ABSTRACT Faced with the fragmented and heterogeneous character of knowledge regarding complex foo... more ABSTRACT Faced with the fragmented and heterogeneous character of knowledge regarding complex food systems, we have developed a practical methodology, in the framework of the dynamic Bayesian networks associated with Dirichlet distributions, able to incrementally build and update model parameters each time new information is available whatever its source and format. From a given network structure, the method consists in using a priori Dirichlet distributions that may be assessed from literature, empirical observations, experts opinions, existing models, etc. Next, they are successively updated by using Bayesian inference and the expected a posteriori each time new or additional information is available and can be formulated into a frequentist form. This method also enables to take (1) uncertainties pertaining to the system; (2) the confidence level on the different sources of information into account. The aim is to be able to enrich the model each time a new piece of information is available whatever its source and format in order to improve the representation and thus provide a better understanding of systems. We have illustrated the feasibility and practical using of our approach in a real case namely the modelling of the Camembert-type cheese ripening.
2010 10th International Conference on Intelligent Systems Design and Applications, 2010
An Intelligent Tutoring Systems (ITS) is concerned with the construction of intelligent softwares... more An Intelligent Tutoring Systems (ITS) is concerned with the construction of intelligent softwares helping students overcoming different problems in their learning process. We present in this work a novel Multicriteria Bayesian Intelligent Tutoring System MBITS used to help students overcoming their their lack of comprehension of concepts in a course. It is based on a Bayesian Network (BN) to model a course and a multicriteria approach to evaluate different solution and information gathering actions using the same set of criteria. MBITS is an automated troubleshooter presented as an interactive and easy to use Web application.
ABSTRACT Faced with the fragmented and heterogeneous character of knowledge regarding complex foo... more ABSTRACT Faced with the fragmented and heterogeneous character of knowledge regarding complex food systems, we have developed a practical methodology, in the framework of the dynamic Bayesian networks associated with Dirichlet distributions, able to incrementally build and update model parameters each time new information is available whatever its source and format. From a given network structure, the method consists in using a priori Dirichlet distributions that may be assessed from literature, empirical observations, experts opinions, existing models, etc. Next, they are successively updated by using Bayesian inference and the expected a posteriori each time new or additional information is available and can be formulated into a frequentist form. This method also enables to take (1) uncertainties pertaining to the system; (2) the confidence level on the different sources of information into account. The aim is to be able to enrich the model each time a new piece of information is available whatever its source and format in order to improve the representation and thus provide a better understanding of systems. We have illustrated the feasibility and practical using of our approach in a real case namely the modelling of the Camembert-type cheese ripening.
2010 10th International Conference on Intelligent Systems Design and Applications, 2010
An Intelligent Tutoring Systems (ITS) is concerned with the construction of intelligent softwares... more An Intelligent Tutoring Systems (ITS) is concerned with the construction of intelligent softwares helping students overcoming different problems in their learning process. We present in this work a novel Multicriteria Bayesian Intelligent Tutoring System MBITS used to help students overcoming their their lack of comprehension of concepts in a course. It is based on a Bayesian Network (BN) to model a course and a multicriteria approach to evaluate different solution and information gathering actions using the same set of criteria. MBITS is an automated troubleshooter presented as an interactive and easy to use Web application.
Uploads
Papers by Pierre-Henri Wuillemin