论文标题

在人类机器人顺序决策任务中群集信任动态

Clustering Trust Dynamics in a Human-Robot Sequential Decision-Making Task

论文作者

Bhat, Shreyas, Lyons, Joseph B., Shi, Cong, Yang, X. Jessie

论文摘要

在本文中,我们介绍了人类机器人团队中信任感知的顺序决策的框架。我们将问题作为有限的马尔可夫决策过程将问题建模为基于奖励的绩效指标,从而使机器人代理可以提出信任的建议。人类受试者实验的结果表明,拟议的信任更新模型能够准确捕获人类代理的时刻信任变化。此外,我们将参与者的信任动力归因于三类,即贝叶斯决策者,振荡器和不信者,并确定可用于预测某人将属于哪种信任动态类型的个人特征。我们发现,与贝叶斯决策者和振荡器相比,对机器人代理商的外向,不太满意,对机器人的期望较低。振荡器比贝叶斯决策者更加沮丧。

In this paper, we present a framework for trust-aware sequential decision-making in a human-robot team. We model the problem as a finite-horizon Markov Decision Process with a reward-based performance metric, allowing the robotic agent to make trust-aware recommendations. Results of a human-subject experiment show that the proposed trust update model is able to accurately capture the human agent's moment-to-moment trust changes. Moreover, we cluster the participants' trust dynamics into three categories, namely, Bayesian decision makers, oscillators, and disbelievers, and identify personal characteristics that could be used to predict which type of trust dynamics a person will belong to. We find that the disbelievers are less extroverted, less agreeable, and have lower expectations toward the robotic agent, compared to the Bayesian decision makers and oscillators. The oscillators are significantly more frustrated than the Bayesian decision makers.

扫码加入交流群

加入微信交流群

微信交流群二维码

扫码加入学术交流群,获取更多资源