TitleUsing Introspective Reasoning to Improve CBR System Performance
Publication TypeConference Paper
Year of Publication2008
AuthorsArcos JLluis, Mulayim O, Leake D
EditorCox MT, Raja A
Conference NameAAAI Metareasoning Workshop
PublisherAAAI Press
Pagination21-28
Abstract

When AI technologies are applied to real-world problems, 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 reasoning 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 deficiencies in their reasoning process. In this paper we present a new introspective model for autonomously improving the performance of a CBR system by reasoning about system problem solving failures. We illustrate its benefits with experimental results from tests in an industrial design application.