  Often, when modeling human decision-making behaviors in the context of
human-robot teaming, the emotion aspect of human is ignored. Nevertheless, the
influence of emotion, in some cases, is not only undeniable but beneficial.
This work studies the human-like characteristics brought by regret emotion in
one-human-multi-robot teaming for the application of domain search. In such
application, the task management load is outsourced to the robots to reduce the
human's workload, freeing the human to do more important work. The regret
decision model is first used by each robot for deciding whether to request
human service, then is extended for optimally queuing the requests from
multiple robots. For the movement of the robots in the domain search, we
designed a path planning algorithm based on dynamic programming for each robot.
The simulation shows that the human-like characteristics, namely, risk-seeking
and risk-aversion, indeed bring some appealing effects for balancing the
workload and performance in the human-multi-robot team.
