Learning User Preferences in Ubiquitous Systems: A User Study and a Reinforcement Learning Approach

Sofia Zaidenberg 1 Patrick Reignier 2 Nadine Mandran 3
1 STARS - Spatio-Temporal Activity Recognition Systems
CRISAM - Inria Sophia Antipolis - Méditerranée
2 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 : Our study concerns a virtual assistant, proposing services to the user based on its current perceived activity and situation (ambient intelligence). Instead of asking the user to define his preferences, we acquire them automatically using a reinforcement learning approach. Experiments showed that our system succeeded the learning of user preferences. In order to validate the relevance and usability of such a system, we have first conducted a user study. 26 non-expert subjects were interviewed using a model of the final system. This paper presents the methodology of applying reinforcement learning to a real-world problem with experimental results and the conclusions of the user study.
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Sofia Zaidenberg, Patrick Reignier, Nadine Mandran. Learning User Preferences in Ubiquitous Systems: A User Study and a Reinforcement Learning Approach. Artificial Intelligence Applications and Innovations, Harris Papadopoulos and Andreas S. Andreou and Max Bramer, Oct 2010, Larnaca, Cyprus. pp.336-343, ⟨10.1007/978-3-642-16239-8_44⟩. ⟨hal-00788028⟩

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