提出信任动态模型，提升多人多机器人协作的任务成功率。
Modeling and Evaluating Trust Dynamics in Multi-Human Multi-Robot Task Allocation
- 构建期望确认信任模型，量化人机团队中的信任变化。
- 在2H-2R到10H-10R配置中，任务成功率提升，耗时与错误率下降。
- 适合研究人机协作、动态任务分配的学者与系统设计者。
信任在人机协作中至关重要，尤其在复杂操作环境中，多人类-多机器人（MH-MR）团队更依赖信任维持凝聚力。尽管单人单机器人研究已证明信任可显著提升性能与体验，但其在多主体任务分配中的应用仍不充分。本文提出期望确认信任（ECT）模型，用于建模MH-MR团队中的信任动态。在2H-2R、5H-5R和10H-10R三种团队配置下，将该模型与五种现有信任模型及无信任基线对比，结果表明：采用ECT模型可提高任务成功率达X%，平均完成时间缩短Y%，任务错误率降低Z%。研究揭示了信任驱动任务分配的复杂性，并探讨了将其融入算法的意义，提出未来需发展自适应信任机制以平衡效率与性能。
原文摘要 · Abstract (English)
Trust is essential in human-robot collaboration, particularly in multi-human, multi-robot (MH-MR) teams, where it plays a crucial role in maintaining team cohesion in complex operational environments. Despite its importance, trust is rarely incorporated into task allocation and reallocation algorithms for MH-MR collaboration. While prior research in single-human, single-robot interactions has shown that integrating trust significantly enhances both performance outcomes and user experience, its role in MH-MR task allocation remains underexplored. In this paper, we introduce the Expectation Confirmation Trust (ECT) Model, a novel framework for modeling trust dynamics in MH-MR teams. We evaluate the ECT model against five existing trust models and a no-trust baseline to assess its impact on task allocation outcomes across different team configurations (2H-2R, 5H-5R, and 10H-10R). Our results show that the ECT model improves task success rate, reduces mean completion time, and lowers task error rates. These findings highlight the complexities of trust-based task allocation in MH-MR teams. We discuss the implications of incorporating trust into task allocation algorithms and propose future research directions for adaptive trust mechanisms that balance efficiency and performance in dynamic, multi-agent environments.
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