15 citations · 21 across the 9 of their papers we have counts for
6 papers · 1 filter
Graph-based Complexity Forecasts in UK En Route Airspace Using Relevant Aircraft Interactions
Edward Henderson, George De Ath, Nick Pepper
Effectively managing Air Traffic Control Officer (ATCO) workload is crucial in maintaining operational safety. Group supervisors use tools that estimate upcoming traffic load to ai…
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…
Air Traffic Controller Task Demand via Graph Neural Networks: An Interpretable Approach to Airspace Complexity
Edward Henderson, Dewi Gould, Richard Everson +2
Real-time assessment of near-term Air Traffic Controller (ATCO) task demand is a critical challenge in an increasingly crowded airspace, as existing complexity metrics often fail t…
How Bayesian Should Bayesian Optimisation Be?
George De Ath, Richard Everson, Jonathan Fieldsend
Bayesian optimisation (BO) uses probabilistic surrogate models - usually Gaussian processes (GPs) - for the optimisation of expensive black-box functions. At each BO iteration, the…
What do you Mean? The Role of the Mean Function in Bayesian Optimisation
George De Ath, Jonathan E. Fieldsend, Richard M. Everson
Bayesian optimisation is a popular approach for optimising expensive black-box functions. The next location to be evaluated is selected via maximising an acquisition function that…
-shotgun: -greedy Batch Bayesian Optimisation
George De Ath, Richard M. Everson, Jonathan E. Fieldsend +1
Bayesian optimisation is a popular, surrogate model-based approach for optimising expensive black-box functions. Given a surrogate model, the next location to expensively evaluate…