Publications

Using Introspective Reasoning to Improve CBR System Performance

Publication Type:

Conference Paper

Authors:

Josep Lluis Arcos; Oguz Mulayim; David Leake

Source:

AAAI Metareasoning Workshop, AAAI Press, p.21-28 (2008)

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.

Projects: 
MID-CBR
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