Béatrice Fuchs

Learn More
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)
This paper presents an approach to context-aware assisting systems that reuse user's personal experience as an alternative to traditional systems having an explicit context model associated to reasoning capabilities on this model. This approach proposes to model the use of the environment through interaction traces representing user's experience and(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)
In Case Based Reasoning (CBR), knowledge acquisition plays an important role as it allows to progressively improve the system's competencies. One of the approaches of knowledge acquisition consists in performing it while the system is used to solve a problem. An advantage of this strategy is that it is not to constraining for the expert: the system exploits(More)
ces connaissances, celles qui sont utilisées pour le raisonnement telles que les mesures de similarité ou les connaissances d'adaptation sont délicatesà acquérir lors de la conception initiale du système. C'est la raison pour laquelle leur apprentissage revêt un enjeu particulier. Nous sommes convaincus que les mesures de similarité sont directement liées(More)