CAREER: Integrating Machine Learning with Game Theory for Multiagent Communication and Coordination

我们面临的许多社会挑战涉及多个决策者（代理人），每个人都有自己的目标或偏好。更重要的是，这些代理往往需要相互沟通和协调，以实现他们的目标或满足他们的偏好。例如，在安全、公共安全和环境可持续性领域，执法机构防范攻击者和偷猎者。这些机构经常与当地社区合作，通过沟通和协调，例如通过社区警务计划，或依靠司法合作者，打击对手的行动。博弈论是对多个决策者之间的战略互动进行推理的既定范式。它由数学模型组成，这些数学模型假设玩家的行为是理性的，他们会努力做出最佳决策，以获得自己的最佳结果。一些博弈论模型和算法已经成功地应用于该领域，以帮助执法机构在对手存在的情况下分配有限的资源。然而，复杂环境下的沟通和协调问题仍未得到充分探讨。本研究旨在为多智能体的通信与协调设计新的博弈论模型。此外，本研究试图开发新的机器学习增强计算框架来解决这些游戏。这些发现将应用于野生动物保护和食品银行运作等现实问题。本研究旨在建立具有不同承诺能力（即，一些代理可以首先承诺策略）设置的多智能体通信和协调的理论基础，使算法进步，并产生变革性的现实影响。本研究将提供以下问题的答案：(i)如何在大规模多智能体交互中找到最佳的沟通和协调策略？（ii）如何解释人类主体的有限理性？（iii）如何处理环境中的不确定性，例如通信中的噪音？该研究包括三个关键交互类别的三个重点：防御者-攻击者-社区交互，平台-用户交互和中介-代理交互。在每一篇文章中，研究人员试图通过以下方式回答这三个问题：(1)提出新的博弈论模型和解决方案概念；（ii）从理论上分析游戏的行为和计算方面，并描述协调和沟通的影响；（iii）根据数据建立人类行为模型；（iv）提出基于数学规划、深度学习和多智能体强化学习的高效算法，在给定人类行为模型和不确定性的情况下计算接近均衡策略。研究结果将丰富计算博弈论的知识体系，并将这一蓬勃发展的工作转变为新的研究课题，将博弈论与机器学习以及人工智能内外的其他研究领域结合起来。该奖项反映了美国国家科学基金会的法定使命，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。

Many societal challenges we are facing involve multiple decision-makers (agents), each with their own goals or preferences. More importantly, these agents often need to communicate and coordinate with each other to achieve their goals or satisfy their preferences. For example, in security, public safety, and environmental sustainability domains, law enforcement agencies defend against attackers and poachers. These agencies often work together with the local community to combat the actions of their opponents using communication and coordination, e.g., through community policing programs, or relying on justice collaborators. Game theory is an established paradigm for reasoning about strategic interactions among multiple decision-makers. It consists of mathematical models with the common assumptions that the players act rationally and they will try to make the best decisions to obtain their own best possible outcome. Several game-theoretic models and algorithms have been successfully deployed in the field to help law enforcement agencies allocate their limited resources in the presence of opponents. However, the problem of communication and coordination in complex environments is still underexplored. This research aims to design new game-theoretic models for multiagent communication and coordination. In addition, this research attempts to develop novel machine learning-enhanced computational frameworks for solving these games. These will findings be applied to the real-world problems of wildlife protection and food bank operations.This research seeks to establish theoretical foundations of multiagent communication and coordination in settings with varying commitment power (i.e., some agents can commit to a strategy first), make algorithmic advances, and make a transformative real-world impact. The research will provide answers to the following questions: (i) How to find the best communication and coordination strategies in large-scale multiagent interaction? (ii) How to account for the bounded rationality of human agents? (iii) How to deal with the uncertainties in the environment, e.g., noise in communication? The research consists of three thrusts for three critical classes of interactions: defender-attacker-community interaction, platform-users interaction, and mediators-agents interaction. In each thrust, the researchers attempt to answer the three questions by (i) propose new game-theoretic models and solution concepts; (ii) theoretically analyze the behavioral and computational aspects of the games and characterize the impact of coordination and communication; (iii) build human behavior models from data; (iv) propose efficient algorithms based on mathematical programming, deep learning, and multiagent reinforcement learning to compute close-to-equilibrium strategies given the human behavior models and uncertainties. The results will enrich the body of knowledge in computational game theory and transform the thriving line of work into new research topics that integrate game theory with machine learning and other research areas in and outside Artificial Intelligence.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.