As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of IoT services and devices is becoming a fundamental requirement for addressing the uncertainties of their environment in decision-making processes. Self-adaptation of HCIoT aims to manage run-time changes and to adjust the functionality of IoT devices in order to achieve desired goals during execution. SMASH is a semantic-enabled multi-agent system for self-adaptation of HCIoT that autonomously adapts IoT objects to uncertainties of their environment. SMASH addresses the self-adaptation of IoT applications only according to the human values of users, while the behavior of users is not considered. This article presents Q-SMASH: a multi-agent reinforcement learning-based approach for self-adaptation of IoT objects in human-centered environments. Q-SMASH uses Q-Learning and aims to learn the behaviors of users along with respecting human values automatically. The learning ability of Q-SMASH allows it to adapt itself to the behavioral change of users and make more accurate decisions in different states and situations.
CITATION STYLE
Rahimi, H., Trentin, I. F., Ramparany, F., & Boissier, O. (2021). -SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things. In ACM International Conference Proceeding Series (pp. 694–698). Association for Computing Machinery. https://doi.org/10.1145/3486622.3493974
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