A Few-shot Intention Recognition Model Based on W-GNN in a Wargame Simulation Environment

XUAN Zizhuo; ZHANG Yongliang; ZHOU Xianzhong; SUN Yuxiang

doi:10.12382/bgxb.2025.0363

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A Few-shot Intention Recognition Model Based on W-GNN in a Wargame Simulation Environment

更新时间：2026-04-24

- A Few-shot Intention Recognition Model Based on W-GNN in a Wargame Simulation Environment
- Acta Armamentarii Vol. 47, Issue 3, Pages: 250363(2026)
- 作者机构：
  
  南京大学工程管理学院，江苏南京 210008
  陆军工程大学江苏南京 210001
  南京大学机器人与自动化学院江苏苏州 215163
- 作者简介：
- 基金信息：
- DOI：10.12382/bgxb.2025.0363
  CLC： TP391.9
- Received：13 May 2025，
  
  Online First：27 January 2026，
  
  Published：2026-03
- 稿件说明：
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XUAN Zizhuo, ZHANG Yongliang, ZHOU Xianzhong, et al. A Few-shot Intention Recognition Model Based on W-GNN in a Wargame Simulation Environment[J]. Acta Armamentarii, 2026, 47(3): 250363.
DOI：

XUAN Zizhuo, ZHANG Yongliang, ZHOU Xianzhong, et al. A Few-shot Intention Recognition Model Based on W-GNN in a Wargame Simulation Environment[J]. Acta Armamentarii, 2026, 47(3): 250363. DOI： 10.12382/bgxb.2025.0363.

摘要

传统意图识别模型通常依赖大规模战斗数据进行建模与训练，但在电子对抗与隐身技术不断发展的背景下，情报信息获取受限，使基于大样本的数据驱动方法面临适应性不足的问题。针对上述挑战，引入小样本学习思想至意图识别研究中，将任务建模为监督式消息传递过程，构建一种融合双向长短期记忆（Bidirectional Long Short-term Memory，BiLSTM）网络与部分可观测图模型的端到端深度学习架构。利用BiLSTM网络从有限兵棋态势信息中提取关键时序特征，刻画动态演化规律，并在此基础上构建加权图结构，通过图卷积实现节点特征更新与关系建模，最终完成意图判别。基于兵棋推演平台，在不同情报完备度条件下开展在线意图识别实验，并对比分析特征提取器配置对识别性能的影响。实验结果表明，在数据稀缺场景下，该模型仍具备良好的识别精度与鲁棒性，整体性能优于多种典型小样本学习模型，体现了其在智能指挥与决策支持中的应用潜力。

Abstract

Traditional intention recognition methods typically rely on large-scale combat data for modeling and training. However

with the continuous development of electronic countermeasures and stealth technologies

the acquisition of intelligence information has been increasingly limited

making the data-driven methods based on large samples difficult to adapt to modern operational environment. To address this challenge

a few-shot learning is introduced into the intention recognition task

which is formulated as a supervised message-passing process. A end-to-end deep learning architecture that integrates a bidirectional long short-term memory（BiLSTM）network with a partially observable graph model is developed. The BiLSTM network is employed to extract the essential temporal features from limited wargaming situation information to capture the dynamic evolution patterns

while suppressing noise and redundant information. A weighted graph structure is constructed based on the extracted features and the observed intention labels

and a graph convolution is applied to model the relational dependencies and iteratively update the node representations

ultimately achieving the intention predictions. Online intention recognition experimentsonline intention recognition experiments are conducted on a wargaming simulation platform under different levels of intelligence completeness

and the influence of feature extractor configuration on recognition performance is analyzed. Experimental results demonstrate that the proposed model maintains robust and accurate intention recognition performance under data-scarce conditions

outperforming several representative few-shot learning models and highlighting its potential applications in intelligent command and decision-support.

关键词

Keywords

references

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