Abstract:

In order to improve the efficiency of combat simulation, this paper provided a theoretical reference for the research on the intelligent generation of combat simulation scenarios. It systematically reviewed the intelligent generation methods of combat simulation scenarios based on large language models (LLMs). It began by introducing the basic content of combat simulation scenarios, analyzed the shortcomings of current mainstream scenario generation methods, and discussed how to leverage LLMs to address these issues. Next, it outlined the application paradigms and key supporting technologies for the intelligent generation of combat simulation scenarios based on LLMs. Finally, it pointed out the research prospects of intelligent generation of combat simulation scenarios by considering both the trends in LLMs and the demands of combat simulation.

Key words: large language model, combat simulation scenario, intelligent generation, retrieval-augmented generation, information extraction