  We present the effect of adapting to human preferences on trust in a
human-robot teaming task. The team performs a task in which the robot acts as
an action recommender to the human. It is assumed that the behavior of the
human and the robot is based on some reward function they try to optimize. We
use a new human trust-behavior model that enables the robot to learn and adapt
to the human's preferences in real-time during their interaction using Bayesian
Inverse Reinforcement Learning. We present three strategies for the robot to
interact with a human: a non-learner strategy, in which the robot assumes that
the human's reward function is the same as the robot's, a non-adaptive learner
strategy that learns the human's reward function for performance estimation,
but still optimizes its own reward function, and an adaptive-learner strategy
that learns the human's reward function for performance estimation and also
optimizes this learned reward function. Results show that adapting to the
human's reward function results in the highest trust in the robot.
