Note: This content is accessible to all versions of every browser. However, this browser does not seem to support current Web standards, preventing the display of our site's design details.

  

Stability and selection in multiagent learning

Back
Abstract:
Recent years have seen significant interest in the area of multiagent or distributed architecture control, with motivating applications ranging from autonomous vehicle teams to communication networks to smart grid. The general setup is a collection of multiple decision-making elements interacting locally, perhaps striving to achieve a common collective objective. In multiagent learning, agents dynamically adapt to the actions of other agents, thereby effectively making the environment non-stationary from the perspective of any single agent. The resulting dynamics can exhibit behaviors ranging from chaos to convergence. This talk focuses on the two concerns of stability and selection---i.e., do agents converge, and if so, to what configurations? We discuss "stability" of population games through new connections between passivity theory and evolutionary game theory. We discuss "selection" in evolutionary games using the notion of stochastic stability and demonstrate its broader applicability in various settings.

Type of Seminar:
Control Seminar Series
Speaker:
Prof. Jeff Shamma
Georgia Institut of Technology
Date/Time:
Mar 16, 2016   5.15pm
Location:

ETZ E 8
Contact Person:

File Download:

Request a copy of this publication.
Biographical Sketch:
Jeff S. Shamma is the Julian T. Hightower Chair in Systems & Control (currently on leave) in the School of Electrical and Computer Engineering at the Georgia Institute of Technology (Georgia Tech) and a Professor of Electrical Engineering in the King Abdullah University of Science and Technology (KAUST). He received a BS in Mechanical Engineering from Georgia Tech in 1983 and a PhD in Systems Science and Engineering from the Massachusetts Institute of Technology in 1988. He held faculty positions at the University of Minnesota, University of Texas-Austin, and University of California-Los Angeles. Prof. Shamma is a recipient of the NSF Young Investigator Award (1992), the American Automatic Control Council Donald P. Eckman Award (1996), and the Mohammed Dahleh Award (2013), and he is a Fellow of the IEEE (2006). Prof. Shamma's research is in the general area of feedback control and systems theory. His most recent research has been in decision and control for distributed multiagent systems and the related topics of game theory and network science, with applications to cyberphysical and societal network systems.