CPS: Medium: Resilient-by-Cognition Cyber-Physical Systems

自动驾驶系统，尤其是自动驾驶汽车，有望成为近年来最具颠覆性的新兴技术之一。然而，这些系统的安全性和弹性，如果不主动解决，将构成重大威胁，可能会损害我们与这些技术的关系，并可能导致社会拒绝永久采用它们。该项目旨在通过为自主系统配备额外的智能层，使它们能够根据认知进行恢复，从而解决这些问题。该研究通过整合博弈论、形式化方法和控制的概念，解决了自主网络物理系统（CPS）的弹性问题。我们提出的方法包括：(i)关于认知CPS的正式推理的原则性框架；也就是说，给定一组以形式语言捕获的策略（例如，时间逻辑），所提出的框架建立在进化博弈论的思想基础上，以理解在敌对环境中操作时哪种策略最适合。设计正确的反馈控制器综合，执行选定的策略，同时满足底层CPS的微动力学施加的物理约束。（iii）数据驱动的策略挖掘方法，解决了从人类演示中设计策略库的基本问题。我们将在包括自动驾驶汽车和自动无人机群在内的关键应用中说明我们的方法。我们的教育计划不仅招收研究生，而且招收高中生和本科生。它还通过提供我们算法的开源实现，使它们既可供行业使用，也可供独立开发人员使用，从而接触到工程师和普通公众。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。

Autonomous systems in general and self-driving cars in particular, hold the promise to be one of the most disruptive technologies emerging in recent years. However, the safety and resilience of these systems, if not proactively addressed, will pose a significant threat potentially impairing our relationship with these technologies and may lead to a societal rejection of adopting them permanently. This project seeks to address such concerns by equipping autonomous systems with an additional layer of intelligence allowing them to be resilient-by-cognition.The research addresses resilience for autonomous cyber-physical systems (CPS) by integrating concepts from game theory, formal methods, and controls. Our proposed approach includes: (i) a principled framework for formally reasoning about cognitive CPS; that is, given a set of strategies captured in a formal language (e.g., temporal logic), the proposed framework builds on ideas from evolutionary game theory to understand which strategies lead to the best fit when operating in adversarial environments (ii) On-the-fly, correct-by-design feedback controller synthesis that executes the chosen strategy while satisfying physical constraints imposed by the micro-dynamics of the underlying CPS (iii) a data-driven strategy-mining approach that addresses the fundamental problem of designing the library of strategies from human demonstrations. We will illustrate our approach over key applications including self-driving cars and autonomous drone swarms. Our educational plan engages not only graduate students but also high school and undergraduate students. It also reaches out to engineers and the lay public, by providing open source implementations of our algorithms making them available both to industry and independent developers.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.