Is It My Looks? Or Something I Said? The Impact of Explanations, Embodiment, and Expectations on Trust and Performance in Human-Robot Teams
Is It My Looks? Or Something I Said? The Impact of Explanations, Embodiment, and Expectations on Trust and Performance in Human-Robot Teams
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这是我的外表吗？
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
S. Hill
中科院分区：
文献类型：
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作者：
Ning Wang;D. Pynadath;E. Rovira;M. Barnes;S. Hill
信任对于人机交互的成功至关重要。研究表明，如果人们对机器人的决策过程有准确的理解，他们会更准确地信任机器人。部分可观测马尔可夫决策过程（POMDP）就是这样一种决策过程，但其定量推理通常对人们来说是不透明的。当机器人可以学习时，这种透明度的缺乏会加剧，使其决策更好，但也更难预测。最近的研究表明，通过自动生成基于POMDP的决策的解释来校准人机信任是有希望的。在这项工作中，我们探索的因素，可以潜在地与这些解释在影响人类决策的人类机器人团队。我们专注于解释与定量表达的不确定性和实验与机器人的常见设计因素：其实施和通信策略的情况下，错误。结果帮助我们确定有价值的属性和动态的人-机器人信任关系。
Trust is critical to the success of human-robot interaction. Research has shown that people will more accurately trust a robot if they have an accurate understanding of its decision-making process. The Partially Observable Markov Decision Process (POMDP) is one such decision-making process, but its quantitative reasoning is typically opaque to people. This lack of transparency is exacerbated when a robot can learn, making its decision making better, but also less predictable. Recent research has shown promise in calibrating human-robot trust by automatically generating explanations of POMDP-based decisions. In this work, we explore factors that can potentially interact with such explanations in influencing human decision-making in human-robot teams. We focus on explanations with quantitative expressions of uncertainty and experiment with common design factors of a robot: its embodiment and its communication strategy in case of an error. Results help us identify valuable properties and dynamics of the human-robot trust relationship.