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

职业：将机器学习与博弈论相结合以实现多智能体通信和协调

• 批准号: 2046640
• 负责人: FEI FANG
• 金额: $ 46.4万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-04-01 至 2026-03-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2046640&HistoricalAwards=false
• 关键词: CAREER; Integrating; Machine; Learning; Game

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.

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，这些智能体经常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，执法机构要防范袭击者和偷猎者。这些机构经常与当地社区合作，通过沟通和协调打击反对者的行动，例如，通过社区警务项目，或者依靠司法合作者。博弈论是一种用于推理多个决策者之间战略互动的既定范式。它由数学模型组成，共同假设参与者理性行事，他们会试图做出最好的决定，以获得自己最好的结果。一些博弈论模型和算法已经成功地部署在该领域，以帮助执法机构在对手面前分配其有限的资源。然而，在复杂环境中的沟通和协调问题仍然没有得到充分探讨。本研究的目的是设计新的博弈论模型的多智能体通信和协调。此外，本研究试图开发新的机器学习增强的计算框架来解决这些游戏。 这些发现将被应用到野生动物保护和食物银行运营的现实问题中。本研究旨在建立多智能体通信和协调的理论基础，在不同的承诺权力（即，一些代理可以首先致力于策略），进行算法改进，并产生变革性的现实世界影响。研究将回答以下问题：（一）如何找到最佳的沟通和协调策略，在大规模的多智能体交互？(ii)如何解释人类代理人的有限理性？(iii)如何处理环境中的不确定性，例如，沟通中的噪音？该研究包括三个关键类的交互：防御者-攻击者-社区交互，平台-用户交互和中介-代理交互。在每一个推力，研究人员试图回答这三个问题：（i）提出新的博弈理论模型和解决方案的概念;（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）从数据中建立人类行为模型;（iv）从数据中建立人类行为模型。（iv）提出基于数学规划，深度学习，和多智能体强化学习，以计算接近平衡的策略，给定的人类行为模型和不确定性。研究结果将丰富计算博弈论的知识体系，并将蓬勃发展的研究领域转变为新的研究课题，将博弈论与机器学习和人工智能内外的其他研究领域相结合。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

Multi-defender Security Games with Schedules

多后卫安全游戏及时间表

• 发表时间: 2023
• 期刊: Springer
• 作者: Zimeng Song, Chun Kai

Navigates Like Me: Understanding How People Evaluate Human-Like AI in Video Games

• DOI: 10.1145/3544548.3581348
• 发表时间: 2023-03
• 期刊: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
• 作者: Stephanie Milani;Arthur Juliani;I. Momennejad;Raluca Georgescu;Jaroslaw Rzepecki;Alison Shaw;Gavin Costello;Fei Fang;Sam Devlin;Katja Hofmann

Bandit Data-Driven Optimization for Crowdsourcing Food Rescue Platforms

Bandit 数据驱动的众包食品救援平台优化

• DOI: 10.1609/aaai.v36i11.21475
• 发表时间: 2022
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Shi, Zheyuan Ryan;Wu, Zhiwei Steven;Ghani, Rayid;Fang, Fei
• 通讯作者: Fang, Fei

Robust reinforcement learning under minimax regret for green security

绿色安全的极小极大遗憾下的鲁棒强化学习

• 发表时间: 2021
• 期刊: Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence
• 作者: Xu, Lily;Perrault, Andrew;Fang, Fei;Chen, Haipeng;Tambe, Milind
• 通讯作者: Tambe, Milind

NewsPanda: Media Monitoring for Timely Conservation Action

NewsPanda：媒体监测及时采取保护行动

• DOI: 10.1609/aaai.v37i13.26841
• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Keh, Sedrick Scott;Shi, Zheyuan Ryan;Patterson, David J.;Bhagabati, Nirmal;Dewan, Karun;Gopala, Areendran;Izquierdo, Pablo;Mallick, Debojyoti;Sharma, Ambika;Shrestha, Pooja
• 通讯作者: Shrestha, Pooja