ABSTRACT

Profitable integration of human and robot decision-making dynamics should take advantage of strengths of human decision-makers as well as strengths of robotic agents.   A major challenge in achieving this goal is understanding how humans make decisions and what are their associated strengths and weaknesses. Correspondingly, a central tenet of this work is to leverage the experimental and modeling work of psychologists and behavioral scientists on human decision-making. We focus on a well-studied class of sequential binary decision-making tasks. We introduce a decision-making problem associated with a collective robotic foraging task that integrates human and robotic decision making dynamics with feedback. To explore the integrated decision dynamics, we present two models of human decision-making and with these models we prove convergence of the human behavior to the observed aggregate decision-making. We also show how adaptive laws for the robot feedback that use only local information can be applied to help the human make optimal decisions.

BIOGRAPHICAL SKETCH

Ming Cao is currently an assistant professor of Discrete Technology and Production Automation with Faculty of Mathematics and Natural Sciences at the University of Groningen, the Netherlands. He received the Bachelor degree in 1999 and the Master degree in 2002 from Tsinghua University, Beijing, China, and the Ph.D degree in 2007 from Yale University, New Haven, CT, USA, all in electrical engineering. From September 2007 to August 2008, he was a Postdoctoral Research Associate with the Department of Mechanical and Aerospace Engineering at Princeton University, Princeton, NJ, USA. He worked as a Research Intern during the summer of 2006 with the Mathematical Sciences Department at IBM T. J. Watson Research Center, NY, USA. His main research interest is in autonomous agents and multi-agent systems, mobile sensor networks and social robotics. Since 2009, he has been an associate editor for Systems and Control Letters responsible for the area of cooperative control and multi-agent systems.