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

点击翻译按钮获取中文摘要

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.