A Future Capabilities Agent for Tactical Air Traffic Control

TL;DR

Agent Mallard is outlined, a forward-planning, rules-based agent for tactical control in systemised airspace that embeds a stochastic digital twin directly into its conflict-resolution loop and is intended to combine model-based safety assessment, interpretable decision logic, and tractable computational performance in future structured en-route environments.

Abstract

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 interpretability. Optimisation-based methods such as reinforcement learning offer strong performance but are difficult to verify and explain, while rules-based systems are transparent yet rarely check safety under uncertainty. This paper outlines Agent Mallard, a forward-planning, rules-based agent for tactical control in systemised airspace that embeds a stochastic digital twin directly into its conflict-resolution loop. Mallard operates on predefined GPS-guided routes, reducing continuous 4D vectoring to discrete choices over lanes and levels, and constructs hierarchical plans from an expert-informed library of deconfliction strategies. A depth-limited backtracking search uses causal attribution, topological plan splicing, and monotonic axis constraints to seek a complete safe plan for all aircraft, validating each candidate manoeuvre against uncertain execution scenarios (e.g., wind variation, pilot response, communication loss) before commitment. Preliminary walkthroughs with UK controllers and initial tests in the BluebirdDT airspace digital twin indicate that Mallard's behaviour aligns with expert reasoning and resolves conflicts in simplified scenarios. The architecture is intended to combine model-based safety assessment, interpretable decision logic, and tractable computational performance in future structured en-route environments.

Figures (8)

• Figure 1: Agent Mallard's operational flowchart. Blue: initialisation (airspace state received, plans generated if needed). Yellow: evaluation (plan simulated on the digital twin). Red: deconfliction (if unsafe, Backtracking search looks for a safe alternative; see Sec. \ref{['sec:backtracking']}). Green: execution (if safe, conditions checked and actions issued). If conditions are never met the aircraft would miss its coordination, be deemed unsafe, and planned again.
• Figure 2: A set of deconflicted lanes for a training sector. The Right and Left lanes for each route are geometrically deconflicted from each other.
• Figure 3: Example visualisation of Agent Mallard's initial flight plans within the BluebirdDT digital twin interface, showing intended routes based on systemised airspace principles.
• Figure 4: Comprehensive Conflict Classification Taxonomy. The agent categorises interactions across three dimensions: (A) Vertical State (Blue/Side View), (B) Lateral Geometry (Black/Plan View), and (C) Speed Differential (Red/Kinetic View). This multidimensional classification enables precise selection of deconfliction strategies.
• Figure 5: (a) Predicted conflict (red 'X') between blue and green aircraft. (b) Tactical vectoring introduces non-standard paths and secondary conflicts (e.g., with yellow aircraft). (c) 'Dual lane change' shifts both aircraft to predefined parallel lanes, maintaining predictable separation. See main text for discussion.