InforLorV4, Main, Exploration, bibRecord, 003E59

Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach

Identifieur interne : 003E59 ( Main/Exploration ); précédent : 003E58; suivant : 003E60

Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach

Auteurs : Amélie Cordier [France] ; Béatrice Fuchs [France] ; Léonardo Lana De Carvalho [France] ; Jean Lieber [France] ; Alain Mille [France]

Source :

RBID : Hal:hal-00364367

English descriptors

mix :
- case-based reasoning, interactions, knowledge acquisition.

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”). 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.

Url:

https://hal.archives-ouvertes.fr/hal-00364367

Affiliations:

Links toward previous steps (curation, corpus...)

to stream Hal, to step Corpus: 003942
to stream Hal, to step Curation: 003942
to stream Hal, to step Checkpoint: 003173
to stream Main, to step Merge: 003F88
to stream Main, to step Curation: 003E59

Le document en format XML

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<front><div type="abstract" xml:lang="en">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.</div>
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Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach

Opportunistic Acquisition of Adaptation Knowledge and Cases - The IakA Approach

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