An Interactive Conflict Solver for Learning Air Traffic Conflict Resolutions

Abstract

The increasing demand in air transportation is pushing the current air traffic management system to its limits in the airspace capacity and workload of air traffic controllers (ATCOs). ATCOs are in an urgent need of assistant tools to aid them in dealing with increased traffic, specifically in resolving potential conflict. Because current automated conflict resolutions are not in conformance with the thinking or preferences of individual ATCOs, consequently, they are unlikely accepted by the ATCOs. In this work, an artificial intelligence (AI) system is built as a digital assistant to support ATCOs in resolving potential conflicts. Our system consists of two core components: an intelligent interactive conflict solver (iCS) to acquire ATCOs’ demonstrations, and an AI agent. The AI agent is based on reinforcement learning to suggest conflict resolutions. It is observed that providing the AI agent with the human resolutions, which are acquired and characterized by our intelligent interactive conflicts solver, not only improves the agent’s performance but also gives it the capability to suggest more humanlike resolutions. That could help to increase the ATCOs’ acceptance rate of the agent’s suggested resolutions. Our system could be further developed as personalized digital assistants of ATCOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.