A Future Capabilities Agent for Tactical Air Traffic Control
🤖 AI Summary
This work proposes Agent Mallard, a rule-based forward-planning agent designed to address the challenge of ensuring both safety and interpretability in air traffic management under uncertainty. Mallard innovatively integrates stochastic digital twins into a rule-driven conflict resolution loop, combining causal attribution, topological plan stitching, and monotonic axis constraints to enable safe, verifiable, hierarchical tactical decision-making. The system employs depth-limited backtracking search, a conflict-resolution strategy library informed by expert knowledge, and discretized route selection, while incorporating safety verification mechanisms tailored to uncertain scenarios such as wind shifts and communication outages. Preliminary experiments demonstrate that Mallard’s behavior aligns with expert reasoning, efficiently resolving flight conflicts in simplified airspace while maintaining strong guarantees of safety, interpretability, and computational tractability.
Application Category
📝 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.
Problem
Research questions and friction points this paper is trying to address.
air traffic control
safety assurance
interpretability
conflict resolution
automation
Innovation
Methods, ideas, or system contributions that make the work stand out.
stochastic digital twin
rules-based agent
conflict resolution
interpretable AI
tactical air traffic control
🔎 Similar Papers
No similar papers found.
