Enhanced Location Prediction for Wargaming with Graph Neural Networks and Transformers
Source abstract
Abstract
In modern wargaming, accurately predicting the locations of the opponent units is crucial for effective strategy and decision making. However, situational data provided by tactical wargame systems present significant challenges: high redundancy across consecutive frames and extreme data sparsity, with units occupying only a small fraction of the overall map. Traditional convolutional neural networks (CNNs) struggle to extract meaningful patterns from such data. To address these limitations, we propose an enhanced location prediction neural network (ELP-Net) that integrates graph neural networks (GNNs) and transformers, combining the robust representation learning capabilities of GNNs with the temporal dependency modeling strength of transformers. By capturing complex inter-node relationships, our model effectively reduces the impact of data repetition and sparsity, achieving robust location predictions in dynamic and sparse wargaming environments. Experimental results demonstrate that our approach significantly improves the prediction accuracy (the combined use of both the GNN and transformer modules results in a 6.4% average performance boost), highlighting its potential to advance intelligent decision making in wargaming applications.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Autonomous Vehicle Technology and Safety · Human Pose and Action Recognition · Anomaly Detection Techniques and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Institute of Applied Physics and Computational Mathematics, Beijing 100094, China
Shanghai Zhangjiang Institute of Mathematics, Shanghai 201203, China
National Key Laboratory of Computational Physics, Beijing 100088, China
Funding
National Postdoctoral Researcher Support Program of China · Major Program of National Natural Science Foundation of China · Tianyuan Fund of the National Natural Science Foundation of China · National Key R&D Program of China · Beijing Natural Science Foundation · Department of Science, Technology and Information of the Ministry of Education
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