You're Not Alone in Battle: Combat Threat Analysis Using Attention Networks and a New Open Benchmark

Soo Yong Lee, Juwon Kim, Kiwoong Park, Dong Kuk Ryu, Sang Heun Shim, Kijung Shin

Abstract: For military commands, combat threat analysis is crucial in predicting future outcomes and informing consequent decisions. Its primary objectives include determining the intention and attack likelihood of the hostiles. The complex, dynamic, and noisy nature of combat, however, presents significant challenges in its analysis. The prior research has been limited in accounting for such characteristics, assuming independence of each entity, no unobserved tactics, and clean combat data. As such, we present spatio-temporal attention for threat analysis (SAFETY) to encode complex interactions that arise within combat. We test the model performance for unobserved tactics and with various perturbations. To do so, we also present the first open-source benchmark for combat threat analysis with two downstream tasks of predicting entity intention and attack probability. Our experiments show that SAFETY achieves a significant improvement in model performance, with enhancements of up to 13% in intention prediction and 7% in attack prediction compared to the strongest competitor, even when confronted with noisy or missing data. This result highlights the importance of encoding dynamic interactions among entities for combat threat analysis. Our codes and dataset are available at https://github.com/syleeheal/SAFETY.