Modular OODA-Based Agents for Scalable and Adaptive AI in System-of-Systems Modelling and Simulation
Jorge L. LOVACO, Karl ZUBER, Raghu Chaitanya MUNJULURY, Christopher JOUANNET, Petter KRUS
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.
Keywords
System-of-Systems, Agent-Based Modelling and Simulation, OODA Loop, Wildfire Suppression, Collaborative AI
Published online 7/20/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: Jorge L. LOVACO, Karl ZUBER, Raghu Chaitanya MUNJULURY, Christopher JOUANNET, Petter KRUS, Modular OODA-Based Agents for Scalable and Adaptive AI in System-of-Systems Modelling and Simulation, Materials Research Proceedings, Vol. 69, pp 88-93, 2026
DOI: https://doi.org/10.21741/9781644904251-16
The article was published as article 16 of the book CEAS – AIDAA Conference 2025
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
References
[1] Brusa, E., Cal’a, A., & Ferretto, D. (2018). Systems engineering and its application to industrial product development. Cham: Springer International Publishing. https://doi.org/10.1007/978-3-319-71837-8
[2] DeLaurentis, D. A., Moolchandani, K., & Guariniello, C. (2022). System of systems modeling and analysis. CRC Press. https://doi.org/10.1201/9781003231011
[3] Murphy, K. J., Ciuti, S., & Kane, A. (2020). An introduction to agent-based models as an accessible surrogate to field-based research and teaching. Ecology and evolution, 10(22), 12482-12498. https://doi.org/10.1002/ece3.6848
[4] Enck, R. E. (2012). The OODA loop. Home Health Care Management & Practice, 24(3), 123-124. https://doi.org/10.1177/1084822312439314
[5] Russell, S. J., Norvig, P. (2018). Artificial intelligence a modern approach (4th ed.). Boston: Pearson.
[6] Silvander, J., & Angelin, L. (2019). Introducing intents to the OODA-loop. Procedia Computer Science, 159, 878-883. https://doi.org/10.1016/j.procs.2019.09.247
