FaiR-IoT：公平感知的人在回路强化学习，用于利用个性化IoT中的人的可变性
FaiR-IoT: Fairness-aware Human-in-the-Loop Reinforcement Learning for Harnessing Human Variability in Personalized IoT
摘要：
由于可穿戴技术的快速发展，监测复杂的人类环境变得可行，为开发人在环物联网系统铺平了道路，这些系统可以自然进化以自主适应人类和环境状态。然而，设计这种个性化物联网应用程序的一个核心挑战来自人类的可变性。这种可变性源于以下事实：不同的人在与IoT应用交互时表现出不同的行为（人内可变性），同一个人在与同一个IoT应用交互时可能会随着时间的推移而改变行为（人间可变性），并且人类行为可能会受到同一环境中其他人的行为的影响（多人可变性）。为此，我们提出了FaiR-IoT，这是一个基于强化学习的通用框架，用于自适应和公平感知的人在环物联网应用。在FaiR-IoT中，三个级别的强化学习代理相互作用，以不断学习人类偏好，并最大限度地提高系统的性能和公平性，同时考虑到人类内部，人类之间和多人的可变性。我们验证了两个应用程序，即（i）人在环汽车高级驾驶辅助系统和（ii）人在环智能家居提出的框架。在这两个应用程序上获得的结果验证了FaiR-IoT的通用性及其提供个性化体验的能力，同时与非个性化系统相比，将系统性能提高了40%-60%，并将多人系统的公平性提高了1.5个数量级。
英文摘要：
Thanks to the rapid growth in wearable technologies, monitoring complex human context becomes feasible, paving the way to develop human-in-the-loop IoT systems that naturally evolve to adapt to the human and environment state autonomously. Nevertheless, a central challenge in designing such personalized IoT applications arises from human variability. Such variability stems from the fact that different humans exhibit different behaviors when interacting with IoT applications (intra-human variability), the same human may change the behavior over time when interacting with the same IoT application (inter-human variability), and human behavior may be affected by the behaviors of other people in the same environment (multi-human variability). To that end, we propose FaiR-IoT, a general reinforcement learning-based framework for adaptive and fairness-aware human-in-the-loop IoT applications. In FaiR-IoT, three levels of reinforcement learning agents interact to continuously learn human preferences and maximize the system's performance and fairness while taking into account the intra-, inter-, and multi-human variability. We validate the proposed framework on two applications, namely (i) Human-in-the-Loop Automotive Advanced Driver Assistance Systems and (ii) Human-in-the-Loop Smart House. Results obtained on these two applications validate the generality of FaiR-IoT and its ability to provide a personalized experience while enhancing the system's performance by 40%-60% compared to non-personalized systems and enhancing the fairness of the multi-human systems by 1.5 orders of magnitude.
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