面向计算机生成兵力的自然语言交互框架研究

doi:10.16182/j.issn1004731x.joss.24-0921

摘要/Abstract

摘要：

为解决军事模拟训练中现有自然语言交互框架与训练任务匹配度不足，难以支撑受训人员与计算机生成兵力(computer generated forces，CGF)流畅交互的问题，提出了面向CGF的自然语言交互技术框架(natural language interaction for CGF，NLI4CGF)。分析了受训人员与CGF之间的自然语言交互逻辑和功能需求，构建了军事模拟训练场景下的自然语言交互框架，支撑了步兵分队模拟训练原型系统中的语义解析和知识查询任务。实验结果表明：所构建的模型表现良好，能够满足步兵分队训练的需求，显著提升了受训用户体验。

关键词: 军事模拟训练, 计算机生成兵力, 自然语言交互, 自然语言处理, 语义解析, 知识查询

Abstract:

To address the mismatch between existing natural language interaction frameworks and training tasks in simulation-based military training, which limits smooth interaction between trainees and Computer Generated Forces (CGF), this paper proposes a Natural Language Interaction framework for Computer Generated Forces (NLI4CGF). The framework analyzes the logic and functional requirements of natural language interaction between trainees and CGF, and establishes an interaction architecture tailored for military simulation training scenarios. It supports semantic parsing and knowledge query tasks within a prototype system developed for infantry squad simulation training. Experimental results demonstrate that the proposed model performs effectively, meets the requirements of infantry training, and significantly enhances the trainee's interactive experience.

Key words: simulation-based military training, computer generated forces(CGF), natural language interaction, natural language processing, semantic parsing, knowledge querying

中图分类号:

TP391.9

李新梦,许凯,胡越等 . 面向计算机生成兵力的自然语言交互框架研究[J]. 系统仿真学报, 2025, 37(11): 2853-2866.

Li Xinmeng,Xu Kai,Hu Yue,et al . Research on CGF-oriented Natural Language Interaction Framework[J]. Journal of System Simulation, 2025, 37(11): 2853-2866.

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能力	输入	输出	技术路线	难点
作战指令理解	文本指令	结构化指令	语义解析	场景扩展，上下文追踪，未登录词的识别
战术任务执行	文本指令，环境状态	机动动作序列	视觉语言导航	多模态信息的融合、对齐与推理
战术任务执行	文本指令，环境状态	操作动作序列	操作序列生成	多模态信息的融合、对齐与推理
战场信息查询	查询问题，视频/图像	答案实体	视觉/图像问答	多模态信息的融合、对齐与推理
	查询问题，知识图谱	答案实体	知识图谱问答	复杂查询问题的关系推理
	查询问题，关系型数据库	数据库查询结果	数据库自然语言接口	关系数据库多表关联查询问题的解析
	查询问题，文本文档	文本片段	机器阅读理解	多跳问题与多篇文本的理解和推理

模型	SA	SP	F1	JA
DSTReader	98.5	99.1	97.0	88.2
TRADE	98.1	99.0	97.9	86.3
GeeX	99.5	99.4	98.7	91.5

模型	WQA-1H		WQA-2H		WQA-3H
模型	HIT@1	MRR	HIT@1	MRR	HIT@1	MRR
IRN	99.5	98.9	93.4	91.5	89.5	89.0
Embed- KGQA	99.2	98.8	94.1	92.4	90.8	90.4
QA2MN	99.6	99.2	95.2	93.2	92.1	91.3

编号	准确度	便捷性	沉浸感
平均值	10.3/4.7/5	10.6/4/5.3	10.6/4.3/5
1	13/3/4	9/5/6	10/4/6
2	10/6/4	12/3/5	10/5/5
3	8/5/7	11/4/5	12/4/4