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.

科学家们普遍认为，我们的宇宙遵循某些对称模式和原则，这导致了守恒定律等深刻的含义。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合作人工智能任务中普遍存在的对称性，其中一组多个自主顺序决策者或代理，计划和学习以最大限度地提高其综合效益。作为一个例子，考虑自适应交通信号控制的应用，其中每个十字路口可以被建模为智能体，以适应实时交通状况的方式控制其交通信号，以减少拥堵。当道路网络的拓扑是规则的时，存在某些对称性，例如，作为一个4连接的网格，道路状况是均匀的。如果处理得当，可以识别和利用这种多智能体对称性，从而大大提高当前合作AI解决方案的效率和有效性。该项目还将拟议的研究纳入一系列教育举措，在课程开发和PI大学的本科生研究经验以及连接学术界与行业从业者和社区利益相关者的外展活动中发挥关键作用。该研究将建立一个统一的框架，并开发一套相互依存的方法，并利用多智能体对称性来完成合作人工智能任务。该研究首先采用数学上严格的语言来制定多智能体对称的概念到对称马尔可夫博弈的框架，揭示其核心属性，可以利用规划和学习方法。然后，研究计划具体化如何利用几种最常见的类型的多智能体对称性，包括置换对称性，欧几里得对称性，和混合类型的多智能体对称性的层次结构。接下来，研究计划讨论了对实践至关重要的问题，包括识别和利用近似的多智能体对称性以及处理部分可观测性。最后，研究功能的几个现实世界的应用，包括自适应交通信号控制，自动化电路设计和材料设计，以评估和展示所提出的方法。 该项目由Robust Intelligence和刺激竞争研究的既定计划（EPSCoR）共同资助。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。