A novel deep cognitive network for battlefield situation awareness in wargaming

Abstract

As an innovative approach to supporting wargaming, computer-based wargames have been well received by military researchers. The battlefield situation in wargames is complex and rapidly evolving, and analysing a single scenario is insufficient to capture the full scope of the battlefield. To address the challenge of identifying trends in situational changes, this study proposes a value network model for battlefield situational awareness in wargaming based on deep learning techniques. Focusing on the Army Tactical Wargame as the research object, this study analyses key elements of battlefield situations using feature engineering methods. It introduces a hierarchical, grid-based model for representing battlefield situation features within wargames and develops a value tagging system that integrates system scores with distance-based rewards. A convolutional neural network-based value network model for situational awareness is then constructed, and the influence of key battlefield characteristics on the model is examined. Experimental results demonstrate that the proposed value network can more accurately predict the situation value at each stage of the wargame. The prediction accuracy exhibits a hump-shaped trend from the beginning to the end of the simulation. During the attack phase, the prediction accuracy exceeds 70 %, reaching a peak of 72.98 %. These findings offer a reliable new method for supporting agents in situation recognition and intelligent decision-making.