Integrating human and robot decision-making dynamics with feedback: Models and convergence analysis

Ming Cao, Andrew Stewart and Naomi Ehrich Leonard

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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