论文标题
自动化飞行冲突的解决：为空中交通管制员服务的深入强化学习
Automating the resolution of flight conflicts: Deep reinforcement learning in service of air traffic controllers
论文作者
论文摘要
与战术冲突检测和分辨率（CD \＆R）工具（ATCO）使用的工具（ATCO）使用相比，密集且复杂的空中交通情况需要更高的自动化水平。但是，空中交通管制（ATC）域非常重要，需要操作员舒适地放弃控制的AI系统，以确保运营完整性和自动化采用。实现该目标的两个主要因素是解决方案的质量和决策的透明度。本文建议使用在多种设置中运行的图形卷积加固学习方法，在该设置中，每个代理（飞行）执行CD \＆R任务，并与其他代理共同执行。我们表明，这种方法可以提供有关利益相关者利益（空中交通管制员和空域用户）的高质量解决方案，从而解决了运营透明度问题。
Dense and complex air traffic scenarios require higher levels of automation than those exhibited by tactical conflict detection and resolution (CD\&R) tools that air traffic controllers (ATCO) use today. However, the air traffic control (ATC) domain, being safety critical, requires AI systems to which operators are comfortable to relinquishing control, guaranteeing operational integrity and automation adoption. Two major factors towards this goal are quality of solutions, and transparency in decision making. This paper proposes using a graph convolutional reinforcement learning method operating in a multiagent setting where each agent (flight) performs a CD\&R task, jointly with other agents. We show that this method can provide high-quality solutions with respect to stakeholders interests (air traffic controllers and airspace users), addressing operational transparency issues.
