Case-base maintenance in a multimodal reasoning system
The definition of suitable case-base maintenance policies is widely recognized as a major key to success for case-based systems; underestimating this issue may lead to systems that either do not fulfill their role of knowledge management and preservation or that do not perform adequately under performance dimensions, namely, computation time and competence and quality of solutions. The goal of this article is to analyze some automatic case-base management strategies in the context of a multimodal architecture combining case-based reasoning and model-based reasoning. We propose and compare two different methodologies, the first one, called replace, is a competence-based strategy aimed at replacing a set of stored cases with the current one, if the latter exhibits an estimated competence comparable with the estimated competence of the considered set of stored cases. The second one, called learning by failure,vith forgetting (LFF), is based on incremental learning of cases interleaved with off-line processes of forgetting (deleting) cases whose usage does not fulfill specific utility conditions. Results from an extensive experimental analysis in an industrial plant diagnosis domain are reported, showing the usefulness of both strategies with respect to the maintenance of suitable performance levels for the target system.
case-base maintenance competence models utility problem