Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach

Amélie Cordier (1) , Béatrice Fuchs (1) , Léonardo Lana de Carvalho (2) , Jean Lieber (3) , Alain Mille (1)

1 SILEX - Supporting Interaction and Learning by Experience
2 LIESP - Laboratoire d'Informatique pour l'Entreprise et les Systèmes de Production
3 ORPAILLEUR - Knowledge representation, reasonning

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”). IakA exploits failures of the CBR inference: when such a failure occurs, the system interacts with the oracle to repair the knowledge base. IakA-NF is a prototype for testing IakA in the domain of numerical functions with an automatic oracle. Two experiments show how IakA opportunistic knowledge acquisition improves the accuracy of the CBR system inferences. The paper also discusses the possible links between IakA and other knowledge acquisition approaches.

Mots clés: case-based reasoning; knowledge acquisition; interactions

Domaines: Intelligence artificielle [cs.AI]

Amélie Cordier, Béatrice Fuchs, Léonardo Lana de Carvalho, Jean Lieber, Alain Mille. Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach. European Conference on Case-Based Reasoning, Sep 2008, Trier, Germany. pp.150-164. ⟨hal-00364367⟩