Multimodal Threat Evaluation in Simulated Wargaming Environments

Threat evaluation offers significant operational advantages in military and non-military contexts by reducing risks to personnel and enhancing the situational awareness. The accurate evaluation of threats, requires a comprehensive analysis of the behaviors of the agents. This paper presents a supervised learning approach to predict the intentions of agents in a simulated military environment, focusing on binary classification to determine whether an agent poses a threat or not. The model integrates multimodal data, including spatial information from a grid-based map and features related to agents, such as velocity and weapon possession. The temporal aspect of agents is considered. However, this yields limited improvements in prediction accuracy. The model is evaluated using self-generated wargaming data, and results show that deep learning approaches leveraging spatial-temporal data outperform traditional methods like Random Forest models, achieving an AUC score of 0.84. The proposed approach demonstrates the potential of using multimodal data fusion for improving threat identification. Future work will focus on expanding the diversity of scenarios and further enhancing the realism of data generation.