Showing papers by "Amélie Cordier published in 2007"
Proceedings Article•
01 Aug 2007
TL;DR: An approach for exploiting domain knowledge in a case-based decision support system in the domain of oncology based on the so-called conservative adaptation that provides a solution necessarily consistent with the domain knowledge.
Abstract: Domain knowledge may be used in a medical application to avoid wrong decisions, e.g., decisions raising contraindications. The contribution of this paper is twofold. First, it presents an approach for exploiting domain knowledge in a case-based decision support system in the domain of oncology. This approach is based on the so-called conservative adaptation that provides a solution necessarily consistent with the domain knowledge. Second, this paper describes an approach for the evolution of this domain knowledge when the expert rejects a proposed solution as being inconsistent with his/her knowledge. This inconsistency is characteristic of a difference between the domain knowledge of the system and the expert knowledge; from an interactive analysis, a piece of knowledge to be added to the domain knowledge is pointed out. This approach to domain knowledge evolution is implemented in a prototype called FrakaS.
16 citations
13 Aug 2007
TL;DR: This paper presents an approach for knowledge acquisition based on some failures of the cbr system: an interactive analysis of this failure, some knowledge is acquired that contributes to fill the gap from the system knowledge to the expert knowledge.
Abstract: 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 that are consistent with the domain knowledge but that may be inconsistent with the expert knowledge, and this inconsistency constitutes a failure. Thanks to an interactive analysis of this failure, some knowledge is acquired that contributes to fill the gap from the system knowledge to the expert knowledge. Another type of failures occurs when the solution produced by the system is only partial: some additional pieces of information are required to use it. Once again, an interaction with the expert involves the acquisition of new knowledge. This approach has been implemented in a prototype, called FrakaS , and tested in the application domain of breast cancer treatment decision support.
13 citations
Proceedings Article•
01 Aug 2007
TL;DR: Two approaches of interactive knowledge acquisition in CBR rely on the exploitation of reasoning failures: an interactive learning process aiming at correcting the solution and at learning new knowledge is triggered when a reasoning failure occurs.
Abstract: 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 its interactions to acquire pieces of knowledge it needs to solve the current problem and takes the opportunity to learn this new knowledge for future use. In this paper, we present two approaches of interactive knowledge acquisition in CBR. Both approaches rely on the exploitation of reasoning failures. Indeed, an interactive learning process aiming at correcting the solution and at learning new knowledge is triggered when a reasoning failure occurs.
11 citations
02 Jul 2007
TL;DR: In this article, the authors decrivons l'architecture IakA, which guides la mise en application des principes de l'acquisition opportuniste de connaissances d'adaptation.
Abstract: En raisonnement a partir de cas, l'acquisition de connaissances au fil des sessions de resolution de problemes revet un enjeu particulier ; elle permet l'amelioration progressive des competences du systeme sans contrainte pour l'expert. Parmi les differents types de connaissances a acquerir se trouvent les connaissances d'adaptation. L'acquisition opportuniste de connaissances d'adaptation repose sur l'exploitation des interactions entre l'expert et le systeme. Dans cet article, nous decrivons l'architecture IakA qui guide la mise en application des principes de l'acquisition opportuniste. Nous presentons egalement KayaK, un prototype developpe dans le but de valider l'architecture.
10 citations
02 Jul 2007
TL;DR: In this article, a systeme de Raisonnement a partir de cas (RaPC) s'appuie sur des connaissances du domaine, en plus de la base de cas.
Abstract: Un systeme de raisonnement a partir de cas (RaPC) s'appuie sur des connaissances du domaine, en plus de la base de cas. L'acquisition de nouvelles connaissances du domaine doit ameliorer les resultats d'un tel systeme. Cet article presente une approche pour une telle acquisition de connaissances qui est fondee sur les echecs du systeme. Le systeme de RaPC considere est suppose produire des solutions qui sont coherentes avec les connaissances du domaine mais mais ces solutions peuvent etre incoherentes avec les connaissances de l'expert et cette incoherence constitue une situation d'echec. Grâce a une analyse interactive de cet echec, des connaissances sont acquises qui contribuent a remplir le fosse existant entre les connaissances du systeme et celles de l'expert. Un autre type d'echec apparait quand la solution presentee par le systeme n'est que partielle : certaines informations additionnelles sont requises pour pouvoir exploiter cette solution. Une fois de plus, l'interaction avec l'expert entraine une acquisition de nouvelles connaissances. Cette approche a ete implantee dans un prototype, baptise FrakaS, et teste sur un exemple dans le domaine d'application de l'aide a la decision therapeutique en cancerologie du sein.
2 citations