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Discovering optimal strategy in tactical combat scenarios through the evolution of behaviour trees

Author

Listed:
  • Martin Masek

    (Edith Cowan University)

  • Chiou Peng Lam

    (Edith Cowan University)

  • Luke Kelly

    (Edith Cowan University)

  • Martin Wong

    (Defence Science and Technology Group)

Abstract

In this paper we address the problem of automatically discovering optimal tactics in a combat scenario in which two opposing sides control a number of fighting units. Our approach is based on the evolution of behaviour trees, combined with simulation-based evaluation of solutions to drive the evolution. Our behaviour trees use a small set of possible actions that can be assigned to a combat unit, along with standard behaviour tree constructs and a novel approach for selecting which action from the tree is performed. A set of test scenarios was designed for which an optimal strategy is known from the literature. These scenarios were used to explore and evaluate our approach. The results indicate that it is possible, from the small set of possible unit actions, for a complex strategy to emerge through evolution. Combat units with different capabilities were observed exhibiting coordinated team work and exploiting aspects of the environment.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:annopr:v:320:y:2023:i:2:d:10.1007_s10479-021-04225-7
    DOI: 10.1007/s10479-021-04225-7
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    References listed on IDEAS

    as
    1. 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).
    2. Juan, Angel A. & Faulin, Javier & Grasman, Scott E. & Rabe, Markus & Figueira, Gonçalo, 2015. "A review of simheuristics: Extending metaheuristics to deal with stochastic combinatorial optimization problems," Operations Research Perspectives, Elsevier, vol. 2(C), pages 62-72.
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