TY - GEN
T1 - Integrating human and robot decision-making dynamics with feedback
T2 - 47th IEEE Conference on Decision and Control, CDC 2008
AU - Cao, Ming
AU - Stewart, Andrew
AU - Leonard, Naomi Ehrich
PY - 2008
Y1 - 2008
N2 - 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, forcedchoice 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 humansupervised 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.
AB - 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, forcedchoice 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 humansupervised 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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U2 - 10.1109/CDC.2008.4739103
DO - 10.1109/CDC.2008.4739103
M3 - Conference contribution
AN - SCOPUS:62949123656
SN - 9781424431243
T3 - Proceedings of the IEEE Conference on Decision and Control
SP - 1127
EP - 1132
BT - Proceedings of the 47th IEEE Conference on Decision and Control, CDC 2008
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 December 2008 through 11 December 2008
ER -