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

职业：大规模网络物理系统中的泛化学习

• 批准号: 2239566
• 负责人: Cathy Wu
• 金额: $ 55万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2028-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239566&HistoricalAwards=false
• 关键词: CAREER; Learning; Generalization; Large; Scale

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.

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，风电场的涡轮机或城市中的车辆和交通信号组成。如果能够进行智能协调，这些系统将在广泛的经济部门释放变革性的社会效益。它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，有效的协调计划一直难以实现。为了在大规模CPS中推进鲁棒协调，该项目研究了学习方法的泛化作为一个关键的解决方案概念，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放源研究和教学材料，并通过与公共部门，工业和学术伙伴合作的大规模CPS应用程序所产生的实验来加强。该项目还通过支持和积极吸引学生参与研究活动，促进研究转化为实践，并通过针对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，本科生，研究生和专业教育。NSF CAREER项目专注于大规模CPS的使能方法：理解学习方法的泛化，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能力来推广到其他场景，例如不同的问题大小或模拟和物理机器人系统之间。然而，归纳目前更多的是一门艺术，而不是一门科学;归纳成功的条件还没有得到很好的理解。与此同时，大规模的CPS往往会导致参数化的家庭的情况下，例如，交通控制必须考虑不同的天气条件，传感模式，数量和类型的代理。因此，这一系列相关的CPS场景提供了一个平台，用于仔细检查跨场景的泛化。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来推进大规模CPS的学习算法; 2）通过形式化、测量和表征CPS场景中偏差的泛化，利用算法来理解泛化;然后3）用于鲁棒协调的线束泛化，该奖项反映了NSF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进行评估，被认为值得支持的搜索.