The wargame commodity course of action automated analysis method

IF 1 Q3 ENGINEERING, MULTIDISCIPLINARY
William T. DeBerry, Richard Dill, K. Hopkinson, D. Hodson, M. Grimaila
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引用次数: 3

Abstract

This research presents the wargaming commodity course of action automated analysis method (WCCAAM) – a novel approach to assist wargame commanders in developing and analyzing courses of action (COAs) through semi-automation of the military decision making process (MDMP). MDMP is a seven-step iterative method that commanders and mission partners follow to build an operational course of action to achieve strategic objectives. MDMP requires time, resources, and coordination – all competing items the commander weighs to make the optimal decision. WCCAAM receives the MDMP’s Mission Analysis phase as input, converts the wargame into a directed graph, processes a multi-commodity flow algorithm on the nodes and edges, where the commodities represent units, and the nodes represent blue bases and red threats, and then programmatically processes the MDMP steps to output the recommended COA. To demonstrate its use, a military scenario developed in the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) processes the various factors through WCCAAM and produces an optimal, minimal risk COA.
兵棋商品行动过程自动分析方法
本研究提出了兵棋推演商品行动过程自动化分析方法(WCCAAM)——一种通过军事决策过程(MDMP)的半自动化来帮助兵棋推演指挥官开发和分析行动过程(COAs)的新方法。MDMP是一种七步迭代方法,指挥官和任务伙伴遵循该方法建立实现战略目标的作战行动方针。MDMP需要时间、资源和协调——所有相互竞争的项目都需要指挥官权衡以做出最佳决策。WCCAAM接收MDMP的任务分析阶段作为输入,将兵棋游戏转换为有向图,在节点和边缘上处理多商品流算法,其中商品代表单位,节点代表蓝色基地和红色威胁,然后通过编程处理MDMP步骤以输出推荐的COA。为了演示其用途,在模拟、集成和建模高级框架(AFSIM)中开发的军事场景通过WCCAAM处理各种因素,并产生最佳的、最小风险的COA。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
2.80
自引率
12.50%
发文量
40
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