CAREER: Learning for Generalization in Large-Scale Cyber-Physical Systems

网络物理系统（CPS）的采用正在加速增长，从而产生了大规模的CPS——例如，由仓库中的大量机器人、风力发电场的涡轮机或城市中的车辆和交通信号组成。如果智能协调，这些系统将在广泛的经济部门释放变革性的社会效益。他们还承诺通过更有效地利用供应链、能源系统和城市系统等资源，为应对本世纪最紧迫的挑战——气候变化做出贡献。不幸的是，由于这些系统所遇到的情况的规模和多样性，有效的协调方案一直难以捉摸。为了推进大规模CPS中的稳健协调，该项目研究了学习支持方法作为关键解决方案概念的泛化，考虑到它们在不同场景中转换协调方案的潜力。该项目的影响将通过传播开源研究和教学材料，以及通过与公共部门、工业和学术伙伴合作开展大规模CPS应用的实验来增强。该项目还通过支持和积极参与学生的研究活动，促进研究成果向实践的转化，以及通过针对来自代表性不足和服务不足社区的中学生的外展工作，促进K-12、本科、研究生和专业教育。NSF CAREER项目关注大规模CPS的使能方法：理解使能学习方法的泛化，并进一步应用它来降低系统设计和分析的复杂性。最近的证据表明，使用机器学习训练的控制器有时具有非凡的推广到其他场景的能力，例如不同规模的问题或模拟和物理机器人系统之间的能力。然而，概括目前更像是一门艺术，而不是一门科学；泛化成功的条件还没有得到很好的理解。同时，大尺度CPS往往会诱发参数化的情景族；例如，交通管制必须考虑到不同的天气条件、传感模式以及代理的数量和类型。因此，这一系列相关的CPS场景为仔细检查跨场景的泛化提供了一个平台。该项目将：1)通过为多智能体系统设计基于协调感知模型的强化学习方法，推进大规模CPS的学习算法；2)利用算法通过形式化、测量和表征CPS场景中偏差的泛化来理解泛化；然后3)通过有效地解决提供高性能和保证所需的大量场景，利用泛化进行稳健协调。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。

The adoption of cyber-physical systems (CPS) is growing at an accelerated rate, giving rise to large-scale CPS––for example, comprised of large numbers of robots in a warehouse, turbines on a wind farm, or vehicles and traffic signals in a city. If intelligently coordinated, these systems will unlock transformative societal benefits across broad economic sectors. They also promise to contribute to the most pressing challenge of the century––climate change––by substantially utilizing resources more effectively, such as from supply chains, energy systems, and urban systems. Unfortunately, effective coordination schemes have been elusive due to the sheer scale and diversity of scenarios that these systems encounter. To advance robust coordination in large-scale CPS, this project investigates the generalization of learning-enabled methods as a key solution concept, in light of their potential to translate coordination schemes across disparate scenarios. The project's impact will be enhanced through the dissemination of open-source research and teaching material, and via experiments derived from large-scale CPS applications in collaboration with public sector, industry, and academic partners. The project also boosts K-12, undergraduate, graduate, and professional education, by supporting and actively engaging students in research activities, promoting the translation of research to practice, and through outreach efforts targeting middle school students from underrepresented and underserved communities.This NSF CAREER project focuses on an enabling methodology for large-scale CPS: understanding generalization of learning-enabled methods, and further applying it to reduce the complexity of system design and analysis. Recent evidence shows that controllers trained using machine learning sometimes have the remarkable ability to generalize to other scenarios, such as to different problem sizes or between simulated and physical robotic systems. However, generalization is currently more of an art than a science; the conditions under which generalization is successful are not well understood. At the same time, large-scale CPS often induce parameterized families of scenarios; for example, traffic control must consider different weather conditions, sensing modalities, and numbers and types of agents. This family of related CPS scenarios thus provides a platform for carefully examining generalization across scenarios. The project will: 1) advance learning algorithms for large-scale CPS by designing coordination-aware model-based reinforcement learning methods for multi-agent systems; 2) leverage the algorithms to understand generalization by formalizing, measuring, and characterizing generalization with respect to deviations in CPS scenarios; and then 3) harness generalization for robust coordination, by efficiently solving large families of scenarios necessary to provide high performance and assurances.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.