Using Introspective Reasoning to Improve CBR System Performance
Josep Lluís Arcos, Oğuz Mülâyim, David B. Leake · The MIT Press eBooks · 2011
When AI technologies are applied to real-world prob-lems, it is often difficult for developers to anticipate all the knowledge needed. Previous research has shown that introspective reasoning can be a useful tool for helping to address this problem in case-based reason-ing systems, by enabling them to augment their routine learning of cases with learning to make better use of their cases, as problem-solving experience reveals de-ficiencies in their reasoning process. In this paper we present a new introspective model for autonomously improving the performance of a CBR system by rea-soning about system problem solving failures. We illus-trate its benefits with experimental results from tests in an industrial design application.