|Títol||Using Introspective Reasoning to Improve CBR System Performance|
|Publication Type||Conference Paper|
|Year of Publication||2008|
|Authors||Arcos JLluis, Mulayim O, Leake D|
|Editor||Cox MT, Raja A|
|Conference Name||AAAI Metareasoning Workshop|
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.
- Quant a IIIA