Publikation

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

Klaus-Dieter Althoff, Agnar Aamodt

In: AI Communications (AIC) 9 3 Seiten 109-116 IOS Press 1996.

Abstrakt

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.

Weitere Links

aicom96.pdf (pdf, 55 KB)

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence