研究机器人配送中信任修复策略如何影响人类信任，发现长期解释最有效。
Modeling Trust Dynamics in Robot-Assisted Delivery: Impact of Trust Repair Strategies
- 用输入输出隐马尔可夫模型建模人类信任动态与行为概率。
- 长期解释修复信任效果最佳，否认最能防止信任下降。
- 模型信任值与自评一致，适合实时调整人机信任关系。
随着自主系统效率与可靠性的提升，它们在各类任务中逐渐成为人类的得力助手。在机器人辅助配送场景中，我们研究了机器人表现及信任修复策略对人类信任的影响。实验中，参与者在执行次要任务时可选择让机器人自主配送或手动控制。考察的信任修复策略包括简短与长期解释、道歉与承诺、否认。基于人类参与者的数据，我们采用输入输出隐马尔可夫模型（IOHMM）建模人类行为，捕捉信任与行动概率的动态变化。结果表明，当信任较高时，人类更倾向于让机器人自主运行。状态转移分析显示，长期解释在故障后修复信任的效果最优，而否认最能有效预防信任损失。此外，模型生成的信任估计值与自我报告值同构，具备可解释性。该模型为开发实时调节人类信任的最优策略提供了基础。
原文摘要 · Abstract (English)
With increasing efficiency and reliability, autonomous systems are becoming valuable assistants to humans in various tasks. In the context of robot-assisted delivery, we investigate how robot performance and trust repair strategies impact human trust. In this task, while handling a secondary task, humans can choose to either send the robot to deliver autonomously or manually control it. The trust repair strategies examined include short and long explanations, apology and promise, and denial. Using data from human participants, we model human behavior using an Input-Output Hidden Markov Model (IOHMM) to capture the dynamics of trust and human action probabilities. Our findings indicate that humans are more likely to deploy the robot autonomously when their trust is high. Furthermore, state transition estimates show that long explanations are the most effective at repairing trust following a failure, while denial is most effective at preventing trust loss. We also demonstrate that the trust estimates generated by our model are isomorphic to self-reported trust values, making them interpretable. This model lays the groundwork for developing optimal policies that facilitate real-time adjustment of human trust in autonomous systems.
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