Battlefield situation comprehension is a fundamental part of the operational command decision-making process. In our current information age, the depth and complexity of war have increased exponentially. It is complicated to analyze the situation through human knowledge and experience alone. The development of artificial intelligence technologies such as deep learning provides new technical support for situation comprehension. Firstly, this paper explores the general process of the commander’s situation comprehension and analyzes the problems faced by the intelligent situation comprehension: i.e., the sample set is scarce, and the comprehension is very complicated. Secondly, this paper proposes the overall framework to understand the tactical situation intelligently using a Wargame. In the Wargame, the battlefield situation can be deduced, the military operational rules and empirical knowledge is used to generate the desired results, then using the CNN neural network to do training and reduce the gap between the ideal result and training result. Finally, the application value of this method in the auxiliary decision-making of commanders is pointed out.


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    Titel :

    Intelligent Battlefield Situation Comprehension Method Based On Deep Learning in Wargame


    Beteiligte:
    Zhang, Junfeng (Autor:in) / Xue, Qing (Autor:in) / Chen, Qihua (Autor:in) / Zhang, Yang (Autor:in) / Ding, Ping (Autor:in) / Deng, Qing (Autor:in)


    Erscheinungsdatum :

    2019-10-01


    Format / Umfang :

    211826 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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