An Intelligent Interactive Conflict Solver Incorporating Air Traffic Controllers' Preferences Using Reinforcement Learning

Abstract

The increasing demand in air transportation is pushing the current air traffic management (ATM) 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. To address this issue, the application of artificial intelligence (AI) in supporting ATCOs is a promising approach. In this work, we build an AI system 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' preferences, and an AI agent based on reinforcement learning to suggest conflict resolutions capturing those preferences. We observe 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 human-like resolutions, which could help increase the ATCOs' acceptance rate of the agent's suggested resolutions. Our system could be further developed as personalized digital assistants of ACTOs to maintain their workloads manageable when they have to deal with sectors with increased traffic.