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Modèle profond pour le contrôle vocal adaptatif d'un habitat intelligent

Abstract : Smart-homes, resulting of the merger of home-automation, ubiquitous computing and artificial intelligence, support inhabitants in their activity of daily living to improve their quality of life.Allowing dependent and aged people to live at home longer, these homes provide a first answer to society problems as the dependency tied to the aging population.In voice controlled home, the home has to answer to user's requests covering a range of automated actions (lights, blinds, multimedia control, etc.).To achieve this, the control system of the home need to be aware of the context in which a request has been done, but also to know user habits and preferences.Thus, the system must be able to aggregate information from a heterogeneous home-automation sensors network and take the (variable) user behavior into account.The development of smart home control systems is hard due to the huge variability regarding the home topology and the user habits.Furthermore, the whole set of contextual information need to be represented in a common space in order to be able to reason about them and make decisions.To address these problems, we propose to develop a system which updates continuously its model to adapt itself to the user and which uses raw data from the sensors through a graphical representation.This new method is particularly interesting because it does not require any prior inference step to extract the context.Thus, our system uses deep reinforcement learning; a convolutional neural network allowing to extract contextual information and reinforcement learning used for decision-making.Then, this memoir presents two systems, a first one only based on reinforcement learning showing limits of this approach against real environment with thousands of possible states.Introduction of deep learning allowed to develop the second one, ARCADES, which gives good performances proving that this approach is relevant and opening many ways to improve it.
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Submitted on : Monday, June 18, 2018 - 4:59:05 PM
Last modification on : Thursday, November 19, 2020 - 1:01:55 PM
Long-term archiving on: : Thursday, September 20, 2018 - 3:23:57 AM


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  • HAL Id : tel-01818123, version 1



Alexis Brenon. Modèle profond pour le contrôle vocal adaptatif d'un habitat intelligent. Intelligence artificielle [cs.AI]. Université Grenoble Alpes, 2017. Français. ⟨NNT : 2017GREAM057⟩. ⟨tel-01818123⟩



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