# Arcades: A deep model for adaptive decision making in voice controlled smart-home

arXiv: https://arxiv.org/abs/1807.01970 · HTML: https://mlsanity.com/paper/1807.01970/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jul 2018.

## Built on by (most cited first, 1 of 1)

- [A Federated Learning Aggregation Algorithm for Pervasive Computing: Evaluation and Comparison](https://mlsanity.com/paper/2110.10223/) (2021)

## Abstract (arXiv)

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.