In this project, we study how trust evolves when humans repeatedly interact with a robot recommendation system. We try to leverage quantitative models of trust to predict human behavior and design interaction strategies for the robot to promote trust in the robot’s recommendations.

We model the interaction as a trust-aware Markov Decision Process (trust-aware MDP) that consists of States, Actions, Transition Function, Reward Function, and Human Behavior Model.

We have developed a high-fidelity 3D environment that simulates a human-robot team performing an Intelligence, Surveillance, and Reconnaissance Mission using Unreal Engine. A few screenshots of the environment can be seen in the images below.