Context Feature Learning through Deep Learning for Adaptive Context-Aware Decision Making in the Home

Abstract : In Intelligent Environments, prediction and decision must take the context of interaction into account to adapt themselves to the evolving environment. If most of the approaches to deal with this problem have used a formal representation of context, we present in this paper a direct extraction of the context from raw sensor data using deep neural network and reinforcement learning. Experiments undertaken in a voice con- trolled smart home showed which elements are useful to perform context-aware decision-making in the home and the adequacy of reinforcement learning to tackle an evolving environment.
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Communication dans un congrès
The 14th International Conference on Intelligent Environments, Jun 2018, Rome, Italy. The 14th International Conference on Intelligent Environments 2018, 〈IEEE〉. 〈10.1109/IE.2018.00013〉
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Alexis Brenon, François Portet, Michel Vacher. Context Feature Learning through Deep Learning for Adaptive Context-Aware Decision Making in the Home. The 14th International Conference on Intelligent Environments, Jun 2018, Rome, Italy. The 14th International Conference on Intelligent Environments 2018, 〈IEEE〉. 〈10.1109/IE.2018.00013〉. 〈hal-01802747〉

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