Contextualised generative AI in system of systems modelling: an approach for firefighting aircraft requirements

Abstract The conceptual design of mission-tailored aircraft is increasingly shifting towards system of systems (SoS) perspectives that account for system interactions using a holistic view. Agent-based modelling and simulation (ABMS) is a common approach for analysing an SoS, but the behaviour of its agents tends to be defined by rigid behaviour trees. The present work aims to evaluate the suitability of a prompt-engineered large language model (LLM) acting as the Incident Commander (IC), replacing the fixed behaviour trees that govern the agents’ decisions. The research contributes by developing a prompting framework for operational guidelines, constraints, and priorities to obtain an LLM commander within a wildfire suppression, SoS capable of replicating human decisions. By enabling agents in a simulation model with decision-making capabilities closer to those expected from humans, the commander’s decisions and potential emergent patterns can be translated into more defined requirements for aircraft conceptual design (ACD) (e.g., endurance, payload, sensors, communications, or turnaround requirements). Results showed that an LLM commander facilitated adaptive and context-aware decisions that can be analysed via decision logs. The results allow designers to derive aircraft requirements for their specific roles from operational outcomes rather than a priori assumptions, linking SoS mission needs and ACD parameters.