用于照明系统的强化学习自适应模糊控制器：在飞机客舱中的应用
Reinforcement learning adaptive fuzzy controller for lighting systems: application to aircraft cabin
AI总结：
针对智能照明难以适配个体偏好的问题，本文提出融合模糊推理系统与Q-learning的自适应照明算法，并在飞机客舱模型中通过用户研究验证其在线学习与个性化适配能力。
摘要：
照明需求具有主观性，单一照明设置无法适用于所有人。然而，目前关于开发能够适应用户偏好的智能照明算法的研究很少。为弥补这一空白，本文利用模糊逻辑和强化学习开发了一种自适应照明算法。具体而言，我们利用领域知识开发了一个基线模糊推理系统（FIS）。我们基于现有文献构建了一个FIS，该系统根据环境条件（即日眩光指数）以及包括年龄、活动和睡眠类型在内的用户信息生成照明设置建议。通过反馈机制，用户与算法进行交互，根据自身偏好修正算法输出。我们将这些修正解释为给予Q-learning智能体的奖励，该智能体在线调整FIS参数，以匹配用户偏好。我们在飞机客舱模型中实现了该算法，并开展了广泛的用户研究，以评估算法的有效性并理解其学习行为。实现结果表明，所开发的算法能够学习用户偏好，同时成功适应广泛的环境条件和用户特征，并能够处理多种多样的环境条件和用户特征。这凸显了其作为具备先进学习能力的智能照明管理有效解决方案的可行性。
英文摘要：
The lighting requirements are subjective and one light setting cannot work for all. However, there is little work on developing smart lighting algorithms that can adapt to user preferences. To address this gap, this paper uses fuzzy logic and reinforcement learning to develop an adaptive lighting algorithm. In particular, we develop a baseline fuzzy inference system (FIS) using the domain knowledge. We use the existing literature to create a FIS that generates lighting setting recommendations based on environmental conditions i.e. daily glare index, and user information including age, activity, and chronotype. Through a feedback mechanism, the user interacts with the algorithm, correcting the algorithm output to their preferences. We interpret these corrections as rewards to a Q-learning agent, which tunes the FIS parameters online to match the user preferences. We implement the algorithm in an aircraft cabin mockup and conduct an extensive user study to evaluate the effectiveness of the algorithm and understand its learning behavior. Our implementation results demonstrate that the developed algorithm possesses the capability to learn user preferences while successfully adapting to a wide range of environmental conditions and user characteristics. and can deal with a diverse spectrum of environmental conditions and user characteristics. This underscores its viability as a potent solution for intelligent light management, featuring advanced learning capabilities.
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。
