Trust between team members is an essential requirement for any successful cooperation. Thus, engendering and maintaining the fellow team members' trust becomes a central responsibility for any member trying to not only successfully participate in the task but to ensure the team achieves its goals. The problem of trust management is particularly challenging in mixed human-robot teams where the human and the robot may have different models about the task at hand and thus may have different expectations regarding the current course of action, thereby forcing the robot to focus on the costly explicable behavior. We propose a computational model for capturing and modulating trust in such iterated human-robot interaction settings, where the human adopts a supervisory role. In our model, the robot integrates human's trust and their expectations about the robot into its planning process to build and maintain trust over the interaction horizon. By establishing the required level of trust, the robot can focus on maximizing the team goal by eschewing explicit explanatory or explicable behavior without worrying about the human supervisor monitoring and intervening to stop behaviors they may not necessarily understand. We model this reasoning about trust levels as a meta reasoning process over individual planning tasks. We additionally validate our model through a human subject experiment.

翻译：团队成员间的信任是任何成功合作的基本要求。因此，培养并维持团队成员的信任，成为每个试图不仅成功参与任务、更确保团队实现目标的成员的核心责任。信任管理问题在人机混合团队中尤为棘手，因为人类与机器人可能对当前任务持有不同模型，从而对现有行动方案抱有不同的期望，这迫使机器人不得不专注于代价高昂的可解释行为。我们提出了一种计算模型，用于捕捉和调节此类迭代人机交互场景中的信任关系——其中人类承担监督角色。在该模型中，机器人将人类的信任及其对机器人的期望整合到自身的规划过程中，以在交互周期内建立并维持信任。通过建立所需的信任水平，机器人可以专注于最大化团队目标，而无需采取显式的解释性或可解释行为，也无需担心人类监督者因不理解其行为而进行监控与干预。我们将这种关于信任水平的推理建模为个体规划任务上的元推理过程，并通过受试者实验验证了该模型的有效性。