智能人机监督支持：将强化学习与注视模拟整合以个性化高亮
Intelligent support for Human Oversight: Integrating Reinforcement Learning with Gaze Simulation to Personalize Highlighting
AI总结：
本文提出利用强化学习与注视模拟技术，通过个性化高亮策略提升人机监督下的情境意识与决策效率。
摘要：
人机监督接口必须在时间紧迫条件下有效支持用户的情境意识。我们探索基于强化学习（RL）的UI适应，以个性化警报策略，平衡高亮关键事件的益处与中断带来的认知成本。为实现无需真实世界部署的学习，我们整合用户注视行为模型，模拟监控期间的注意力动态。在无人机监督场景中，我们展示了初步结果，表明基于RL的高亮方法可以超越静态、基于规则的方法，并讨论智能监督支持的挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
