  In this paper, we present a framework for trust-aware sequential
decision-making in a human-robot team. We model the problem as a finite-horizon
Markov Decision Process with a reward-based performance metric, allowing the
robotic agent to make trust-aware recommendations. Results of a human-subject
experiment show that the proposed trust update model is able to accurately
capture the human agent's moment-to-moment trust changes. Moreover, we cluster
the participants' trust dynamics into three categories, namely, Bayesian
decision makers, oscillators, and disbelievers, and identify personal
characteristics that could be used to predict which type of trust dynamics a
person will belong to. We find that the disbelievers are less extroverted, less
agreeable, and have lower expectations toward the robotic agent, compared to
the Bayesian decision makers and oscillators. The oscillators are significantly
more frustrated than the Bayesian decision makers.
