We propose an approach that allows a robot to evaluate its trustworthiness and adapt its behavior accordingly. The The addition of a robot to a team can be difficult if trust estimate, which we refer to as an inverse trust estimate, the human teammates do not trust the robot. This differs from traditional computational trust metrics in that it can result in underutilization or disuse of the robot, measures how much trust other agents have in the robot rather even if the robot has skills or abilities that are necessary than how much trust the robot has in other agents. Since the to achieve team goals or reduce risk. To robot can only use observable information and not information help a robot integrate itself with a human team, we that is internal to the teammates' reasoning, the inverse present an agent algorithm that allows a robot to estimate trust estimate relies on evaluating the standard interactions its trustworthiness and adapt its behavior accordingly.

Robots can be added to human teams to provide improved capabilities or to perform tasks that humans are unsuited for. However, in order to get the full benefit of the robots the human teammates must use the robots in the appropriate situations. If the humans do not trust the robots, they may underutilize them or disuse them which could result in a failure to achieve team goals. We present a robot that is able to estimate its trustworthiness and adapt its behavior accordingly. This technique helps the robot remain trustworthy even when changes in context, task or teammates are possible.