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Apprentissage par renforcement de modeles de contexte pour l'informatique ambiante

Sofia Zaidenberg 1 
1 PRIMA - Perception, recognition and integration for observation of activity
Inria Grenoble - Rhône-Alpes, UJF - Université Joseph Fourier - Grenoble 1, INPG - Institut National Polytechnique de Grenoble , CNRS - Centre National de la Recherche Scientifique : UMR5217
Abstract : This thesis studies the automatic acquisition by machine learning of a context model for a user in a ubiquitous environment. In such an environment, devices can communicate and cooperate in order to create a consistent computerized space. Some devices possess perceptual capabilities. The environment uses them to detect the user's situation - his context. Other devices are able to execute actions. Our problematics consists in determining the optimal associations, for a given user, between situations and actions. Machine learning seems to be a sound approach since it results in a customized environment without requiring an explicit specification from the user. A life long learning lets the environment adapt itself continuously to world changes and user preferences changes. Reinforcement learning can be a solution to this problem, as long as it is adapted to some particular constraints due to our application setting.
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Submitted on : Monday, July 5, 2010 - 3:16:34 PM
Last modification on : Friday, March 25, 2022 - 11:10:24 AM
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  • HAL Id : tel-00497656, version 1



Sofia Zaidenberg. Apprentissage par renforcement de modeles de contexte pour l'informatique ambiante. Autre [cs.OH]. Institut National Polytechnique de Grenoble - INPG, 2009. Français. ⟨tel-00497656⟩



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