CPS: Medium: Collaborative Research: Efficient Control Synthesis and Learning in Distributed Cyber-Physical Systems

本研究的目的是应用语法推理模型最近开发的语言学和语音学领域，作为基础的抽象，组成，符号控制，并在分布式多智能体网络物理系统的学习。该方法是映射的系统动力学，规范，任务的相互依赖性有限的抽象模型，然后描述所需的系统行为在一个适当的语法，可以分解成本地代理规范。在这个框架中，智能体可以通过观察其环境的动态来学习其环境的行为，并相应地更新其规范。所提出的方法来学习网络物理系统，这是基于语法推理在一个纯粹的离散水平，是一个显着偏离目前的作品。遵循这种方法，可以推理由代理之间的事件相互依赖性导致的大规模过程，而不必构建大型产品系统。为了实现这一计划，提出了具体的建模，抽象和控制综合的技术进展。问题有关的形式化分解和组成异构系统是普遍存在的形式语言和计算学习领域。在基于已知具有特定性质的化合物对的文献记载发现新的共沸混合物的领域中，也存在具有商业意义的应用。拟议的传播和推广活动包括初中和高中学生和教师的参与，在特拉华州大学和波士顿大学现有的NSF赞助的方案相结合。

The objective of this research is to apply grammatical inference models recently developed in the field of linguistics and phonology, as a basis for abstraction, composition, symbolic control, and learning in distributed multi-agent cyber-physical systems. The approach is to map the system dynamics, specifications, and task interdependences to finite abstract models, and then describe the desired behavior of the system in an appropriate grammar that can be decomposed into local agent specifications. In this framework, the agents can learn the behavior of their environment by observing its dynamics, and update their specifications accordingly. The proposed approach to learning in cyber-physical systems, which is based on grammatical inference at a purely discrete level, is a significant departure from current works. Following this approach, one can reason about large-scale processes resulting from event interdependencies between agents, without having to construct large product systems. To realize this plan, specific technical advances on modeling, abstraction, and control synthesis are proposed.Questions related to formally factoring and composing heterogeneous systems are pervasive in the fields of formal languages and computational learning. There are also applications of commercial significance in the area of discovering new azeotropic mixtures based on documented pairs of compounds that are known to have the particular property. Proposed dissemination and outreach activities include the involvement of middle and high school students and teachers, integrated in existing NSF-sponsored programs at the University of Delaware and Boston University.