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Representing Case Variations for Learning General and Specific Adaptation Rules
Fadi Badra, Jean Lieber
1 - 11
10.3233/978-1-58603-893-9-1
Frontiers in Artificial Intelligence and Applications
Volume 179: STAIRS 2008
Abstract

Adaptation is a task of case-based reasoning systems that is largely domain-dependant. This motivates the study of adaptation knowledge acquisition (AKA) that can be carried out thanks to learning processes on the variations between cases of the case base. This paper studies the representation of these variations and the impact of this representation on the AKA process, through experiments in an oncology domain.

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