Abstract. System-of-Systems Engineering (SoSE) studies how independent constituent systems interact to generate emergent capabilities. However, it is challenging linking the study operations and SoSE to software implementation and agent-based simulations. This paper considers communication as the primary architectural concern and examines how software architectures can improve SoS modelling and simulation. In particular, by relating software architectures to Observe–Orient–Decide–Act (OODA) loops, which are a promising structure for aligning communication, decision logic, and agent interactions with modular and traceable agent architectures. The goal of this work is to explore how communication within SoS architectures can be better structured, represented, and analysed through software engineering and agent-based modelling frameworks in the context of hierarchical and cooperative operational environments, where information exchange directly influences system performance and emergent outcomes.
Background
J. Lovaco· Materials Research Proceedin...· 0 citations
Abstract. Agent-Based Modelling and Simulation (ABMS) is widely used in System-of-Systems (SoS) studies to represent constituent systems operating in dynamic environments. However, the absence of standardised agent architectures hinders scalability, behaviour traceability, and comparative evaluation of emergence, coordination, and operational performance. This work introduces a modular agent design based on Observe–Orient–Decide–Act (OODA) loops as a framework for SoS simulations, enabling transparent information flow and Artificial Intelligence (AI) integration at the decision layer. A wildfire suppression scenario is used for the study, modelling firefighting crews, aircraft, helicopters, bulldozers, and an incident commander as OODA-driven agents. Results show that OODA-based agents exhibit coherent and traceable team behaviours, adaptive task switching, and communication-driven coordination, supporting the study of emergence and interoperability in SoS operations. The Decision stage is designed for future improvements in the form of learning-based AI and Large Language Models to enhance autonomy and operational fidelity.
J. Lovaco· Materials Research Proceedin...· 0 citations
