In order to improve the problem of low efficiency of tactical rules relying on manual summarization in traditional behavior modeling, an intelligent modeling method of air combat behavior based on LLM is proposed. Under our existing air combat simulation framework, the reasoning ability of DeepSeek-R1-Distill-Qwen-7B model is used to output structured coding to support the reading and operation of behavior model. RAG retrieval enhancement generation and thinking chain are used to improve the output effect of the model. In addition, the rerank secondary re-ranking method is introduced in the similarity retrieval stage, and an indicator for evaluating the generation effect of structured html coding text is proposed. The results show that this method has good accuracy when processing a certain amount of situation information, improves the construction efficiency, and the simulation results have verified the effectiveness of the method.

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Intelligent Modeling Method of Air Combat Behavior Based on LLM

  • Zexi Yu,
  • Ni Li,
  • Guanghong Gong,
  • Yang Liu

摘要

In order to improve the problem of low efficiency of tactical rules relying on manual summarization in traditional behavior modeling, an intelligent modeling method of air combat behavior based on LLM is proposed. Under our existing air combat simulation framework, the reasoning ability of DeepSeek-R1-Distill-Qwen-7B model is used to output structured coding to support the reading and operation of behavior model. RAG retrieval enhancement generation and thinking chain are used to improve the output effect of the model. In addition, the rerank secondary re-ranking method is introduced in the similarity retrieval stage, and an indicator for evaluating the generation effect of structured html coding text is proposed. The results show that this method has good accuracy when processing a certain amount of situation information, improves the construction efficiency, and the simulation results have verified the effectiveness of the method.