Large Language Models in Wargaming: Methodology Application and Robustness

Yuwei Chen, Shiyong Chu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 2894-2903

Traditional artificial intelligence (AI) has contributed strategic enhancements to wargaming but often encounters difficulties in dynamically complex environments and in adapting to unforeseen developments. In contrast Large Language Models (LLMs) offer advanced natural language processing analytical capabilities and intuitive decision-making communication. LLMs excel in rapidly analyzing voluminous textual data identifying patterns and generating insights for strategic planning thereby addressing the critical demand for anticipatory strategy and creative solution development in wargaming. Nonetheless deploying LLMs in this context introduces potential robustness challenges particularly their vulnerability to adversarial prompts. Our experimental investigations reveal LLMs' susceptibility to misleading or hostile inputs underscoring the imperative for implementing robustness measures to safeguard their operational integrity and reliability in strategic applications. Our pioneering research through targeted experiments within a commercial wargaming demonstrates the feasibility and potential of LLMs to significantly improve outcomes in representative scenarios. This work not only evidences the significant impact of LLMs on the decision-making landscape in wargaming but also establishes a foundation for future research and the practical implementation of LLMs in advanced decision support systems.