CPS: Small: Self-Improving Cyber-Physical Systems

传统的网络物理系统在严格约束和受控的环境中运行，对意想不到的变化和不确定性的暴露有限。例如，在化工厂的制造装配线和计算机物理控制系统上操作的机器人。基于模型的设计范式，其中设计、实施和验证都由系统的数学模型指导，已被证明在建立这种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，以收集大量数据为基础，然后进行学习和推理的数据驱动方法取得了成功，这使现代网络物理系统具有更强的适应性。例如自动驾驶汽车和仓储机器人。嵌入在这些系统中的学习算法允许他们在执行时学习，并根据需要修改自己的行为。这样的系统能够进行广泛的非预编程行为。但这带来了一个新的挑战。基于模型的设计范例不再足够。由于系统的演变不再是静态的，而是由数据驱动并由系统的动态经验指导，因此很难建立对安全性、稳健性或性能改进的正式保证。该项目的目标是建立和评估一个形式化的框架，该框架结合了数据驱动和基于模型的自适应网络物理系统的开发，为设计安全的、数据驱动的和基于模型的自适应网络物理系统(CPS)开发了一种新的方法。基于模型的技术最初被用来引导系统并为系统找到最自由的安全包络。设计健壮性和对定量解释的丰富的时序逻辑的运行时监测相结合被用来将系统保持在安全包络内。数据驱动技术用于主动探索、调整和改进系统性能，同时将系统行为约束在安全范围内。通过严格学习新数据的时态逻辑属性来总结新数据；反过来，学习到的逻辑规范用于指导主动探索。该项目的主要进展包括：(A)数据作为模型范例，其中来自过去运行的数据在CPS的设计中被视为一级对象；(B)从仅为正的实例中的紧密学习，其中先前的运行(都是安全运行，因此仅提供正实例)被总结为丰富的时间逻辑公式；(C)用于稳健性度量引导的监测和包络内的系统性能优化的安全包络合成；(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；以及(E)主动探索，其中自适应CPS仅为了提高其知识和性能而主动执行一些安全操作。

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.