Abstract
Interfaces for human oversight must effectively support users' situation awareness under time-critical conditions. We explore reinforcement learning (RL)-based UI adaptation to personalize alerting strategies that balance the benefits of highlighting critical events against the cognitive costs of interruptions. To enable learning without real-world deployment, we integrate models of users' gaze behavior to simulate attentional dynamics during monitoring. Using a delivery-drone oversight scenario, we present initial results suggesting that RL-based highlighting can outperform static, rule-based approaches and discuss challenges of intelligent oversight support.
Abstract (translated)
人类监督的界面必须在时间紧迫的情况下有效地支持用户的态势感知。我们探索了基于强化学习（RL）的用户界面适应，以个性化警报策略，在突出关键事件的好处和中断的认知成本之间取得平衡。为了实现不进行实际部署即可学习的目标，我们将用户注视行为模型整合进来，以此来模拟监控过程中的注意力动态变化。通过一个送货无人机监督场景，我们展示了初步结果表明基于RL的高亮显示方法可以优于静态、规则驱动的方法，并讨论了智能监督支持所面临的挑战。
URL
https://arxiv.org/abs/2602.08403
