Béatrice Fuchs

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Case-based reasoning (CBR) uses various knowledge containers for problem solving: cases, domain, similarity, and adaptation knowledge. These various knowledge containers are characterised from the engineering and learning points of view. We focus on adaptation and similarity knowledge containers that are of first importance, difficult to acquire and to(More)
The adaptation process is an important and complex step of case-based reasoning (CBR) and is most of the time designed for a specific application. This article presents a domain-independent algorithm for adaptation in CBR. Cases are mapped to a set of numerical descriptors filled with values and local constraint intervals. The algorithm computes every(More)
A case-based reasoning system relies on different knowledge containers, including cases and adaptation knowledge. The knowledge acquisition that aims at enriching these containers for the purpose of improving the accuracy of the CBR inference may take place during design , maintenance, and also on-line, during the use of the system. This paper describes(More)
A knowledge-intensive case-based reasoning system has profit of the domain knowledge, together with the case base. Therefore, acquiring new pieces of domain knowledge should improve the accuracy of such a system. This paper presents an approach for knowledge acquisition based on some failures of the system. The CBR system is assumed to produce solutions(More)
Le raisonnement à partir de cas (RÀPC) consiste à résoudre un problème en se remémorant et en adaptant des cas passés déjà résolus. Les systèmes de RÀPC manipulent des connaissances de natures diverses : les cas, les connaissances du domaine, les connaissances de similarité et d'adaptation. Les cas sont collectés de manière graduelle lors de l'utilisation(More)