信任感知人机交互中的逆向心理
Reverse Psychology in Trust-Aware Human-Robot Interaction
摘要：
为了促进有效的人机交互（HRI），信任感知HRI已被提出，其中机器人代理明确考虑人类的信任在其规划和决策。信任感知HRI的成功依赖于信任动态模型和信任行为模型的规范。在本研究中，我们提出了一个新的信任行为模型，即逆向心理模型，并将其与常用的停用模型进行了比较。我们研究了这两个模型如何影响机器人的最优策略和人机团队的表现。结果表明，机器人会故意“操纵”人类的信任在逆向心理模型。为了纠正这种“操纵”行为，我们提出了一个信任寻求奖励功能，促进信任的建立，而不会显着牺牲团队绩效。
英文摘要：
To facilitate effective human-robot interaction (HRI), trust-aware HRI has been proposed, wherein the robotic agent explicitly considers the human's trust during its planning and decision making. The success of trust-aware HRI depends on the specification of a trust dynamics model and a trust-behavior model. In this study, we proposed one novel trust-behavior model, namely the reverse psychology model, and compared it against the commonly used disuse model. We examined how the two models affect the robot's optimal policy and the human-robot team performance. Results indicate that the robot will deliberately "manipulate" the human's trust under the reverse psychology model. To correct this "manipulative" behavior, we proposed a trust-seeking reward function that facilitates trust establishment without significantly sacrificing the team performance.
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