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Representing Case Variations for Learning General and Specific Adaptation Rules

Fadi Badra 1, * Jean Lieber 1
* Corresponding author
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
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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Submitted on : Thursday, August 27, 2009 - 5:38:06 PM
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  • HAL Id : inria-00337554, version 1



Fadi Badra, Jean Lieber. Representing Case Variations for Learning General and Specific Adaptation Rules. Fourth Starting AI Researcher's Symposium (STAIRS 2008), Jul 2008, Patras, Greece. pp.1--11. ⟨inria-00337554⟩



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