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

CPS：媒介：协作研究：分布式网络物理系统中的高效控制综合和学习

• 批准号: 1035588
• 负责人: Calin Belta
• 金额: $ 40万
• 依托单位: Trustees of Boston University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-09-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1035588&HistoricalAwards=false
• 关键词: CPS; Medium; Collaborative; Research; Efficient

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.

本研究的目的是应用语言学和音韵学领域最新发展的语法推理模型，作为分布式多智能体网络物理系统中抽象、组成、符号控制和学习的基础。该方法是将系统动态、规范和任务相互依赖性映射到有限的抽象模型，然后用可分解为本地代理规范的适当语法描述系统的期望行为。在这个框架中，代理可以通过观察其环境的动态来学习其行为，并相应地更新其规范。提出的在网络物理系统中学习的方法是基于纯粹离散级别的语法推理，这与当前的工作有很大的不同。按照这种方法，可以推断出由代理之间的事件相互依赖导致的大规模流程，而不必构建大型产品系统。为实现这一计划，提出了在建模、抽象和控制综合方面的具体技术进展。在形式语言和计算学习领域中，与形式化分解和组合异构系统相关的问题非常普遍。在根据已知具有特定性质的化合物对发现新的共沸混合物方面，也有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，整合到现有的美国国家科学基金会在特拉华大学和波士顿大学赞助的项目中。