through problem solving experience. This is therefore a particularly important topic of study, and an issue that has now become mature enough to be addressed in a more systematic way. To enable such an analysis of problem solving and learning, we have initiated work towards the development of an analytic framework for studying CBR methods. It provides an explicit ontology of basic CBR task types, domain characterisations, and types of problem solving and learning methods. Further, it incorporates within this framework a methodology for combining a knowledge-level, top-down analysis with a bottom-up, case-driven one. In this article, we present the underlying view and the basic approach being taken, the main components of the framework and accompanying methodology, examples of studies recently done and how they relate to the framework.

DOI: 10.3233/AIC-1996-9303

Journal: AI Communications, vol. 9, no. 3, pp. 109-116, 1996