Prof. Jason Marden
Dept. of Electrical, Computer & Energy Engineering,
University of Colorado at Boulder.
The central goal in multiagent systems is to design local control laws for the individual agents to ensure that the emergent global behavior is desirable with respect to a given system level objective. Ideally, a system designer seeks to satisfy this goal while conditioning each agent’s control law on the least amount of information possible. This talk focuses on achieving this goal using the field of game theory. In particular, we derive a systematic methodology for designing local agent objective functions that guarantees (i) an equivalence between the resulting Nash equilibria and the optimizers of the system level objective and (ii) that the resulting game possesses an inherent structure that can be exploited in distributed learning, e.g., potential games. The control design can then be completed utilizing any distributed learning algorithm which guarantees convergence to a Nash equilibrium for the attained game structure. Furthermore, in many settings the resulting controllers will be inherently robust to a host of uncertainties including asynchronous clock rates, delays in information, and component failures.
Jason Marden is an Assistant Professor in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado. He received a BS in Mechanical Engineering in 2001 from UCLA, and a PhD in Mechanical Engineering in 2007, also from UCLA, under the supervision of Jeff S. Shamma, where he was awarded the Outstanding Graduating PhD Student in Mechanical Engineering. After graduating from UCLA, he served as a junior fellow in the Social and Information Sciences Laboratory at the California Institute of Technology until 2010 when he joined the University of Colorado. In 2012, he received the Donald P. Eckman award and an AFOSR Young Investigator Award. His research interests focus on game theoretic methods for feedback control of distributed multiagent systems.