We aim to investigate the potential impacts of smart homes on human behavior. To this end, we simulate a series of human models capable of performing various activities inside a reinforcement learning-based smart home. We then investigate the possibility of human behavior being altered as a result of the smart home and the human model adapting to one-another. We design a semi-Markov decision process human task interleaving model based on hierarchical reinforcement learning that learns to make decisions to either pursue or leave an activity. We then integrate our human model in the smart home which is based on Q-learning. We show that a smart home trained on a generic human model is able to anticipate and learn the thermal preferences of human models with intrinsic rewards similar to the generic model. The hierarchical human model learns to complete each activity and set optimal thermal settings for maximum comfort. With the smart home, the number of time steps required to change the thermal settings are reduced for the human models. Interestingly, we observe that small variations in the human model reward structures can lead to the opposite behavior in the form of unexpected switching between activities which signals changes in human behavior due to the presence of the smart home.
PBRE: A Rule Extraction Method from Trained Neural Networks Designed for Smart Home Services
Télécom Paris（巴黎电信学院）；EDF R&D（法国电力研发中心）
当同时部署具有大量传感器和执行器的多个服务时，设计灵巧家庭服务是一项复杂的任务。它可能依赖于基于知识或数据驱动的方法。前者可以使用基于规则的方法静态地设计服务，后者可以使用学习方法动态地发现居民的偏好。然而，这两种方法都不能完全令人满意，因为规则不能涵盖所有可能发生变化的情况，并且学习方法可能会做出居民有时无法理解的决定。提出了一种基于教学法的规则提取器PBRE（Pedagogic Based Rule Extractor），用于从学习方法中提取规则，实现灵巧家庭系统的动态规则生成。预期的优点是，基于规则的方法的可解释性和学习方法的动态性。我们比较PBRE与现有的规则提取方法，结果表明PBRE更好的性能。我们还应用PBRE从由NRL（基于神经网络的强化学习）表示的灵巧家庭服务中提取规则。结果表明，PBRE可以帮助NRL模拟服务，使居民可以理解的建议。
SMH-Bench: Benchmarking LLM Agents for Environment-Grounded Reasoning and Action in Smart Homes
Midea Group（美的集团）；Beijing University of Posts and Telecommunications（北京邮电大学）；Donghua University（东华大学）；The University of Sydney（悉尼大学）；Peking University（北京大学）
SmartBench: Evaluating LLMs in Smart Homes with Anomalous Device States and Behavioral Contexts
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；Jilin University（吉林大学）；The Chinese University of Hong Kong（香港中文大学）；Pengcheng Laboratory（鹏城实验室）
Identifying and Addressing User-level Security Concerns in Smart Homes Using "Smaller" LLMs
Department of CSE Bangladesh University of Engineering（计算机科学与工程系 bangladesh 工程大学）；Concordia Institute for Information Systems Engineering Concordia University Montreal, Canada（信息系统工程研究所 博士大学 蒙特利尔 加拿大）
Layout Agnostic Human Activity Recognition in Smart Homes through Textual Descriptions Of Sensor Triggers (TDOST)
School of Interactive Computing, Georgia Institute of Technology（佐治亚理工学院交互计算学院）；School of Electrical and Computer Engineering, Georgia Institute of Technology（佐治亚理工学院电气与计算机工程学院）
Explaining AI Decisions: Towards Achieving Human-Centered Explainability in Smart Home Environments
Fraunhofer Institute for Applied Information Technology FIT（弗劳恩霍夫应用信息技术研究所）；University of Siegen（锡根大学）；Bonn-Rhein-Sieg University of Applied Sciences（波恩-莱茵-锡格应用科学大学）
SmartHomeSecure: Automated Detection and Repair of Smart Home Configuration Errors Using Large Language Models
Myers-Lawson School of Construction, Virginia Polytechnic Institute and State University（Myers-Lawson工程学院，弗吉尼亚理工学院和州立大学）；School of Technology, Eastern Illinois University（技术学院，东伊利诺伊大学）
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes
Tsinghua Shenzhen International Graduate School（清华大学深圳国际研究生院）；PengCheng Laboratory（鹏城实验室）；The Chinese University of Hong Kong（香港中文大学）；University of Warwick（华威大学）
Energy saving in smart homes based on consumer behaviour: A case study
Institute of Business Information Systems Research and Development（商业信息系统研发研究所）；University of Applied Sciences and Arts Northwestern Switzerland（瑞士西北应用科学与艺术大学）；digitalSTROM AG（digitalSTROM股份公司）
