CAREER: Enhancing Trust-Driven Human-Autonomy Interaction: Modeling Trust Dynamics and Supporting Trust Calibration

职业：增强信任驱动的人类自主交互：对信任动态进行建模并支持信任校准

• 批准号: 2045009
• 负责人: Xi Jessie Yang
• 金额: $ 54.98万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-01 至 2026-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2045009&HistoricalAwards=false
• 关键词: CAREER; Enhancing; Trust; Driven; Human

This project aims to tackle a fundamental question at the heart of human-technology partnership: How can designers facilitate the establishment of appropriate trust in technology? Advanced technologies such as autonomous vehicles and collaborative robots are entering every sector of the economy and will fundamentally alter the way people live and work. However, realizing the full economic, safety, and health potential of these technologies is only possible if people establish appropriate trust in them. This project aims to understand and model the formation and evolution of trust, and to develop adaptive autonomy that facilitates the establishment of appropriate trust. The project advances STEM education and workforce development by nurturing the next generation of scientists in human-autonomy interaction, and by developing outreach activities for K-12 students with an emphasis on increasing participation of women and underrepresented minorities, and for working professionals aimed at helping them adapt to the future workplace wherein humans and autonomous agents will increasingly work as a team. The research work has three main thrusts: (1) modeling temporal dynamics of trust formation and evolution; (2) estimating a person's trust in autonomy from behavioral and physiological information; and (3) developing methods that enable the autonomous agent to adapt its behavior and guide a person toward a more desired level of trust required for successful operation. The project will conduct multiple human-in-the-loop studies on a platform with humans interacting with autonomous drones in several situations, including search and rescue where a sequence of tasks and decisions are required. Based on the data, trust dynamics models, trust inference algorithms and adaptive methods will be built, tested and validated.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目旨在解决人类与技术合作关系的核心问题：设计师如何促进建立对技术的适当信任？自动驾驶汽车和协作机器人等先进技术正在进入经济的各个领域，并将从根本上改变人们的生活和工作方式。然而，只有当人们对这些技术建立适当的信任时，才有可能充分实现这些技术的经济、安全和健康潜力。该项目旨在理解和模拟信任的形成和演变，并发展适应性自治，促进建立适当的信任。该项目通过培养人类自主互动的下一代科学家，并通过为K-12学生开发外展活动，重点是增加妇女和代表性不足的少数民族的参与，以及旨在帮助他们适应未来工作场所的工作人员，来推进STEM教育和劳动力发展。研究工作有三个主要方面：（1）建模信任的形成和演变的时间动态;（2）从行为和生理信息中估计一个人对自主的信任;（3）开发使自主代理能够适应其行为并引导一个人走向成功操作所需的更理想的信任水平的方法。该项目将在一个平台上进行多项人在回路研究，在几种情况下，人类与自主无人机进行交互，包括需要一系列任务和决策的搜索和救援。基于这些数据，将建立、测试和验证信任动态模型、信任推理算法和自适应方法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Human operators’ blind compliance, reliance, and dependence behaviors when working with imperfect automation: A meta-analysis

人类操作员在不完美的自动化环境下工作时的盲目服从、依赖和依赖行为：荟萃分析

• DOI: 10.1109/ichms56717.2022.9980609
• 发表时间: 2022
• 期刊: 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS
• 作者: Schuler, Patrik T.;Yang, X. Jessie
• 通讯作者: Yang, X. Jessie

Evaluating Effects of Enhanced Autonomy Transparency on Trust, Dependence, and Human-Autonomy Team Performance over Time

随着时间的推移，评估增强的自主透明度对信任、依赖和人类自主团队绩效的影响

• DOI: 10.1080/10447318.2022.2097602
• 发表时间: 2022
• 期刊: International Journal of Human–Computer Interaction
• 作者: Luo, Ruikun;Du, Na;Yang, X. Jessie
• 通讯作者: Yang, X. Jessie

TIP: A Trust Inference and Propagation Model in Multi-Human Multi-Robot Teams

• DOI: 10.1145/3568294.3580164
• 发表时间: 2023-01
• 期刊: Companion of the 2023 ACM/IEEE International Conference on Human-Robot Interaction
• 作者: Yaohui Guo;Jessie X. Yang;Cong Shi