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

下一代网络物理系统（CPS）将越来越依赖机器学习算法， 情况感知和决策，并有望提高人的能力。例子从自动驾驶汽车和机器人到计算机控制的工厂生产线和可穿戴医疗设备。然而，支持学习的系统对训练数据非常敏感，并且难以确保功能安全性和鲁棒性。最近部署的不希望的结果，例如涉及半自动驾驶汽车的事故，提出了关于构建安全的学习系统所需的设计原则的问题。该项目旨在为学习型CPS的设计和验证开发一种新方法的基础。它将追求一个组成框架和计算工具，可以对学习组件引入的不确定性和近似性进行推理，并通过分层和模块化方法实现系统设计。拟议的研究可以对安全和具有成本效益的自主系统的设计和实际部署产生非常积极的影响，这些系统适用于各种应用，包括自动驾驶，机器人和工业自动化。此外，它有可能提供一个统一的框架，推理一些强大的和容错的设计方法，目前主要是基于特设的解决方案。将与行业合作伙伴进行合作，以促进将研究成果转化为实践。包括新的本科生和研究生课程以及大学预科生课程在内的教育计划将补充研究工作，旨在教育下一代工程师和研究人员了解实现安全，技术和经济可行的“智能”系统所需的概念和多学科态度，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，以及用于组合和细化它们的严格规则。丰富的，定量的，基于逻辑的随机规范形式主义和数据驱动的建模技术将被利用来表达和传播计算上易于处理的表示在不同的抽象层次的不确定性。该框架将是垂直集成的，并提供映射机制，以桥接设计层次中的异构模型和异构分解架构。它将提供计算工具，有效地解决验证和随机合同的综合问题。最后，它将提供在整个系统生命周期中监控需求的机制，并在设计时和运行时提供保证。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.