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 and forcing the robot to focus on the costly explicable behavior. We propose a computational model for capturing and modulating trust in such longitudinal human-robot interaction, where the human adopts a supervisory role. In our model, the robot integrates human's trust and their expectations from 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.

中文翻译：

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信任感知计划：在纵向人机交互中建立信任演化模型

团队成员之间的信任是任何成功合作的基本要求。因此，对于所有试图不仅成功地参加任务而且确保团队实现其目标的成员，增强和维护团队成员之间的信任已成为中心责任。信任管理的问题在人机混合的团队中尤其具有挑战性，因为人和机器人可能对手头的任务有不同的模型，因此对当前的操作过程可能有不同的期望，并迫使机器人专注于代价高昂的工作。明确的行为。我们提出了一种计算模型，用于在这种纵向人机交互中捕获和调节信任，其中人扮演监督角色。在我们的模型中，机器人将人类 机器人的信任和他们的期望，并将其纳入计划过程中，以在交互范围内建立并保持信任。通过建立所需的信任级别，机器人可以避免出现明确的解释性或可解释的行为，从而专注于最大化团队目标，而不必担心监督人员的干预和干预以阻止他们不一定理解的行为。我们将有关信任级别的这种推理建模为针对单个计划任务的元推理过程。我们还通过人类主题实验来验证我们的模型。机器人可以通过避免明确的解释性或可解释的行为来专注于最大化团队目标，而不必担心监督人员的干预和干预以阻止他们不一定理解的行为。我们将有关信任级别的这种推理建模为针对单个计划任务的元推理过程。我们还通过人类主题实验来验证我们的模型。机器人可以通过避免明确的解释性或可解释的行为来专注于最大化团队目标，而不必担心监督人员的干预和干预以阻止他们不一定理解的行为。我们将有关信任级别的这种推理建模为针对单个计划任务的元推理过程。我们还通过人类主题实验来验证我们的模型。