Self-optimising CBR retrieval

One reason why Case-Based Reasoning (CBR) has become popular is because it reduces development cost compared to rule-based expert systems. Still, the knowledge engineering effort may be demanding. In this paper we present a tool which helps to reduce the knowledge acquisition effort for building a typical CBR retrieval stage consisting of a decision-tree index and similarity measure. We use Genetic Algorithms to determine the relevance/importance of case features and to find optimal retrieval parameters. The optimisation is done using the data contained in the casebase. Because no (or little) other knowledge is needed this results in a self-optimising CBR retrieval. To illustrate this we present how the tool has been applied to optimise retrieval for a tablet formulation problem.

• Type: Conference
• Year: 2000
• Where: ICTAI
• Authors: Jacek Jarmulak, Susan Craw, Ray Rowe
• Keywords: Artificial Intelligence, CBR Retrieval, CBR Retrieval Stage, ICTAI 2000, Self-optimising Cbr Retrieval