Leveraging Large Language Models as an Interface to Conflict Resolution for Human-AI Alignment in Air Traffic Control

Berro Charles, Deligiannaki Fotini, Stefani Thomas, Christensen Johann Maximilian, Gerdes Ingrid, Köster Frank, Hallerbach Sven, Raulf Arne Peter

Abstract:

A steep aircraft increase is forecasted in the near
future, putting additional strain on en-route air traffic control.
To meet the safety and efficiency goals, contemporary research
explores the Single Controller Operations (SCOs) concept to
replace traditional positioning of two Air Traffic Controllers
(ATCOs) per sector. During workshops with ATCOs addressing
SCOs, Conflict Resolution (CR) has been identified as one
important task that can be supported by automation. Although
existing work on CR solvers shows promising results, solvers
based on fixed optimization functions are incompatible with the
dynamic evolving preferences of ATCOs. This work proposes
two additional steps that filter and rank CR solutions based on
a set of rules in natural language—forming a flexible policy—to
better align with ATCO preferences in automation-supported CR.
Inspired from related work on LLM-driven agents, an algorithm
using LLMs to filter and sort CR solutions for alignment with
natural language policies is presented. The algorithm is tested on
a synthetic dataset of policies and solutions for several minimal
filtering and sorting scenarios. The experiments show success in
solving the task in most cases and a correct understanding of
the task by the LLM. Nevertheless, the analysis of failure cases
highlights several limitations of LLMs that must be considered
in future research and development of similar systems.

External IDs:doi:10.1109/dasc66011.2025.11257297