  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.
