CAREER: Identifying and Exploiting Multi-Agent Symmetries

职业：识别和利用多智能体对称性

• 批准号: 2237963
• 负责人: Qi Zhang
• 金额: $ 53.53万
• 依托单位: University of South Carolina at Columbia
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-01 至 2028-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2237963&HistoricalAwards=false
• 关键词: CAREER; Identifying; Exploiting; Multi; Agent

It is widely believed by scientists that our universe follows certain symmetry patterns and principles, which lead to profound implications such as conservation laws. Artificial intelligence (AI) can and has already benefited tremendously from exploiting these symmetries. This project seeks to identify and exploit symmetries that are prevalent in cooperative AI tasks, where a group of multiple autonomous sequential decision makers, or agents, plan and learn to maximize their combined benefit. As an example, consider the application of adaptive traffic signal control, where each intersection can be modeled as an agent controlling its traffic signal in a way that adapts to real-time traffic conditions to reduce congestion. There exist certain symmetries when the topology of the road network is regular, e.g., as a 4-connected grid, and the road condition is uniform. When done properly, such multi-agent symmetries can be identified and exploited to greatly improve the efficiency and effectiveness of the current solutions to cooperative AI. This project also integrates the proposed research into an array of education initiatives, playing key roles in the curriculum development and undergraduate research experiences at the PI's university, as well as outreach activities that bridge academia with industry practitioners and community stakeholders.This research will establish a unified framework and develop a set of interdependent methods that formulate, identify, and exploit multi-agent symmetries for cooperative AI tasks. The research first adopts a mathematically rigorous language to formulate the notion of multi-agent symmetry into the framework of symmetric Markov game, revealing its core property which can be exploited by planning and learning methods. Then, the research plan concretizes how to exploit several most common types of multi-agent symmetries, including permutation symmetries, Euclidean symmetries, and hierarchies of multi-agent symmetries of mixed types. Next, the research plan discusses issues that are critical for practice, including identifying and exploiting approximate multi-agent symmetries and dealing with partial observability. Finally, the research features several real-world applications, including adaptive traffic signal control, automated circuit design, and material design, to evaluate and showcase the proposed methodology. This project is jointly funded by Robust Intelligence and the Established Program to Stimulate Competitive Research (EPSCoR).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.

科学家普遍认为，我们的宇宙遵循某些对称模式和原则，从而导致了诸如保护法等深远的含义。人工智能（AI）可以并且已经从利用这些对称性中受益匪浅。该项目旨在识别和利用在合作AI任务中普遍存在的对称性，其中一组多个自主的顺序决策者或代理人计划并学会了最大化其综合利益。例如，考虑适应性流量信号控制的应用，可以将每个交叉路口建模为控制其交通信号的代理，以适应实时交通状况以减少拥塞的方式。当道路网络的拓扑是常规的，例如，作为4个连接的网格并且道路状况均匀时，存在某些对称性。正确完成后，可以确定和利用此类多代理对称性，以极大地提高当前合作AI解决方案的效率和有效性。该项目还将拟议的研究集成到一系列教育计划中，在Pi University的课程开发中扮演关键角色和本科研究经验，以及与行业从业人员和社区利益相关者桥接学术界的外展活动。这项研究将建立一项研究统一的框架并开发一组相互依存的方法，这些方法为合作AI任务制定，识别和利用多代理对称性。该研究首先采用了一种数学上严格的语言，以将多代理对称性的概念提出在对称马尔可夫游戏的框架中，从而揭示了其核心属性，可以通过计划和学习方法来利用。然后，研究计划结合了如何利用多种类型的多代理对称性类型的类型，包括置换对称性，欧几里得对称性和混合类型多代理对称性的层次结构。接下来，研究计划将讨论对实践至关重要的问题，包括识别和利用大概的多代理对称性以及处理部分可观察性。最后，该研究具有多种现实世界应用，包括自适应交通信号控制，自动化电路设计和材料设计，以评估和展示拟议的方法。 该项目由强大的情报和刺激竞争性研究的既定计划共同资助（EPSCOR）。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，认为值得通过评估来获得支持。