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，其目的是提高舒适度，提高能源效率并降低运营成本。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，以及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HAS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偏好和约束是不实际的。这个项目的总体目标是设计一个HAS，它可以在用户和系统之间的有限数量的交互中为用户找到可接受的解决方案。为此，本项目研究的技术，引起了少数的偏好和约束，这是在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，他们可以提供其他首选项，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性贡献，特别是在考虑与人类用户交互的算法技术方面。为了在有限的用户交互次数内找到可接受的解决方案，该项目将（1）对与人类用户交互的干扰成本进行建模;（2）通过将干扰成本纳入优化过程来改进偏好获取技术;（3）通过将干扰成本纳入优化过程来改进偏好获取技术;（3）使用矩阵补全算法的组合并利用它们的多样性来近似未引出的偏好及其相应的不确定性程度;（4）设计鲁棒的和可扩展的算法，该算法基于不确定的偏好来找到调度;以及（5）在向用户介绍和解释这些时间表时，提出考虑到麻烦成本的视觉和语言界面。该奖项反映了NSF的法定使命，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价，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.