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From case-based reasoning to traces-based reasoning

Alain Mille 1
1 SILEX - Supporting Interaction and Learning by Experience
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
Abstract : CBR is an original AI paradigm based on the adaptation of solutions of past problems in order to solve new similar problems. Hence, a case is a problem with its solution and cases are stored in a case library. The reasoning process follows a cycle that facilitates ‘‘learning’’ from new solved cases. This approach can be also viewed as a lazy learning method when applied for task classification. CBR is applied for various tasks as design, planning, diagnosis, information retrieval, etc. The paper is the occasion to go a step further in reusing past unstructured experience, by considering traces of computer use as experience knowledge containers for situation based problem solving.
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Submitted on : Saturday, September 23, 2017 - 12:44:53 PM
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  • HAL Id : hal-01592324, version 1


Alain Mille. From case-based reasoning to traces-based reasoning. Annual Reviews in Control, Elsevier, 2006, 2, 30, pp.223-232. ⟨hal-01592324⟩



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