CAREER: High-Assurance Design of Learning-Enabled Cyber-Physical Systems with Deep Contracts

下一代网络物理系统（CPS）将越来越依赖于机器学习算法来进行态势感知和决策，并有望提高人类的能力。例子包括自动驾驶汽车和机器人，计算机控制的工厂生产线和可穿戴医疗设备。然而，学习系统对训练数据非常敏感，难以确保功能安全和鲁棒性。最近的部署带来了一些意想不到的结果，比如涉及半自动驾驶汽车的事故，这引发了人们对构建安全的学习系统所需的设计原则的质疑。该项目旨在为设计和验证学习型CPS开发一种新方法的基础。它将追求一个组合框架和计算工具，可以对学习组件引入的不确定性和近似进行推理，并通过分层和模块化方法实现系统设计。拟议的研究可以对包括自动驾驶，机器人和工业自动化在内的各种应用的安全且具有成本效益的自主系统的设计和实际部署产生高度积极的影响。此外，它有可能提供一个统一的框架，用于对目前主要基于临时解决方案的许多健壮和容错设计方法进行推理。将寻求与行业伙伴的合作，以促进将研究成果转化为实践。一项教育计划将包括新的本科和研究生课程以及大学预科学生计划，以补充研究工作，旨在教育下一代工程师和研究人员的概念和多学科态度，以实现安全，技术和经济上可行的“智能”系统，并与人无缝互动。该项目开发了一个组合框架，用于推理由不可靠组件构建的CPS的概率行为。该框架依赖于组件及其环境之间接口的随机模型，称为深度契约，以及用于组合和精炼它们的严格规则。丰富的、定量的、基于逻辑的随机规范形式化和数据驱动的建模技术将被用来在不同的抽象层次上表达和传播计算上可处理的不确定性表示。该框架将垂直集成，并提供映射机制，以在设计层次结构中连接异构模型和异构分解体系结构。它将提供计算工具来有效地解决随机契约的验证和综合问题。最后，它将提供在整个系统生命周期中监视需求的机制，并在设计时和运行时提供保证。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。

Next-generation cyber-physical systems (CPS) will increasingly rely on machine learning algorithms for situational awareness and decision-making, with the promise of enhancing human capabilities. Examples range from autonomous vehicles and robots to computer-controlled factory lines and wearable medical devices. However, learning-enabled systems have shown to be very sensitive to training data and have difficulty in ensuring functional safety and robustness. The undesired outcomes of recent deployments, such as the accidents involving semi-autonomous vehicles, raise questions about the design principles needed to build learning-enabled systems that are safe. This project aims to develop the foundations of a novel methodology for the design and verification of learning-enabled CPS. It will pursue a compositional framework and computational tools that can reason about the uncertainty and approximation introduced by learning components and enable system design via a hierarchical and modular approach. The proposed research can have a highly positive influence on the design and real-world deployment of safe and cost-effective autonomous systems for a variety of applications, including autonomous driving, robotics, and industrial automation. Moreover, it has the potential to offer a unifying framework for reasoning about a number of robust and fault-tolerant design approaches that are currently based mostly on ad hoc solutions. Collaborations with industry partners will be pursued to facilitate transitioning the research findings into practice. An educational plan including new undergraduate and graduate courses and a program for pre-college students will complement the research effort, aiming to educate the next generation of engineers and researchers on the concepts and the multidisciplinary attitude needed to realize "intelligent" systems that are safe, technologically and economically feasible, and seamlessly interacting with people.The project develops a compositional framework for reasoning about the probabilistic behaviors of CPS built out of unreliable components. The framework relies on stochastic models of the interfaces between the components and their environments, termed deep contracts, together with rigorous rules for composing and refining them. Rich, quantitative, logic-based stochastic specification formalisms and data-driven modeling techniques will be leveraged to express and propagate computationally tractable representations of uncertainty at different abstraction levels. The framework will be vertically-integrated and offer mapping mechanisms to bridge heterogeneous models and heterogeneous decomposition architectures in the design hierarchy. It will provide computational tools to efficiently solve verification and synthesis problems with stochastic contracts. Finally, it will offer mechanisms to monitor requirements throughout the entire system life-cycle and provide assurance both at design time and runtime.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.