Q-SMASH：基于Q学习的以人为中心的物联网自适应
Q-SMASH: Q-Learning-based Self-Adaptation of Human-Centered Internet of Things
摘要：
随着以人为中心的物联网（HCIOT）应用数量的增加，其服务和设备的自适应正在成为解决决策过程中环境不确定性的基本要求。HCIOT的自适应旨在管理动态环境中的运行时更改，并调整IoT对象的功能，以便在执行期间实现预期目标。SMASH是一个支持语义的多智能体系统，用于HCIOT的自适应，可以自主地使物联网对象适应其环境的不确定性。SMASH只根据用户的人性价值来解决物联网应用的自适应，而没有解决用户的行为。本文介绍了Q-SMASH：一种基于多智能体强化学习的方法，用于在以人为中心的环境中自适应物联网对象。Q-SMASH旨在学习用户的行为，同时尊重人类价值观。Q-SMASH的学习能力使其能够适应用户的行为变化，并在不同的状态和情况下做出更准确的决策。
英文摘要：
As the number of Human-Centered Internet of Things (HCIoT) applications increases, the self-adaptation of its services and devices is becoming a fundamental requirement for addressing the uncertainties of the environment in decision-making processes. Self-adaptation of HCIoT aims to manage run-time changes in a dynamic environment and to adjust the functionality of IoT objects 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 addressed. This article presents Q-SMASH: a multi-agent reinforcement learning-based approach for self-adaptation of IoT objects in human-centered environments. Q-SMASH aims to learn the behaviors of users along with respecting human values. 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
