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Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design

Abstract : Despite various publications in the area during the last few years, the adaptation step is still a crucial phase for a relevant and reasonable Case Based Reasoning system. Furthermore, the online acquisition of the new adaptation knowledge is of particular interest as it enables the progressive improvement of the system while reducing the knowledge engineering effort without constraints for the expert. Therefore this paper presents a new interactive method for adaptation knowledge elicitation, acquisition and reuse, thanks to a modification of the traditional CBR cycle. Moreover to improve adaptation knowledge reuse, a test procedure is also implemented to help the user in the adaptation step and its diagnosis during adaptation failure. A study on the quality and usefulness of the new knowledge acquired is also driven. As our Knowledge Based Systems (KBS) is more focused on preliminary design, and more particularly in the field of process engineering, we need to unify in the same method two types of knowledge: contextual and general. To realize this, this article proposes the integration of the Constraint Satisfaction Problem (based on general knowledge) approach into the Case Based Reasoning (based on contextual knowledge) process to improve the case representation and the adaptation of past experiences. To highlight its capability, the proposed approach is illustrated through a case study dedicated to the design of an industrial mixing device.
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Eduardo Roldan Reyes, Stéphane Negny, G. Cortes Robles, Jean-Marc Le Lann. Improvement of online adaptation knowledge acquisition and reuse in case-based reasoning: Application to process engineering design. Engineering Applications of Artificial Intelligence, Elsevier, 2015, 41, pp.1-16. ⟨10.1016/j.engappai.2015.01.015⟩. ⟨hal-01879760⟩

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