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A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics

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  • Lam, Chiou-Peng
  • Masek, Martin
  • Kelly, Luke
  • Papasimeon, Michael
  • Benke, Lyndon

Abstract

The automatic generation of behavioural models for intelligent agents in military simulation and experimentation remains a challenge. Genetic Algorithms are a global optimization approach which is suitable for addressing complex problems where locating the global optimum is a difficult task. Unlike traditional optimisation techniques such as hill-climbing or derivatives-based methods, Genetic Algorithms are robust for addressing highly multi-modal and discontinuous search landscapes. In this paper, we outline a simheuristic GA-based approach for automatic generation of finite state machine based behavioural models of intelligent agents, where the aim is the identification of novel combat tactics. Rather than evolving states, the proposed approach evolves a sequence of transitions. We also discuss workable starting points for the use of Genetic Algorithms for such scenarios, shedding some light on the associated design and implementation difficulties.

Suggested Citation

  • Lam, Chiou-Peng & Masek, Martin & Kelly, Luke & Papasimeon, Michael & Benke, Lyndon, 2019. "A simheuristic approach for evolving agent behaviour in the exploration for novel combat tactics," Operations Research Perspectives, Elsevier, vol. 6(C).
    • Handle: RePEc:eee:oprepe:v:6:y:2019:i:c:s221471601930082x
    DOI: 10.1016/j.orp.2019.100123
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    References listed on IDEAS

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    Cited by:

    1. Angel A. Juan & Peter Keenan & Rafael Martí & Seán McGarraghy & Javier Panadero & Paula Carroll & Diego Oliva, 2023. "A review of the role of heuristics in stochastic optimisation: from metaheuristics to learnheuristics," Annals of Operations Research, Springer, vol. 320(2), pages 831-861, January.
    2. Martin Masek & Chiou Peng Lam & Luke Kelly & Martin Wong, 2023. "Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees," Annals of Operations Research, Springer, vol. 320(2), pages 901-936, January.

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