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

• 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

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（例如物联网、智能电网、交通系统）机会的核心在于去中心化，这意味着部署协调运行的相互关联的代理，例如传感器、电器和设备。这些系统中去中心化操作的一个隐含假设是，这些代理同意或能够解决其目标上的分歧。在大规模网络系统中，除非环境信息的不合理准确性和不合理的协调水平，否则智能体无法确定其他智能体正在优化什么。该项目将为技术网络系统中的信号处理和通信问题提供实用且可扩展的分散解决方案，其中无线网络技术为信息交换提供了支柱。此外，该项目还将涉及指导博士生、为本科生提供研究经验以及在德克萨斯农工大学开发有关学习和网络的研究生课程。 该项目的前提是，当代理由于环境不确定性而在网络多代理系统中对其目标产生分歧时，他们正在与不确定性进行博弈，然后通过博弈论概念定义最优性标准。采用博弈论均衡作为最优标准后，该项目分为三个主要方向。第一个推动力将开发基于博弈论概念的去中心化算法，并将在大规模网络系统中的信息聚合或共识不可能时表征有限时间结果。第二个重点旨在了解通信与最优性之间的基本权衡，并开发去中心化算法来解决昂贵且错误的通信问题。第三个重点将寻求描述收敛到不良均衡的可能性，并将使用影响力最大化方法来避免不良结果。总体而言，该项目借鉴了去中心化优化（例如共识）和信号处理（例如通信审查）中常见的方法，并将它们与博弈论、最佳响应类型、学习算法相结合，以设计可扩展和通信感知的去中心化算法该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Maximizing Social Welfare and Agreement Via Information Design in Linear-Quadratic-Gaussian Games

通过线性二次高斯博弈中的信息设计最大化社会福利和共识

• DOI: 10.1109/tac.2023.3270241
• 发表时间: 2023-04
• 期刊: IEEE Transactions on Automatic Control
• 影响因子: 6.8
• 作者: Sezer, Furkan;Khazaei, Hossein;Eksin, Ceyhun
• 通讯作者: Eksin, Ceyhun

Convergence Bounds of Decentralized Fictitious Play Around a Single Nash Equilibrium in Near-Potential Games

近势博弈中围绕单一纳什均衡的去中心化虚拟博弈的收敛界

• DOI: 10.1109/cdc51059.2022.9993189
• 发表时间: 2022-12-06
• 期刊: 2022 IEEE 61st Conference on Decision and Control (CDC)
• 作者: Sarper Aydın;Sina Arefizadeh;Ceyhun Eksin
• 通讯作者: Ceyhun Eksin

Networked Policy Gradient Play in Markov Potential Games

马尔可夫势博弈中的网络化策略梯度博弈

• DOI: 10.1109/icassp49357.2023.10094870
• 发表时间: 2023-06-04
• 期刊: ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
• 作者: Sarper Aydın;Ceyhun Eksin
• 通讯作者: Ceyhun Eksin

Decentralized inertial best-response with voluntary and limited communication in random communication networks

随机通信网络中自愿和有限通信的去中心化惯性最佳响应

• DOI: 10.1016/j.automatica.2022.110566
• 发表时间: 2022-11
• 期刊: Automatica
• 影响因子: 6.4
• 作者: Aydın, Sarper;Eksin, Ceyhun
• 通讯作者: Eksin, Ceyhun

An Incentive Compatible Iterative Mechanism for Coupling Electricity Markets

耦合电力市场的激励兼容迭代机制

• DOI: 10.1109/tpwrs.2021.3100782
• 发表时间: 2021-07
• 期刊: IEEE Transactions on Power Systems
• 影响因子: 6.6
• 作者: Garcia, Alfredo;Khatami, Roohallah;Eksin, Ceyhun;Sezer, Furkan
• 通讯作者: Sezer, Furkan