Optimization and simulation prediction of war strategies based on Markov networks and BP neural networks

Zihan Zhang; Kunpeng Chen; Bin Liu; Zhongyang Liu

doi:10.1117/12.2681633

23 May 2023 Optimization and simulation prediction of war strategies based on Markov networks and BP neural networks

Zihan Zhang, Kunpeng Chen, Bin Liu, Zhongyang Liu

Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 1264534 (2023) https://doi.org/10.1117/12.2681633
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China

Abstract

Both sides in contemporary warfare must thoroughly consider their strategies and implement effective tactics to increase their offensive or defensive capabilities. It is critical to emphasize the strengths and weaknesses of one’s own side to establish a balanced and stable situation on the battlefield. This paper presented a suggested method that employed Markov networks to define the weight of nodes in a map, resulting in an optimal predetermined layout. Furthermore, we proposed adopting BP neural network algorithms to give the army autonomy in decision-making. That enabled each unit to develop its own decision-making process and enhance the overall war strategy via repeated simulations.

Citation Download Citation

Zihan Zhang, Kunpeng Chen, Bin Liu, and Zhongyang Liu "Optimization and simulation prediction of war strategies based on Markov networks and BP neural networks", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 1264534 (23 May 2023); https://doi.org/10.1117/12.2681633

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