In the evolving landscape of human-autonomy teaming (HAT), fostering effective collaboration and trust between human and autonomous agents is increasingly important. To explore this, we used the game Overcooked AI to create dynamic teaming scenarios featuring varying agent behaviors (clumsy, rigid, adaptive) and environmental complexities (low, medium, high). Our objectives were to assess the performance of adaptive AI agents designed with hierarchical reinforcement learning for better teamwork and measure eye tracking signals related to changes in trust and collaboration. The results indicate that the adaptive agent was more effective in managing teaming and creating an equitable task distribution across environments compared to the other agents. Working with the adaptive agent resulted in better coordination, reduced collisions, more balanced task contributions, and higher trust ratings. Reduced gaze allocation, across all agents, was associated with higher trust levels, while blink count, scan path length, agent revisits and trust were predictive of the humans contribution to the team. Notably, fixation revisits on the agent increased with environmental complexity and decreased with agent versatility, offering a unique metric for measuring teammate performance monitoring. These findings underscore the importance of designing autonomous teammates that not only excel in task performance but also enhance teamwork by being more predictable and reducing the cognitive load on human team members. Additionally, this study highlights the potential of eye-tracking as an unobtrusive measure for evaluating and improving human-autonomy teams, suggesting eye gaze could be used by agents to dynamically adapt their behaviors.

翻译：在人机自主团队协作不断演进的背景下，促进人类与自主智能体之间的有效合作与信任日益重要。为探究此问题，我们利用游戏《Overcooked AI》创建了动态团队协作场景，其中包含不同智能体行为（笨拙型、刻板型、自适应型）与环境复杂度（低、中、高）。本研究旨在评估采用分层强化学习设计、以提升团队合作为目标的自适应AI智能体的性能，并测量与信任及合作变化相关的眼动追踪信号。结果表明，与其他智能体相比，自适应智能体在管理团队协作和创建跨环境公平任务分配方面更为有效。与自适应智能体协作带来了更好的协调性、更少的碰撞、更平衡的任务贡献以及更高的信任评分。在所有智能体条件下，减少的注视分配与更高的信任水平相关，而眨眼次数、扫描路径长度、智能体重访次数和信任度则可预测人类对团队的贡献。值得注意的是，对智能体的注视重访次数随环境复杂度增加而增加，随智能体适应能力增强而减少，这为衡量队友表现监控提供了一个独特指标。这些发现强调了设计自主团队成员的重要性，其不仅应在任务表现上出色，还应通过提升行为可预测性和降低人类团队成员认知负荷来促进团队协作。此外，本研究凸显了眼动追踪作为评估和改进人机自主团队的一种非侵入性测量手段的潜力，表明智能体可利用眼动注视信号动态调整其行为。