  Trust in automation, or more recently trust in autonomy, has received
extensive research attention in the past two decades. The majority of prior
literature adopted a "snapshot" view of trust and typically evaluated trust
through questionnaires administered at the end of an experiment. This
"snapshot" view, however, does not acknowledge that trust is a time-variant
variable that can strengthen or decay over time. To fill the research gap, the
present study aims to model trust dynamics when a human interacts with a
robotic agent over time. The underlying premise of the study is that by
interacting with a robotic agent and observing its performance over time, a
rational human agent will update his/her trust in the robotic agent
accordingly. Based on this premise, we develop a personalized trust prediction
model based on Beta distribution and learn its parameters using Bayesian
inference. Our proposed model adheres to three major properties of trust
dynamics reported in prior empirical studies. We tested the proposed method
using an existing dataset involving 39 human participants interacting with four
drones in a simulated surveillance mission. The proposed method obtained a Root
Mean Square Error (RMSE) of 0.072, significantly outperforming existing
prediction methods. Moreover, we identified three distinctive types of trust
dynamics, the Bayesian decision maker, the oscillator, and the disbeliever,
respectively. This prediction model can be used for the design of
individualized and adaptive technologies.
