Opportunistic Acquisition of Adaptation Knowledge and Cases -- The IakA Approach

Authors:
Amélie Cordier;Béatrice Fuchs;Léonardo Lana De Carvalho;Jean Lieber;Alain Mille
Affiliations:
LIRIS CNRS, UMR 5202, Université Lyon 1, INSA Lyon, Université Lyon 2, ECL,;LIRIS CNRS, UMR 5202, Université Lyon 1, INSA Lyon, Université Lyon 2, ECL,;LEACM-Cris, Université Lyon 2, Institut de Sciences de l'Homme (ISH) LIESP, Université Lyon 1, INSA Lyon,;LORIA UMR 7503 CNRS, INRIA, Universités de Nancy,;LIRIS CNRS, UMR 5202, Université Lyon 1, INSA Lyon, Université Lyon 2, ECL,
Venue:
ECCBR '08 Proceedings of the 9th European conference on Advances in Case-Based Reasoning
Year:
2008

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Abstract

A case-based reasoning system relies on different knowledge containers, including cases and adaptation knowledge. The knowledge acquisition that aims at enriching these containers for the purpose of improving the accuracy of the CBR inference may take place during design, maintenance, and also on-line, during the use of the system. This paper describes IakA, an approach to on-line acquisition of cases and adaptation knowledge based on interactions with an oracle (a kind of "ideal expert"). IakAexploits failures of the CBR inference: when such a failure occurs, the system interacts with the oracle to repair the knowledge base. IakA-NFis a prototype for testing IakAin the domain of numerical functions with an automatic oracle. Two experiments show how IakAopportunistic knowledge acquisition improves the accuracy of the CBR system inferences. The paper also discusses the possible links between IakAand other knowledge acquisition approaches.