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.

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