Integrating induction in a case-based reasoner

Abstract

This paper focuses on two key issues in building case-based reasoners (CBRs). The first issue is the knowledge engineering phase needed for CBRs as well as knowledge-based systems (lOS); the second issue is the integration of different methods of learning into CBRs. We show that we can use a knowledge modelling framework for the description and implementation of CBR systems; in particular we show how we used it in developing a CBR in the domain of protein purification. In order to encompass CBR (and learning in general) our knowledge modelling framework extends the usual frameworks with the notion of memory. Including memory we provide the capability for storing and retrieving episodes of problem solving, the basis of case-based reasoning and learning. We show here that this framework, and the supporting language NOOS, allows furthermore to integrate other learning methods as needed. Specifically, we show how a method for the induction of class prototypes can be implemented and integrated with case-based methods in an uniform framework.