Reinforcement Learning of Context Models for a Ubiquitous Personal Assistant

Sofia Zaidenberg 1 Patrick Reignier 1 James L. Crowley 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 : Ubiquitous environments may become a reality in a foreseeable future and research is aimed on making them more and more adapted and comfortable for users. Our work consists on applying reinforcement learning techniques in order to adapt services provided by a ubiquitous assistant to the user. The learning produces a context model, associating actions to perceived situations of the user. Associations are based on feedback given by the user as a reaction to the behavior of the assistant. Our method brings a solution to some of the problems encountered when applying reinforcement learning to systems where the user is in the loop. For instance, the behavior of the system is completely incoherent at the be-ginning and needs time to converge. The user does not accept to wait that long to train the system. The user's habits may change over time and the assistant needs to integrate these changes quickly. We study methods to accelerate the reinforced learning process.
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Communication dans un congrès
Juan M. Corchado and Dante I. Tapia and José Bravo. UCAmI - 3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008, Oct 2008, Salamanca, Spain. Springer Berlin Heidelberg, 51, pp.254-264, 2008, Advances in Soft Computing. 〈10.1007/978-3-540-85867-6_30〉
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Sofia Zaidenberg, Patrick Reignier, James L. Crowley. Reinforcement Learning of Context Models for a Ubiquitous Personal Assistant. Juan M. Corchado and Dante I. Tapia and José Bravo. UCAmI - 3rd Symposium of Ubiquitous Computing and Ambient Intelligence 2008, Oct 2008, Salamanca, Spain. Springer Berlin Heidelberg, 51, pp.254-264, 2008, Advances in Soft Computing. 〈10.1007/978-3-540-85867-6_30〉. 〈hal-00788055〉

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