Title: Arcades: A deep model for adaptive decision making in voice controlled smart-home,
Abstract: 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.