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.
人类监督的智能支持：集成强化学习与视线模拟以实现个性化高亮 / Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting
这篇论文提出了一种新方法，通过结合强化学习和模拟人眼视线行为，为无人机监控等需要快速决策的场景，智能地生成个性化的界面高亮提示，以在提醒关键信息和避免干扰之间取得更好平衡。
源自 arXiv: 2602.08403
