Using Introspective Reasoning to Improve CBR System Performance

Josep L. Arcos, Oguz Mulayim, David Leake

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.

Subjects: 3.1 Case-Based Reasoning; 15. Problem Solving

Submitted: May 5, 2008


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