Arcades: A deep model for adaptive decision making in voice controlled smart-home
This addresses the need for robust, context-aware control in smart-homes, but it appears incremental as it builds on existing deep reinforcement learning methods for adaptation.
The paper tackles the problem of adaptive decision-making in voice-controlled smart-homes by proposing Arcades, a deep reinforcement learning system that extracts context from a graphical representation and continuously updates behavior based on user interactions, with experiments on realistic data showing it promises long-life context-aware control.
In a voice-controlled smart-home, a controller must respond not only to user's requests but also according to the interaction context. This paper describes Arcades, a system which uses deep reinforcement learning to extract context from a graphical representation of home automation system and to update continuously its behavior to the user's one. This system is robust to changes in the environment (sensor breakdown or addition) through its graphical representation (scale well) and the reinforcement mechanism (adapt well). The experiments on realistic data demonstrate that this method promises to reach long life context-aware control of smart-home.