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We consider scenarios where a worker robot, who may be unaware of the human'sexact expectations, may have the incentive to deviate from a preferred plan(e.g. safe but costly) when a human supervisor is not monitoring it. On theother hand, continuous monitoring of the robot's behavior is often difficultfor humans because it costs them valuable resources (e.g., time, cognitiveoverload, etc.). Thus, to optimize the cost of monitoring while ensuring therobots follow the {\em safe} behavior and to assist the human to deal with thepossible unsafe robots, we model this problem in a game-theoretic framework oftrust. In settings where the human does not initially trust the robot,pure-strategy Nash Equilibrium provides a useful policy for the human.Unfortunately, we show the formulated game often lacks a pure strategy Nashequilibrium. Thus, we define the concept of a trust boundary over the mixedstrategy space of the human and show that it helps to discover optimalmonitoring strategies. We conduct humans subject studies that demonstrate (1)the need for coming up with optimal monitoring strategies, and (2) the benefitsof using strategies suggested by our approach.

Inference of Human's Observation Strategy for Monitoring Robot's Behavior based on a Game-Theoretic Model of Trust