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理，这导致了守恒定律等深远的影响。人工智能 (AI) 可以而且已经从利用这些对称性中获益匪浅。该项目旨在识别和利用协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进行计划和学习，以最大化其综合效益。例如，考虑自适应交通信号控制的应用，其中每个交叉口都可以建模为一个代理，以适应实时交通状况的方式控制其交通信号，以减少拥堵。当路网拓扑规则（例如四连通网格）且路况均匀时，存在一定的对称性。如果处理得当，这种多智能体对称性可以被识别和利用，从而大大提高当前协作人工智能解决方案的效率和有效性。该项目还将拟议的研究整合到一系列教育举措中，在 PI 大学的课程开发和本科生研究经验以及在学术界与行业从业者和社区利益相关者之间架起桥梁的外展活动中发挥着关键作用。这项研究将建立一个统一框架并开发一套相互依赖的方法，用于制定、识别和利用多代理对称性来执行协作人工智能任务。该研究首先采用数学严谨的语言将多主体对称性的概念表述到对称马尔可夫博弈的框架中，揭示了其可以通过规划和学习方法利用的核心属性。然后，研究计划具体化了如何利用几种最常见的多智能体对称类型，包括排列对称、欧几里得对称和混合类型多智能体对称的层次结构。接下来，研究计划讨论对实践至关重要的问题，包括识别和利用近似多智能体对称性以及处理部分可观察性。最后，该研究以几个实际应用为特色，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 和刺激竞争研究既定计划 (EPSCoR) 联合资助。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。