CPS: Small: Self-Improving Cyber-Physical Systems

传统的网络物理系统在严格约束和控制的环境中运行，对意外变化和不确定性的暴露有限。例子包括在制造装配线上操作的机器人和化工厂的网络物理控制系统。基于模型的设计范式，其中设计、实现和验证都由系统的数学模型指导，已被证明在构建此类非自适应网络物理系统并证明其安全性方面非常成功。最近成功的数据驱动方法基于大量数据的收集，然后是学习和推理，这使得现代网络物理系统具有更强的适应性。例子包括自动驾驶汽车和仓库机器人。这些系统中嵌入的学习算法允许它们在执行和根据需要修改自己的行为时进行学习。这样的系统能够有广泛的非预编程行为。但这也带来了新的挑战。基于模型的设计范式已经不够了。由于系统演化不再是静态的，因此很难建立对安全性、健壮性或性能改进的正式保证；相反，它是由数据驱动的，并由系统的动态经验指导。该项目的目标是建立和评估一个正式框架，该框架结合了自适应网络物理系统的数据驱动和基于模型的开发。该项目开发了一种设计安全、数据驱动和基于模型的自适应网络物理系统（CPS）的新方法。基于模型的技术最初用于引导系统并为系统找到最自由的安全包络。将设计稳健性和定量解释的丰富时态逻辑的运行时监控相结合，使系统保持在安全范围内。数据驱动技术用于主动探索、调整和改进系统性能，同时将系统行为限制在安全范围内。新数据通过对时间逻辑属性的严格学习进行总结；学习到的逻辑规范反过来又用于指导主动探索。本项目的关键进展包括(a)数据作为模型范式，其中过去运行的数据被视为CPS设计中的一级对象；(b)从仅限正的示例中紧密学习，其中以前的运行（都是安全运行，因此只提供正示例）被总结为丰富的时间逻辑公式；(c)安全包络综合，用于鲁棒性度量指导下的监控和优化包络内的系统性能。(d)基于模型的控制的数据驱动扩展，其中数据用于扩展经典的模型预测控制；(e)主动探索，其中自适应CPS主动执行一些安全操作，仅为了提高其知识和性能。

Traditional cyber-physical systems operate in heavily constrained and controlled environments with limited exposure to unexpected changes and uncertainties. Examples include robots operating on manufacturing assembling-lines and cyber-physical control systems of chemical plants. The model-based design paradigm, where design, implementation and verification are all guided by mathematical models of the system, has proven to be very successful in building such non-adaptive cyberphysical systems and proving their safety. The recent success of data-driven approaches based on the collection of a large amount of data followed by learning and inference has enabled modern cyberphysical systems to be more adaptive. Examples include self-driving cars and warehouse robots. Learning algorithms embedded in these systems allow them to learn as they execute and modify their behavior as needed. Such systems are capable of a wide range of non-preprogrammed behaviors. But this creates a new challenge. Model-based design paradigm is no longer sufficient. Formal guarantees on safety, robustness or improvement in performance are difficult to establish since the system evolution is no longer static; instead, it is data-driven and guided by the system's dynamic experience. The goal of this project is to build and evaluate a formal framework that combines data-driven and model-based development of adaptive cyber-physical systems.This project develops a new approach for designing safe, data-driven, and model-based adaptive cyber-physical systems (CPS). Model-based techniques are used initially to bootstrap the system and find the most liberal safety envelope for the system.  A combination of design robustness and runtime monitoring of quantitatively-interpreted rich temporal logic is used to keep the system within the safety envelope. Data-driven techniques are used to actively explore, adapt, and improve system performance while constraining the system behavior to lie within the safety envelope.  New data is summarized by tight learning of temporal logic properties from it; the learned logical specification is, in turn, used to guide active exploration. The key advances in this project include (a) data as model paradigm, where data from past runs is treated as a first-class object in the design of CPS, (b) tight learning from positive-only examples, where previous runs (that are all safe runs, and hence provide only positive examples) are summarized into rich temporal logic formulae, (c) safety envelope synthesis for robustness-metric guided monitoring and optimization of system performance within the envelope, (d) data-driven extensions of model-based control, where data is used to extend classical model-predictive control, and (e) active exploration, where an adaptive CPS actively executes some safe manoeuvres solely for the purpose of improving its knowledge and performance.