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.

翻译：日益增长的空中交通需求正推动自动化技术应用于支持空中交通管制员，但现有方法面临安全保证与可解释性之间的权衡。基于优化的方法（如强化学习）虽能提供优异性能，却难以验证和解释；而基于规则的系统虽具备透明度，却很少在不确定性条件下验证安全性。本文提出Agent Mallard——一种面向体系化空域战术控制的前向规划、基于规则的智能体，它将随机数字孪生直接嵌入冲突消解循环中。Mallard基于预定义的GPS引导航路运行，将连续的4D矢量控制简化为对航路层级与飞行高度的离散选择，并通过专家知识构建的冲突解脱策略库生成分层规划方案。该智能体采用深度受限的回溯搜索算法，结合因果归因、拓扑规划拼接与单调轴约束，为所有航空器寻找完整的安全规划方案，并在执行前针对不确定的运行场景（如风场变化、飞行员响应、通信中断）验证每个候选机动动作。通过与英国管制员的初步走查以及在BluebirdDT空域数字孪生环境中的初始测试表明，Mallard的行为模式与专家推理逻辑相符，并能在简化场景中有效解决冲突。该架构旨在未来结构化航路环境中，实现基于模型的安全评估、可解释的决策逻辑与可处理的计算性能三者结合。