Collaborative Research: CPS: Medium: ASTrA: Automated Synthesis for Trustworthy Autonomous Utility Services

具有社会意义的大型系统，如发电系统，越来越能够利用新技术来减轻其对环境的影响，例如，通过从可再生资源中收集能源。NSF CPS项目旨在研究方法和计算工具，为能源分配和分配设计一种新的以用户为中心的范例，更广泛地说，为值得信赖的公用事业服务。在这个范例中，分布式网络系统将帮助电力的最终用户调度和分配他们的消费。此外，它们将使地方和国家公用事业管理人员能够优化绿色能源的使用，同时减轻间歇性的影响，促进公平、公平和可负担性。该项目采用一种易于处理的方法来解决建模和设计这些大规模、混合自治、多代理cps的挑战。智力上的优点包括用于设计分布式决策策略和系统架构的新的可伸缩方法、算法和工具，这些方法和工具可以帮助最终用户满足他们的目标，同时保证遵循设计的公平性、可靠性和物理约束。更广泛的影响包括实现分布式cps的自动化设计，以协调其在许多应用中的决策，从机器人群到智能制造和智能城市。研究成果也将用于K-12和本科STEM推广工作。该框架被称为可信赖自治公用事业服务的自动合成（ASTrA），通过三管齐下的方法解决了设计挑战。它使用人口博弈来模拟分布式决策基础设施（DMI）对大量战略代理群体的影响。dmi将通过我们寻求设计的专用网络混合硬件架构和算法来实现。ASTrA进一步引入了一种系统化的分层方法，用于根据需求的表达表示自动化dmi的设计、验证和确认。最后，它提供了一套尖端的计算工具，通过对离散模型（例如，用于描述复杂任务或嵌入式软件组件）和用于描述物理过程的连续模型之间的交互进行有效推理，来促进我们的方法。评估计划包括在一个为零净能源应用设计的真实试验台上进行实验。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。

Large-scale systems with societal relevance, such as power generation systems, are increasingly able to leverage new technologies to mitigate their environmental impact, e.g., by harvesting energy from renewable sources. This NSF CPS project aims to investigate methods and computational tools to design a new user-centric paradigm for energy apportionment and distribution and, more broadly, for trustworthy utility services. In this paradigm, distributed networked systems will assist the end users of electricity in scheduling and apportioning their consumption. Further, they will enable local and national utility managers to optimize the use of green energy sources while mitigating the effects of intermittence, promote fairness, equity, and affordability. This project pursues a tractable approach to address the challenges of modeling and designing these large-scale, mixed-autonomy, multi-agent CPSs. The intellectual merits include new scalable methods, algorithms, and tools for the design of distributed decision-making strategies and system architectures that can assist the end users in meeting their goals while guaranteeing compliance with the fairness, reliability, and physical constraints of the design. The broader impacts include enabling the automated design of distributed CPSs that coordinate their decision-making in many applications, from robotic swarms to smart manufacturing and smart cities. The research outcomes will also be used in K-12 and undergraduate STEM outreach efforts. The proposed framework, termed Automated Synthesis for Trustworthy Autonomous Utility Services (ASTrA), addresses the design challenges via a three-pronged approach. It uses population games to model the effect of distributed decision-making infrastructures (DMI) on large populations of strategic agents. DMIs will be realized via dedicated networked hybrid hardware architectures and algorithms we seek to design. ASTrA further introduces a systematic, layered methodology to automate the design, verification, and validation of DMIs from expressive representations of the requirements. Finally, it offers a set of cutting-edge computational tools to facilitate our methodology by enabling efficient reasoning about the interaction between discrete models, e.g., used to describe complex missions or embedded software components, and continuous models used to describe physical processes. The evaluation plan involves experimentation on a real testbed designed for zero-net-energy applications.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.