Research on Hybrid Intelligence Wargame Method

Xin Jin, Xinnian Wang, Ran Ding, Yunchao Wu
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Abstract

Wargame, as a tool to generate sample data for analysis and model training, has vast application in fields of training, command & control, and tactical research. Traditional wargame technologies greatly rely on human wisdom in the loop, impossible to generate large scale sample data. Reinforcement learning technology can generate large scale sample data, but it is not competent for the decision complexity above campaign level. This paper proposes a hybrid intelligence wargame method, which can generate large scale sample data using AI algorithms under the guidance of human wisdom. It has wide applications, which provides data analysis functions that existing wargame methods cannot provide. Prototype software has been developed based on the method, with feasibility and effectiveness verified through experiments, which has certain reference value.
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混合智能战棋方法研究
兵棋推演作为一种生成样本数据进行分析和模型训练的工具,在训练、指挥控制、战术研究等领域有着广泛的应用。传统的兵棋技术在很大程度上依赖于人类的智慧在循环中,不可能产生大规模的样本数据。强化学习技术可以生成大规模的样本数据,但对于战役级别以上的决策复杂性,它的能力不足。本文提出了一种混合智能兵棋演算法,在人类智慧的指导下,利用人工智能算法生成大规模样本数据。它具有广泛的应用,提供了现有兵棋推演方法无法提供的数据分析功能。基于该方法开发了原型软件,并通过实验验证了其可行性和有效性,具有一定的参考价值。
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