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Conference Papers Year : 2007

Case Base Mining for Adaptation Knowledge Acquisition

Mathieu d'Aquin
Fadi Badra
Sandrine Lafrogne
  • Function : Author
  • PersonId : 836299
Jean Lieber
Amedeo Napoli
Laszlo Szathmary
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  • PersonId : 830970


In case-based reasoning, the adaptation of a source case in order to solve the target problem is at the same time crucial and difficult to implement. The reason for this difficulty is that, in general, adaptation strongly depends on domain-dependent knowledge. This fact motivates research on adaptation knowledge acquisition (AKA). This paper presents an approach to AKA based on the principles and techniques of knowledge discovery from databases and data-mining. It is implemented in CABAMAKA, a system that explores the variations within the case base to elicit adaptation knowledge. This system has been successfully tested in an application of case-based reasoning to decision support in the domain of breast cancer treatment.
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inria-00127347 , version 1 (30-03-2007)



Mathieu d'Aquin, Fadi Badra, Sandrine Lafrogne, Jean Lieber, Amedeo Napoli, et al.. Case Base Mining for Adaptation Knowledge Acquisition. Twentieth International Joint Conference on Artificial Intelligence - IJCAI'07, Jan 2007, Hyderabad, India. pp.750-755. ⟨inria-00127347⟩
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