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Suboptimality of Constrained Action Adversarial Cyber-Physical Games

Author

Listed:
  • Takuma A. Adams

    (The University of Melbourne
    Defence Science and Technology Group)

  • Andrew C. Cullen

    (The University of Melbourne)

  • Tansu Alpcan

    (The University of Melbourne)

Abstract

Analysing complex cyber-physical systems using established game-theoretic tools poses significant challenges due to the nonlinear dynamics inherent to such systems. To address this, we leverage multi-agent reinforcement learning (MARL) to study the impact constrained action spaces have on a player’s ability to uncover optimal strategies in a system governed by adversarial nonlinear dynamics. The system is posed as a dynamic, two-player, zero-sum game with elements of adversarial decision-making and resource competition, making it suitable for a variety of cyber-security, business, and military scenarios. Comparing player strategies over an ensemble of different action spaces suggests that MARL converges to an approximate $$\epsilon $$ ϵ -Nash equilibrium even under constraints. In addition, numerical results reveal agreement between MARL solutions and our theoretical understanding of the problem, offering insight into action space selection for this adversarial game.

Suggested Citation

  • Takuma A. Adams & Andrew C. Cullen & Tansu Alpcan, 2025. "Suboptimality of Constrained Action Adversarial Cyber-Physical Games," Dynamic Games and Applications, Springer, vol. 15(3), pages 769-788, July.
    • Handle: RePEc:spr:dyngam:v:15:y:2025:i:3:d:10.1007_s13235-025-00631-9
    DOI: 10.1007/s13235-025-00631-9
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    References listed on IDEAS

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    1. Kalloniatis, Alexander C. & McLennan-Smith, Timothy A. & Roberts, Dale O., 2020. "Modelling distributed decision-making in Command and Control using stochastic network synchronisation," European Journal of Operational Research, Elsevier, vol. 284(2), pages 588-603.
    2. Anthony Dekker, 2007. "Studying Organisational Topology with Simple Computational Models," Journal of Artificial Societies and Social Simulation, Journal of Artificial Societies and Social Simulation, vol. 10(4), pages 1-6.
    3. Cullen, Andrew C. & Alpcan, Tansu & Kalloniatis, Alexander C., 2022. "Adversarial decisions on complex dynamical systems using game theory," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 594(C).
    4. Steven Ceron & Kevin O’Keeffe & Kirstin Petersen, 2023. "Diverse behaviors in non-uniform chiral and non-chiral swarmalators," Nature Communications, Nature, vol. 14(1), pages 1-13, December.
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