Abstract
How can a smart home system control a connected device to be in a desired state? Recent developments in the Internet of Things (IoT) technology enable people to control various devices with the smart home system rather than physical contact. Furthermore, smart home systems cooperate with voice assistants such as Bixby or Alexa allowing users to control their devices through voice. In this process, a user’s query clarifies the target state of the device rather than the actions to perform. Thus, the smart home system needs to plan a sequence of actions to fulfill the user’s needs. However, it is challenging to perform action planning because it needs to handle a large-scale state transition graph of a real-world device, and the complex dependence relationships between capabilities. In this work, we propose SmartAid (Smart Home Action Planning in awareness of Dependency), an action planning method for smart home systems. To represent the state transition graph, SmartAid learns models that represent the prerequisite conditions and operations of actions. Then, SmartAid generates an action plan considering the dependencies between capabilities and actions. Extensive experiments demonstrate that SmartAid successfully represents a real-world device based on a state transition log and generates an accurate action sequence for a given query.
Funder
Samsung Electronics
Institute of Engineering Research, Seoul National University
Institute of Computer Technology, Seoul National University
Publisher
Public Library of Science (PLoS)
Reference29 articles.
1. Smart homes: How much will they support us? A research on recent trends and advances;A Zielonka;IEEE Access,2021
2. Rafailidis D, Manolopoulos Y. Can virtual assistants produce recommendations? In: Proceedings of the 9th International Conference on Web Intelligence, Mining and Semantics; 2019. p. 1–6.
3. Jeon H, Kim J, Yoon H, Lee J, Kang U. Accurate Action Recommendation for Smart Home via Two-Level Encoders and Commonsense Knowledge. In: Proceedings of the 31st ACM International Conference on Information & Knowledge Management; 2022. p. 832–841.
4. Xiao J, Zou Q, Li Q, Zhao D, Li K, Tang W, et al. User Device Interaction Prediction via Relational Gated Graph Attention Network and Intent-aware Encoder. In: Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems; 2023. p. 1634–1642.
5. Isyanto H, Arifin AS, Suryanegara M. Design and implementation of IoT-based smart home voice commands for disabled people using Google Assistant. In: 2020 International Conference on Smart Technology and Applications (ICoSTA). IEEE; 2020. p. 1–6.