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Optimization of Parameterized Behavior Trees in RTS Games

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Artificial Intelligence and Soft Computing (ICAISC 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13588))

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Abstract

Introduction of Behavior Trees (BTs) impacted the field of Artificial Intelligence (AI) in games, by providing flexible and natural representation of non-player characters (NPCs) logic, manageable by game-designers. Recent trends in the field focused on automatic creation of AI-agents: from deep- and reinforcement-learning techniques to combinatorial (constrained) optimization and evolution of BTs. In this paper, we present a novel approach to semi-automatic construction of AI-agents, that mimic and generalize given human gameplays by adapting and tuning of expert-created BT under a developed similarity metric between source and BT gameplays. To this end, we formulated mixed discrete-continuous optimization problem, in which topological and functional changes of the BT are reflected in numerical variables, and constructed a dedicated hybrid-metaheuristic. The performance of presented approach was verified experimentally in a prototype real-time strategy game. Carried out experiments confirmed efficiency and perspectives of presented approach, which is going to be applied in a commercial game.

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Notes

  1. 1.

    In a performed experiment (not showed here) we obtain 4 Hz sampling rate had no effect on the convergence of the presented algorithm, while it dramatically increased its run-time, due to \(O(n^3)\) complexity of similarity metric. On the other hand, with 1 Hz sampling frequency the algorithm was not able to find a good solution.

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Acknowledgments

The work was financially supported by the National Centre of Research and Development in Poland within GameINN programme under grant no. POIR.01.02.00-00-0108/16.

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Authors and Affiliations

  1. Institute of Computer Science, University of Opole, ul. Oleska 48, 45-052, Opole, Poland

    Tomasz Machalewski, Mariusz Marek & Adrian Ochmann

Authors
  1. Tomasz Machalewski
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  2. Mariusz Marek
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  3. Adrian Ochmann
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Corresponding author

Correspondence to Mariusz Marek .

Editor information

Editors and Affiliations

  1. Systems Research Institute of the Polish Academy of Sciences, Warsaw, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  3. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  4. University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  5. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  6. University of Louisville, Louisville, KY, USA

    Jacek M. Zurada

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Machalewski, T., Marek, M., Ochmann, A. (2023). Optimization of Parameterized Behavior Trees in RTS Games. In: Rutkowski, L., Scherer, R., Korytkowski, M., Pedrycz, W., Tadeusiewicz, R., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2022. Lecture Notes in Computer Science(), vol 13588. Springer, Cham. https://doi.org/10.1007/978-3-031-23492-7_33

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  • DOI: https://doi.org/10.1007/978-3-031-23492-7_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-23491-0

  • Online ISBN: 978-3-031-23492-7

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