Thanh Nguyen 
Assistant Professor 
Department of Computer Science
University of Oregon

Many real-world problems require the creation of robust AI models that include both learning and planning for an agent (or a team of agents) in interaction with adversaries in a multi-agent environment. In such a complex setting, it is important to predict strategic behavior of the adversaries, as well as to anticipate potential adversarial manipulations that could deteriorate the learning outcomes and the decision quality of our agents. In this talk, I will discuss the challenges of modeling adversaries’ decision making and the security of machine learning in data-driven multi-agent competitive environments. I will present our algorithms to address these challenges that explore techniques in reinforcement learning, game theory, and optimization research. In addition, I will introduce some of the real-world applications of our algorithms in the domains of wildlife protection and public health.

Talk Location: Jubel 121