Integrating human and robot decision-making dynamics with feedback: Models and convergence analysis
Ming Cao, Andrew Stewart and Naomi Ehrich Leonard
Proceedings of the 47th IEEE Conference on Decision and Control, Cancun, Mexico, December 2008.
Leveraging research by psychologists on human decision-making, we present a human-robot
decision-making problem associated with a complex task and study the corresponding joint
decision-making dynamics. The collaborative task is designed so that the human makes
decisions just as human subjects make decisions in the two-alternative, forced-choice task,
a well-studied decision-making task in behavioral experiments. The human subject chooses
between two options at regular time intervals and receives a reward after each choice; for a
variety of reward structures, the behavioral experiments show convergence to suboptimal choices.
We propose a human-supervised robot foraging problem in
which the human supervisor makes a sequence of binary decisions to
assign the role of each robot in a group in response to a report from the robots on their resource return.
We discuss conditions under which the decision dynamics of
this human-robot task is reasonably well approximated by the kinds of reward structures studied
in the psychology experiments.
Using the Win-Stay, Lose-Switch human decision-making model, we prove convergence to the experimentally
observed aggregate human decision-making behavior for reward structures with
matching points. Finally, we propose an adaptive law for robot
reward feedback designed to help the human make optimal decisions.
(492 KB pdf)
Back to home page
Copyright 2008 IEEE. Personal use of this material is permitted. However,
permission to reprint/republish this material for advertising or promotional
purposes or for creating new collective works for resale or redistribution
to servers or lists, or to reuse any copyrighted component of this work in
other works must be obtained from the IEEE.