Large language model empowered decision-making behavior modeling for computer generated force: a survey

CGF (computer generated force) acts as a core component in modern military simulation systems, and the authenticity and intelligence level of its decision-making behavior modeling directly determine the effectiveness of war simulation, tactical training, and operational deduction. Traditional modeling approaches suffer from rigid knowledge representation, scarcity of high-quality training samples, insufficient modeling of complex decision-making processes, and limited behavioral evolution capabilities, which severely restrict the development of intelligent and highly realistic CGF systems. LLMs (large language models), with their superior natural language understanding and generation, knowledge utilization, and few-shot reasoning abilities, provide a new technical paradigm to address these bottlenecks. This paper systematically reviews the research progress of LLM empowered CGF decision-making behavior modeling, which is of great theoretical significance and practical value for promoting the intellectual upgrading of military simulation systems and supporting the research and development of next-generation intelligent CGF.

This paper first identified four core challenges faced by current CGF decision-making behavior modeling: decision knowledge representation, decision complexity modeling, decision behavior evolution, and scarcity of high-quality decision samples. Then, it clarified three enabling paths of LLMs for CGF, including data and knowledge enhancement, decision intelligence generation, and capability iterative evolution. On this basis, a complete LLM based CGF decision-making behavior modeling framework was constructed, which consisted of five key modules: perception, decision-making, action, role, and memory. The technical implementation routes and representative research works of each module were elaborated in detail. The perception module transforms heterogeneous battlefield situation data into standardized semantic information; the decision module generated reasonable and interpretable strategies through military knowledge fusion, structured reasoning, and hierarchical planning; the action module converts high-level strategies into executable instructions constrained by equipment performance and battlefield rules; the role module endows CGF with personalized decision-making characteristics to solve the problem of decision homogeneity; the memory module realized experience accumulation and behavioral evolution through memory modeling, retrieval, and dynamic evolution mechanisms. Finally, this paper summarized potential research directions from five aspects: decision real-time performance, decision quality, decision fidelity, evaluation system, and decision risk control.

LLMs have demonstrated remarkable application potential in CGF decision-making behavior modeling and have become a key technology for constructing evolvable, interactive, and highly realistic intelligent CGF. At present, relevant research is still in the initial exploration stage, and there are still key problems to be solved urgently, such as the mismatch between LLM reasoning delay and tactical real-time requirements, LLM hallucinations affecting decision reliability, the lack of a complete and standardized evaluation system, and decision security risks in military scenarios. Future research should carry out in-depth exploration in lightweight model customization, large and small model collaborative decision-making, human factors embedded modeling, standardized evaluation system construction, and full-process security risk control. Meanwhile, strengthening interdisciplinary integration of artificial intelligence, military psychology, operations research, and other fields will continuously promote the maturity and practical application of LLM empowered CGF technology, and provide strong support for the development of intelligent military simulation.