CPS:Medium: Safe Learning-Enabled Cyberphysical Systems

尽管机器学习取得了巨大的进步，但设计能够从环境中学习并与环境交互以实现复杂规范的真正自主的网络物理系统(CP)的目标仍然难以实现。这项研究试图通过开发一种新的可验证的安全学习CP来解决这一明显的悖论(机器学习的进步/相对较低的自主性水平)，能够适应以前未见过的动态场景，其中数据是生成的，并且必须在系统运行时做出决策。它通过在动态系统、机器学习和生存理论的融合中创建一个新的框架来应对数据革命和高动态系统带来的CPS挑战，该框架专门为在不确定的、数据泛滥的场景中学习和安全操作而定制。研究围绕三个紧密交互的主题--R1：稀疏潜在特征和流形的联合学习，R2：动态场景中的实时推理；R3：可验证决策算法--利用CPS的动态诱导的底层稀疏结构，以获得挑战现有技术的问题的快速解决方案。拟议框架的一个关键特征是它能够利用推力之间的紧密耦合来获得可处理的问题。例如，利用学习过程中揭示的简约结构的低复杂性实时推理方法，以及通过使用这些结构将问题重塑为混合系统分析形式来验证闭环系统特性的控制策略。在大学预科阶段，将为城市高中生制定暑期STEM计划。参与者将探索CPS的概念，并完成一个赋予自动驾驶车辆有限学习能力的期末项目。在本科生层面，这项提案中提出的想法将被注入到课程中。教育计划的标志将是通过学习支持的CPS这一中心隐喻进行整合。在研究生一级，将继续以合作绩效指标为代表的各学科的综合主题，包括教授一门包括经验作业的课程。此外，这个项目将为研究生提供机会和支持，让他们成为跨学科团队的成员。扩大参与的战略是双管齐下的：一方面，除了针对城市青年的夏季STEM计划外，它还将利用NUPRIME(新大学多元文化工程计划)。另一方面，它将利用共同个人投资机构在各自社会中的领导作用，在会议上组织针对高中生和代表不足的群体的活动。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

In spite of tremendous advances in machine learning, the goal of designing truly autonomous cyber-physical systems (CPS), capable of learning from and interacting with the environment to achieve complex specifications remains elusive. This research seeks to address this apparent paradox (advances in machine learning/relatively low levels of autonomy) by developing a new class of verifiable safe learning- enabled CPS, capable of adapting to previously unseen dynamic scenarios where the data is generated, and decisions must be made, as the system operates. It addresses the CPS challenges posed by the data revolution and highly dynamic systems by creating a new framework at the confluence of dynamical systems, machine learning and viability theory, specifically tailored to learning and safely acting in uncertain, data deluged scenarios.The research is organized around three tightly interacting thrusts -- R1: Joint learning of sparse latent features and manifolds, R2: Real-time inference in dynamic scenarios; and R3: Verifiable decision-making algorithms -- that exploit the underlying sparse structure induced by the dynamics of the CPS to obtain fast solutions to problems that challenge current techniques. A key feature of the proposed framework is its ability to take advantage of the tight coupling between thrusts to obtain tractable problems. Examples are low-complexity real-time inference methods that leverage parsimonious structures unveiled during learning, and control strategies that verify closed-loop properties by using these structures to recast the problem into a hybrid system analysis form.Education is proactively integrated into this project. At the pre-college level, summer STEM programs for urban high school students will be developed. Participants will explore CPS concepts and complete a final project endowing autonomous vehicles with limited learning capabilities. At the undergraduate level, ideas put forth in this proposal will be infused through the curriculum. The hallmark of the educational program will be its integration through the central metaphor of learning-enabled CPS. At the graduate level, this integrative theme across the disciplines represented by the Co-PIs will be continued, including teaching of a course that includes experiential assignments. In addition, this project will provide opportunities and support for graduate students to engage as members of an interdisciplinary team. The strategy to broaden participation is two pronged: on one hand, it will leverage, in addition to the summer STEM programs for urban youth, NUPRIME (NEU's Program in Multicultural Engineering). On the other hand, it will take advantage of the co-PIs leadership roles in their respective societies to organize events targeting high schoolers and underrepresented groups at conferences.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.