聚类人类信任动态以实现定制化实时预测
Clustering Human Trust Dynamics for Customized Real-time Prediction
AI总结：
本文提出基于信任动态聚类人类用户来构建定制化信任预测模型的方法，以少量数据克服个体差异，并证明其优于通用模型和基于人口统计因素的定制化模型。
摘要：
在先进自动化技术中，信任校准对于确保适当的用户接受度是必要的。实现信任校准的一个重大挑战是实时定量估计人类信任。尽管存在多种信任模型，但这些模型的预测性能有限，部分原因在于信任动态中的个体差异。为每个人建立个性化模型可以解决这一问题，但需要为每个用户收集大量数据。我们提出了一种通过基于信任动态对人类进行聚类来开发定制化模型的方法。这种基于聚类的方法解决了信任动态中的个体差异，同时所需数据量显著少于个性化模型。我们表明，基于聚类的定制化模型不仅优于基于整个人群的通用模型，而且优于基于简单人口统计因素的定制化模型。具体而言，我们提出分别基于“自信”组和“怀疑”组参与者的两个模型可以代表整个人群的信任行为。与“怀疑”组参与者相比，“自信”组参与者具有更高的初始信任水平，在遇到低可靠性操作时信任下降更慢，并且在低可靠性操作后的信任修复期间具有更高的信任水平。总之，基于聚类的定制化模型提高了信任预测性能，以用于进一步的信任校准考虑。
英文摘要：
Trust calibration is necessary to ensure appropriate user acceptance in advanced automation technologies. A significant challenge to achieve trust calibration is to quantitatively estimate human trust in real-time. Although multiple trust models exist, these models have limited predictive performance partly due to individual differences in trust dynamics. A personalized model for each person can address this issue, but it requires a significant amount of data for each user. We present a methodology to develop customized model by clustering humans based on their trust dynamics. The clustering-based method addresses the individual differences in trust dynamics while requiring significantly less data than personalized model. We show that our clustering-based customized models not only outperform the general model based on entire population, but also outperform simple demographic factor-based customized models. Specifically, we propose that two models based on ``confident'' and ``skeptical'' group of participants, respectively, can represent the trust behavior of the population. The ``confident'' participants, as compared to the ``skeptical'' participants, have higher initial trust levels, lose trust slower when they encounter low reliability operations, and have higher trust levels during trust-repair after the low reliability operations. In summary, clustering-based customized models improve trust prediction performance for further trust calibration considerations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
