3 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
Cognitively Constrained Partners for Zero-Shot Coordination in Real-Time Partitioned Control
Ash Simmons, George De Ath, Richard M. Everson
Effective human-machine teaming requires agents that coordinate with unpredictable human partners. Reinforcement learning from live human feedback or large behavioural datasets is…
Human-in-the-Loop Testing of AI Agents for Air Traffic Control with a Regulated Assessment Framework
Ben Carvell, Marc Thomas, Andrew Pace +7
We present a rigorous, human-in-the-loop evaluation framework for assessing the performance of AI agents on the task of Air Traffic Control, grounded in a regulator-certified simul…
Online Action-Stacking Improves Reinforcement Learning Performance for Air Traffic Control
Ben Carvell, George De Ath, Eseoghene Benjamin +1
We introduce online action-stacking, an inference-time wrapper for reinforcement learning policies that produces realistic air traffic control commands while allowing training on a…
A Future Capabilities Agent for Tactical Air Traffic Control
Paul Kent, George De Ath, Martin Layton +3
Escalating air traffic demand is driving the adoption of automation to support air traffic controllers, but existing approaches face a trade-off between safety assurance and interp…
A Probabilistic Digital Twin of UK En Route Airspace for Training and Evaluating AI Agents for Air Traffic Control
Nick Pepper, Adam Keane, Amy Hodgkin +11
This paper presents the first probabilistic Digital Twin of operational en route airspace, developed for the London Area Control Centre. The Digital Twin is intended to support the…