Application of the Revision Theory to Adaptation in Case-Based Reasoning: the Conservative Adaptation

Jean Lieber 1
1 ORPAILLEUR - Knowledge representation, reasonning
INRIA Lorraine, LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications
Abstract : Case-based reasoning aims at solving a problem by the adaptation of the solution of an already solved problem that has been retrieved in a case base. This paper defines an approach to adaptation called conservative adaptation; it consists in keeping as much as possible from the solution to be adapted, while being consistent with the domain knowledge. This idea can be related to the theory of revision: the revision of an old knowledge base by a new one consists in making a minimal change on the former, while being consistent with the latter. This leads to a formalization of conservative adaptation based on a revision operator in propositional logic. Then, this theory of conservative adaptation is confronted to an application of case-based decision support to oncology: a problem of this application is the description of a patient ill with breast cancer, and a solution, the therapeutic recommendation for this patient. Examples of adaptations that have actually been performed by experts and that can be captured by conservative adaptation are presented. These examples show a way of adapting contraindicated treatment recommendations and treatment recommendations that cannot be applied.
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Rosina O. Weber and Michael M. Richter. 7th International Conference on Case-Based Reasoning - ICCBR'07, Aug 2007, Belfast, United Kingdom. Springer Berlin / Heidelberg, 4626, pp.239-253, 2007, Lecture Notes in Artificial Intelligence. 〈10.1007/978-3-540-74141-1_17〉
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Jean Lieber. Application of the Revision Theory to Adaptation in Case-Based Reasoning: the Conservative Adaptation. Rosina O. Weber and Michael M. Richter. 7th International Conference on Case-Based Reasoning - ICCBR'07, Aug 2007, Belfast, United Kingdom. Springer Berlin / Heidelberg, 4626, pp.239-253, 2007, Lecture Notes in Artificial Intelligence. 〈10.1007/978-3-540-74141-1_17〉. 〈inria-00189626〉

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