基于时序图的作战指挥行为知识表示学习方法

doi:10.3969/j.issn.1001-506X.2020.11.14

系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (11): 2520-2528.doi: 10.3969/j.issn.1001-506X.2020.11.14

基于时序图的作战指挥行为知识表示学习方法

王保魁^1,²(), 吴琳²(), 胡晓峰²(), 贺筱媛²(), 郭圣明²()

1. 国防大学研究生院, 北京 100091
2. 国防大学联合作战学院, 北京 100091

收稿日期:2020-03-26 出版日期:2020-11-01 发布日期:2020-11-05
作者简介:王保魁(1982-),男,工程师,博士研究生,主要研究方向为运筹分析与军事智能决策、知识表示学习。E-mail:baokuiwang@outlook.com|吴琳(1974-),男,教授,博士,主要研究方向为战争模拟。E-mail:13601309481@139.com|胡晓峰(1957-),男,教授,博士,主要研究方向为战争模拟。E-mail:xfhu@vip.sina.com|贺筱媛(1964-),女,教授,博士,主要研究方向为战争模拟。E-mail:bingling1922@sina.com|郭圣明(1981-),男,高级工程师,博士,主要研究方向为态势智能认知。E-mail:30706732@qq.com
基金资助:
“十三五”装备预研共用技术(41412030401);国家自然科学基金青年科学基金项目(61703412);中国博士后科学基金(2016M602996)

Operations command behavior knowledge representation learning method based on sequential graph

Baokui WANG^1,²(), Lin WU²(), Xiaofeng HU²(), Xiaoyuan HE²(), Shengming GUO²()

1. Graduate School, National Defense University, Beijing 100091, China
2. Joint Operations College, National Defense University, Beijing 100091, China

Received:2020-03-26 Online:2020-11-01 Published:2020-11-05

摘要/Abstract

摘要：

为深入探索时序作战指挥行为知识的建模方法,实现对具有时序关联特征的指挥员作战指挥行为的有效表征,以兵棋推演作战指令为基础,提出一种基于时序图的作战指挥行为知识表示学习方法,对作战指挥行为进行知识表示学习,并通过作战指挥行为预测任务验证模型的有效性。实验结果表明,提出的方法对于评估指标提升较大,能够有效捕捉想定场景下联合作战指挥员的作战指挥行为时空特征,为时序作战指挥行为知识的表示学习提供了可行范例,为联合作战指挥员的指挥经验抽取和联合作战态势认知提供基础。

关键词: 作战指挥行为, 时序图, 知识表示学习, 图嵌入, 联合作战

Abstract:

In order to explore the modeling method of the sequential operations command behaviors knowledge deeply and effectively obtain the temporal correlation features of operations command behaviors of the commanders, an operations command behaviors knowledge representation learning method is proposed based on the sequential graph of wargaming operational orders. The operations command behaviors are presented by knowledge representation learning, and the model is verified by the task of operations command behaviors prediction. Experimental results show that the evaluating index is increased obviously, and the spatial-temporal features of operations command behaviors by the joint operations commanders are obtained effectively. Our work highlights the sequential joint operations command behavior representation learning and provides a foundation for the command experience extraction of joint operations commanders and joint operations situation cognition.

Key words: operations command behavior, sequential graph, knowledge representation learning, graph embedding, joint operation

中图分类号:

TP391.9

王保魁, 吴琳, 胡晓峰, 贺筱媛, 郭圣明. 基于时序图的作战指挥行为知识表示学习方法[J]. 系统工程与电子技术, 2020, 42(11): 2520-2528.

Baokui WANG, Lin WU, Xiaofeng HU, Xiaoyuan HE, Shengming GUO. Operations command behavior knowledge representation learning method based on sequential graph[J]. Systems Engineering and Electronics, 2020, 42(11): 2520-2528.

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数据集	实体数	关系数	实体属性类型	稀疏度
亚马逊电子产品数据集	300 150	3 740 196	3	0.996
作战指挥行为数据集	26 500	2 106 671	3	0.997

模型	亚马逊电子产品数据集	作战指挥行为数据集
BGE	0.932 7	0.847 3
LINE(1st)	0.955 4	0.850 7
LINE(2nd)	0.866 4	0.874 2
metapath2vec	0.964 5	0.880 1
时序作战指挥行为知识表示学习模型	0.972 5	0.921 2

模型	亚马逊电子产品数据集	作战指挥行为数据集
BGE	0.154 6	0.192 1
LINE(1st)	0.132 0	0.157 3
LINE(2nd)	0.143 2	0.166 5
metapath2vec	0.120 1	0.124 9
时序作战指挥行为知识表示学习模型	0.087 5	0.085 6

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基于时序图的作战指挥行为知识表示学习方法

Operations command behavior knowledge representation learning method based on sequential graph

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