EAGER: Developing a Mathematical Framework to Enable Bi-Directional Interactions of Humans with Smart Engineered Systems Using Relational Elements

建筑消耗了我们国家总能源消耗的38%。解决这一问题的现有自动化方法要么集中在建筑物上，让它们更好地感知和响应居住者的行为，要么集中在居住者身上，试图教育他们做出更有效的能源选择。相比之下，这个探索性研究的早期概念拨款（EAGER）项目考虑了增强建筑物和居住者之间互动的方法。研究团队假设，当构建用户与建筑自动化建立信任关系时，用户构建交互将是最有效的。通过开发数学模型和理论，通过关系特性增强用户能力，用户可以提高个人性能和建筑性能，同时也可以改善社会福祉。为了做到这一点，这项工作借鉴了行为科学的理论，以数学模型的方式建立了一个建筑何时以及如何与用户互动，以及这些互动应该如何构建的模型。研究结果将改变我们感知和体验当今建筑环境的方式，引领我们创造出前所未有的、专注的、有个性的建筑环境。该项目将通过免费的研究许可证提供模型和数据，将研究成果纳入工程课程，通过出版物以及国内和国际报告传播研究成果，从而加强研究和教育的基础设施。用户-虚拟人代理交互的建模框架是智能工程系统建模和设计的关键贡献，是工程学、行为科学和计算建模的交叉领域。如果成功，数学框架将用于设计与人双向互动的智能建筑。研究目标有助于实现使网络物理系统与人类互动和协作的最终目标。该项目将实验数据整合到数学模型中，测试建筑拟人化中嵌入的关系元素的包容性。这些模型将预测哪种响应最适合于建筑-用户交互。该模型还将受到现有说服理论工作的启发和约束。该模型将考虑各种环境、时间和个人因素，以及由于与建筑的持续互动而导致的用户反应的变化。多步建模方法结合了机器学习技术、分类问题的数学预测、统计模型（如马尔可夫模型）和自回归移动平均模型。特别是，贡献是双重的：(1)使用拟人化建筑物的虚拟人类代理对用户构建交互进行建模；(2)对人际信任和影响理论如何指导自动化设计进行基础研究。这项研究将有助于对人机团队合作的基本理解，包括阐明人们为什么以及如何与自动化系统建立联系的理论，并推进我们对自动化表现出的关系特征如何促进这些系统所服务的人群的行为改变的一般理解。

Buildings consume a staggering 38 percent of our nation's total energy use. Existing automation approaches to address this problem focus either on buildings, allowing them to better sense and respond to the behavior of their occupants, or focus on occupants, seeking to educate them on making more efficient energy choices. In contrast, this EArly-concept Grant for Exploratory Research (EAGER) project considers ways to enhance the interaction between buildings and occupants. The research team hypothesizes that user-building interactions will be most effective when building users establish a relationship of trust with building automation. By developing mathematical models and theory that amplify user capabilities through relational features, users are empowered to improve individual performance as well as building performance, while also improving societal well-being. To do so, the work draws on theories from the behavioral sciences to mathematically model when and how a building should interact with a user and how these interactions should be framed. The results will change the way we perceive and experience today's built environments, leading what could become the creation of unprecedented built environments that are attentive and have an identity. The project will enhance infrastructure for research and education by making the models and data available via a free research license, incorporating research findings into the engineering curriculum, disseminating research findings via publications, and national and international presentations. The modeling framework for user-virtual human agent interactions is the key contribution to smart engineered systems modeling and design and occurs at the intersection of engineering, the behavioral sciences and computational modeling. If successful, the mathematical framework will be used to design smart buildings that have two-way interactions with people. The research objectives contribute to the ultimate goal of enabling cyber-physical systems to interact and collaborate with humans. This project integrates experimental data into the mathematical models, testing the inclusion of relational elements embedded in the personification of a building. The models will predict which response is the most suitable for a building-user interaction. This model will also be informed and constrained by existing theoretical work on persuasion. The model will account for various contextual, temporal and personal factors as well as the changes in user response due to continuous interactions with the building. The multiple-step modeling methodology incorporates a combination of machine learning techniques, mathematical projections for the classification problem, and statistical models such as Markov model, and autoregressive moving average models. In particular, the contributions are twofold: (1) modeling user-building interactions using virtual human agents personifying buildings; and (2) performing fundamental research on how theories of human interpersonal trust and influence can inform the design of automation. The research will contribute to the fundamental understanding of human-machine teamwork, including elucidating theories of why and how people build connections with automated systems and advance our general understanding of how automation exhibiting relational features can facilitate behavior change in the population served by those systems.