RI: Small: Collaborative Research: Preference Elicitation and Device Scheduling for Smart Homes

家庭自动化系统（HAS）是一种控制家庭智能设备的自动化系统，目的是提高舒适度，提高能源效率，降低运营成本。家庭自动化问题涉及确定用户偏好和约束，调度智能设备以满足用户约束并最小化能源成本，并向用户提出这些设备的调度并响应用户的更改。由于用户和他们的hase之间的交互可能是有限的，因此系统在调度用户和家庭设备之前引出所有的偏好和约束是不实际的。这个项目的总体目标是设计一个能够在用户和系统之间有限数量的交互中为用户找到可接受的解决方案的HAS。为了达到这个目的，本项目研究了一些技术，这些技术可以引起少量的偏好和限制，这些偏好和限制是为用户找到好的解决方案的关键。如果用户对建议的解决方案不满意，他们可以提供额外的首选项，这将指导搜索新的解决方案。通过对家庭自动化问题的调查，该项目将为网络物理系统领域做出必要的基础贡献，特别是在考虑与人类用户交互的算法技术方面。该项目的研究成果将改进未来系统的设计，并指导商业HAS的发展，这有可能影响未来的智能和互联社区。为了在有限数量的用户交互中找到可接受的解决方案，本项目将(1)建模与人类用户交互的麻烦成本；(2)改进偏好诱导技术，将干扰成本纳入其优化中；(3)使用矩阵补全算法组合，并利用其多样性来近似非诱导偏好及其相应的不确定性程度；(4)设计基于不确定偏好的鲁棒可扩展算法；(5)在向用户展示和解释这些时间表时，提出考虑麻烦成本的视觉和口头界面。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

A home automation system (HAS) is an automated system that controls a home's smart devices with the objective of improved comfort, improved energy efficiency, and reduced operational costs. The home automation problem involves determining user preferences and constraints, scheduling smart devices to satisfy user constraints and minimize energy costs, and proposing to users schedules for these devices and responding to users' changes. As interactions between users and their HASes are likely to be limited, it is not practical for the system to elicit all preferences and constraints prior to scheduling user and home devices. The overall goal in this project is to design an HAS that can find acceptable solutions for users within a bounded number of interactions between the user and system. Towards that end, this project investigates techniques that elicit a small number of preferences and constraints that are key in finding good solutions for the user. If users are unhappy with the proposed solution, they can provide additional preferences, which will guide the search for a new solution. Through investigations of the home automation problem, this project will make the necessary foundational contributions to the field of cyber-physical systems, especially in the algorithmic techniques that take into account interactions with human users. Findings from this project will improve the design of future systems and guide the development of commercial HAS, which has the potential to impact future smart and connected communities.In order to find acceptable solutions within a bounded number of user interactions, this project will (1) model the bother cost of interacting with human users; (2) improve preference elicitation techniques by incorporating bother costs in their optimization; (3) use a portfolio of matrix completion algorithms and leverage their diversity to approximate unelicited preferences and their corresponding degrees of uncertainty; (4) design robust and scalable algorithms that find schedules based on uncertain preferences; and (5) propose visual and verbal interfaces that take into account bother costs while presenting and explaining those schedules to users.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.