Bernard Lefebvre
Université du Québec à Montréal
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WIT Transactions on Biomedicine and Health | 2003
Hélène Pigot; Bernard Lefebvre; Jean-Guy Meunier; Brigitte Kerhervé; André Mayers; Sylvain Giroux
The intelligent habitat is made of fixed components (movements detectors and intelligent electric household appliances) and small mobile processors worn by the elder. Fixed and mobile components communicate to assist the elder in performing his tasks and to intervene in case of risk. The system has two types of features: those carried out inside the residence (information acquisition, cognitive help like sound or visual cues when everyday life activity is carried out in an incomplete or dangerous way) and those reporting to the relatives and the external care network major risk events or evolution of the elder health state. The system intervention with the elder must be personalized according to the incurred risk gravity, his health state, his life habits and his preferred interaction mode: image, text, sound, voice ...
Applied Artificial Intelligence | 2005
Bernard Lefebvre; Gilles Gauthier; Serge Tadié; Tran Huu Duc; Hicham Achaba
In this paper, we present a competence ontology for domain knowledge dissemination and retrieval services, which has been used in the MDKT project (Management and Dissemination of Knowledge in Telecommunication). The main objective of this project is to set up a computerized knowledge management system related to a specific domain in order to develop the human resources expertise for the needs of the enterprise. In the case of this project, the knowledge is about wireless networking and is expressed in digital documents. Among all the ontologies that implement the knowledge needed by the system, the competence ontology plays a key role. The competence ontology defines at a meta-level the concept of competence and its relationships with other concepts such as document or user. Its instantiation is used to characterize a user model and a document model. This knowledge organization makes it possible to infer which document, or more generally which domain knowledge information, is suitable for a given person or to whom specific domain knowledge information should be disseminated.
ACM Sigcue Outlook | 2001
André Mayers; Bernard Lefebvre; Claude Frasson
Miace as a human cognitive architecture is a computational model that explains how a student acquires, encodes and uses domain knowledge. Because Miace takes into account the cognitive psychological laws and the environment in which the student works, it can be used as a virtual student in help systems dedicated to pedagogical formation, in intelligent tutoring systems, in cooperative learning applications and for the conception of didactic material. This paper describes the implementation of Miace and discusses the Miace theoretical components from three point of view: temporal, their roles in cognitive activity and their generic or functional forms. A comparison is done to show the originality and the contribution of Miace in user modeling.
international conference on advanced learning technologies | 2006
Abderrahim Danine; Bernard Lefebvre; André Mayers
We present in this paper an intelligent tutoring system using a Bayesian network. This tutor is dedicated to the analysis and diagnosis of students errors. The elaboration of such a system necessitates nearly always taking into consideration information that is potentially incomplete or uncertain. Indeed, in a learning situation, we can neither know exactly the students plan nor his goal. In addition, we cannot observe what the student knows or does not know, but we can only make imperfect estimations through his actions. In order to model the student in this situation, we designed and implemented an intelligent system that uses Bayesian networh.
intelligent tutoring systems | 1998
Jean-Yves Rossignol; Claude Frasson; Bernard Lefebvre
In the past, Knowledge Acquisition represented a barrier for developing intelligent systems, and particularly intelligent tutoring systems. In a recent work we proposed a model (Monaco-T ) for structuring and representing cooperative tasks. These tasks can be used directly in a cooperative learning environment, or in a simulation environment able to show how the cooperative task is effectively realized. To help experts representing knowledge with this model, we have built an editor able to produce tasks in Monaco-T format. The editor has graphic capabilities for editing rules, trees and layers, that are the basic elements of Monaco-T. Our editor also provides tools to directly link cooperative tasks to an external system able to react to direct manipulation. It represents an environment for component oriented programming.
Archive | 2002
Hélène Pigot; André Mayers; Sylvain Giroux; Bernard Lefebvre; Vincent Rialle; Norbert Noury
mexican international conference on artificial intelligence | 2005
Yassine Gargouri; Bernard Lefebvre; Jean Meunier
intelligent tutoring systems | 1996
André Mayers; Bernard Lefebvre
Explication '96. Journées | 1996
Claude Frasson; Bernard Lefebvre
intelligent tutoring systems | 1992
André Mayers; Bernard Lefebvre