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.

翻译：在时间紧迫条件下，用于人工监督的界面必须有效支持用户的情境感知。本研究探索基于强化学习（RL）的用户界面自适应方法，以个性化警报策略，从而在高亮关键事件的益处与中断的认知成本之间取得平衡。为实现无需实际部署的学习过程，我们整合了用户注视行为模型以模拟监控期间的注意力动态。通过配送无人机监督场景，我们展示了初步结果，表明基于强化学习的高亮策略能够超越静态的基于规则的方法，并讨论了智能监督支持所面临的挑战。