Bounded Rational Intelligence and Metalearning in Autonomous Systems 

Abstract: Autonomous systems will be tasked with operating in complex, human-centric environments both in cooperation and in competition with humans and other agents. In this talk, issues of prediction in the context of game theory and multi-agent cyber-physical systems will be addressed. To account for the cognitive limitations of human and machine decision-makers, we introduce ideas and principles of bounded rationality for autonomy using tools from control theory and reinforcement learning. Specifically, we will formulate level-k thinking and cognitive hierarchy models in nonlinear and linear non-cooperative differential games, where each agent is assigned an intelligence level corresponding to several thinking iterations. The applicability of this approach will be highlighted via the example of a pursuit evasion game between Unmanned Aerial Vehicles. The versatility of the proposed methods will be shown via results of level-k thinking in discrete stochastic games. Finally, to design more advanced decision-making algorithms that explicitly exploit the learning abilities of the other agents in the environment, a meta-learning framework in games will be presented, via which an autonomous agent can achieve learning manipulation and deception.

Bio: Kyriakos G. Vamvoudakis was born in Athens, Greece. He received the Diploma (a 5-year degree, equivalent to a Master of Science) in Electronic and Computer Engineering from the Technical University of Crete, Greece in 2006 with highest honors. After moving to the United States of America, he studied at The University of Texas at Arlington with Frank L. Lewis as his advisor, and he received his M.S. and Ph.D. in Electrical Engineering in 2008 and 2011 respectively. During the period from 2012 to 2016 he was a project research scientist at the Center for Control, Dynamical Systems and Computation at the University of California, Santa Barbara. He was an assistant professor at the Kevin T. Crofton Department of Aerospace and Ocean Engineering at Virginia Tech until 2018. He currently serves as the Dutton-Ducoffe Endowed Professor and Associate Professor at The Daniel Guggenheim School of Aerospace Engineering at Georgia Tech. He holds a secondary appointment in the School of Electrical and Computer Engineering. His research interests include reinforcement learning, control theory, cyber-physical security, bounded rationality, and safe/assured autonomy. Dr. Vamvoudakis is the recipient of a 2019 ARO YIP award, a 2018 NSF CAREER award, a 2021 GT Chapter Sigma Xi Young Faculty Award, and of several international awards including the 2016 International Neural Network Society Young Investigator (INNS) Award, and the Best Researcher Award from the Automation and Robotics Research Institute in 2011. He is an associate editor for many journals.