权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

CIF: Small: Communication-Aware Decentralized Game-Theoretic Learning Algorithms for Networked Systems with Uncertainty

CIF：小型：用于不确定性网络系统的通信感知去中心化博弈论学习算法

基本信息

批准号：
2008855
负责人：
Ceyhun Eksin
金额：
$ 36.11万
依托单位：
Texas A&M Engineering Experiment Station
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2024-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008855&HistoricalAwards=false
关键词：
CIF Small Communication Aware Decentralized

项目摘要

At the core of the opportunities for many emerging technological networked systems, such as Internet-of-things, smart grid, transportation systems, lies decentralization which means the deployment of interlinked agents, e.g., sensors, appliances, and devices, that operate in harmony. An implicit assumption in the decentralized operations in these systems is that these agents agree or are able to resolve their differences in what is their objective. In large scale networked systems, barring unreasonable accuracy of environmental information and unjustifiable levels of coordination, agents cannot be sure of what other agents are optimizing. This project will enable practical and scalable decentralized solutions to signal processing and communication problems in technological networked systems where wireless network technologies provide the backbone for information exchange. Furthermore, this project will involve mentoring of PhD students, providing research experience to undergraduate students, and the development of a graduate-level course on learning and networks at Texas A&M University. The premise of this project is that when agents disagree on their objectives in a networked multi-agent system due to environmental uncertainty, they are playing a game against uncertainty, and the optimality criterion is then defined by game-theoretic notions. Upon adopting, game-theoretic equilibria as the optimality criterion, the project is divided into three major thrusts. The first thrust will develop decentralized algorithms based on game-theoretic notions, and will characterize finite-time outcomes when information aggregation or consensus in large-scale networked systems may not be possible. The second thrust aims to understand fundamental trade-offs in communication vs. optimality, and to develop decentralized algorithms that account for costly and faulty communication. The third thrust will seek to characterize the likelihood of converging to undesirable equilibria, and will use influence maximization methods to avoid bad outcomes. Overall, the project draws upon methods common in decentralized optimization, e.g., consensus, and in signal processing, e.g., communication censoring, and combines them with game-theoretic, best-response type, learning algorithms to design scalable and communication-aware decentralized algorithms.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.

许多新兴技术网络系统（如物联网、智能电网、交通系统）的机会核心在于分散化，这意味着部署相互关联的代理，例如，传感器，电器和设备，和谐地运作。在这些系统中，分散操作的一个隐含假设是，这些代理人同意或能够解决他们在目标上的分歧。在大规模的网络系统中，除非环境信息的不合理的准确性和不合理的协调水平，代理不能确定其他代理优化。该项目将为无线网络技术为信息交换提供骨干的技术网络系统中的信号处理和通信问题提供实用和可扩展的分散解决方案。此外，该项目将涉及指导博士生，为本科生提供研究经验，并在得克萨斯州农工大学开发研究生水平的学习和网络课程。这个项目的前提是，当代理不同意他们的目标在网络多智能体系统中，由于环境的不确定性，他们正在玩一个游戏对不确定性，最优性标准，然后定义的博弈论的概念。在采用博弈论均衡作为最优性标准后，该项目分为三个主要目标。第一个推力将开发基于博弈论概念的分散算法，并将描述在大规模网络系统中不可能实现信息聚合或共识时的有限时间结果。第二个目标是理解通信与最优性的基本权衡，并开发分散算法，以解决昂贵和错误的通信。第三个重点将试图描述收敛到不受欢迎的均衡的可能性，并将使用影响最大化方法来避免坏结果。总的来说，该项目借鉴了分散优化中常见的方法，例如，共识，以及在信号处理中，例如，该奖项反映了NSF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。