The automatic generation of realistic behaviour such as tactical intercepts for Unmanned Aerial Vehicles (UAV) in air combat is a challenging problem. State-of-the-art solutions propose hand–crafted algorithms and heuristics whose performance depends heavily on the initial conditions and aerodynamic properties of the UAVs involved. This paper shows how to employ domain–independent planners, embedded into professional multi–agent simulations, to implement two–level Model Predictive Control (MPC) hybrid control systems for simulated UAVs. We compare the performance of controllers using planners with others based on behaviour trees that implement real world tactics. Our results indicate that hybrid planners derive novel and effective tactics from first principles inherent to the dynamical constraints UAVs are subject to.