Data Science Seminar by Shuo Han: Strategic influencing in multi-agent systems

Speaker: Shuo Han, University of Illinois at Chicago

Title: Strategic Influencing in Multi-Agent Systems

Abstract:

In a multi-agent system, it is well known that an agent is capable of implicitly influencing the behaviors of other agents by playing her actions strategically. This allows the agent to potentially take advantage of opponents in a competitive setting or facilitate coordination with partners in a collaborative setting. In game theory, an optimal influencing strategy is characterized by the solution concept of Stackelberg equilibrium. Nevertheless, efficient and provably correct algorithms for computing a Stackelberg equilibrium only exist for relatively simple games. In this talk, I will discuss methods for computing a Stackelberg equilibrium in more complex and realistic game setups, including 1) influencing an agent who makes sequential decisions and 2) influencing multiple types of agents.

Bio: Shuo Han is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Illinois Chicago (UIC). Before joining UIC, he was a postdoctoral researcher in the Department of Electrical and Systems Engineering at the University of Pennsylvania. He received his B.E. and M.E. in Electronic Engineering from Tsinghua University, and his Ph.D. in Electrical Engineering from the California Institute of Technology. His research interests lie broadly in the areas of optimization and control theory, with a particular emphasis on multi-agent systems.

Data Science Seminar