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Gestion de l'incertitude pour l'optimisation de systèmes interactifs

Abstract : The behaviour of machines is difficult to define, especially when machines have to adapt to a changing environment. For example, this is the case when human-machine interactions are concerned. Indeed, the machine has to deal with several sources of uncertainty to exhibit a consistent behaviour to the user. First, it has to deal with the different behaviours of the users and also with a change in the behaviour of a user when he gets used to the machine. Secondly, the communication between the user and the machine can be noisy, which makes the transfer of information more complicated. The objective is thus to deal with the different sources of uncertainty to show a consistent behaviour. Usually, dealing with uncertainties is performed by introducing models : models of the users, the task concerned or the decision. However, the accuracy of the solution depends on the accuracy of expert knowledge used to build the models. If machine learning, through reinforcement learning, has successfully avoided the use of model for the decision and removed \textit{ad hoc} knowledge about it, expert knowledge is still necessary. The thesis presented in this work is that some constraints related to human expertise can be slackened without a loss of generality related to the introduction of models
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Submitted on : Thursday, March 29, 2018 - 12:44:00 PM
Last modification on : Tuesday, April 19, 2022 - 10:10:31 AM


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Lucie Daubigney. Gestion de l'incertitude pour l'optimisation de systèmes interactifs. Autre [cs.OH]. Université de Lorraine, 2013. Français. ⟨NNT : 2013LORR0099⟩. ⟨tel-01750370⟩



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