Abstract
This paper considers aspects of applying machine learning methods to existing ways of modeling intelligent agent behavior. Such a goal is considered to enable agents to improve their performance in competitive models. An overview of existing machine learning methods is given. Ways of modeling the behavior of agents are considered. The most advantageous combination of machine learning and behavioral modeling approaches is identified. The advantages and disadvantages of existing methods are considered. The intelligent agent models are implemented based on behavioral trees with the introduction of reinforcement learning. A test platform with an integrated agent competition model is implemented. The ability of the developed intelligent agent behavior model to win in competition with agents equipped with different variants of traditional tree-based behaviors has been tested on the basis of the developed platform. The workability and benefits of using the developed behavioral model were analyzed in relation to the potential of the chosen combination of techniques.
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Acknowledgments
The study has been supported by the grant from the Russian Science Foundation (RSF) and the Administration of the Volgograd Oblast (Russia) No. 22-11-20024, https://rscf.ru/en/project/22-11-20024/. The authors express gratitude to colleagues from the Department of Digital Technologies for Urban Studies, Architecture and Civil Engineering, VSTU involved in the development of the project.
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Anokhin, A., Ereshchenko, T., Parygin, D., Khoroshun, D., Kalyagina, P. (2023). Applying Machine Learning and Agent Behavior Trees to Model Social Competition. In: Kabassi, K., Mylonas, P., Caro, J. (eds) Novel & Intelligent Digital Systems: Proceedings of the 3rd International Conference (NiDS 2023). NiDS 2023. Lecture Notes in Networks and Systems, vol 784. Springer, Cham. https://doi.org/10.1007/978-3-031-44146-2_26
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