【摘要】 针对杀伤网作战体系在复杂战场中存在的数据处理能力和决策速度限制，以及现有AI决策方法数据需求大、泛化能力弱、可解释性低的问题，提出了一种基于知识增强的大语言模型(LLMs)的决策方法 。该方法通过集成环境状态映射、知识增强的LLM决策及决策文本到智能体行为转换三大模块，直接利用LLM理解战场态势和语义以优化决策。结果表明，该方法能有效理解战场态势并制定决策方案，显著降低了对大量数据的依赖，同时保证了良好的可解释性。更多还原

【Abstract】 Aimed at the problems that data processing capability and decision-making speed are limited in kill net combat system in complex battlefields, as well as the problems that requirements of data are large, generalization ability is weak, and interpretability is low in the existing AI decision-making methods, this paper proposes a decision-making method based on knowledge-enhanced Large Language Models(LLMs). The method is to optimize the kill net decisions by directly leveraging LLM to understand the battlefield situation and semantics, through integrating three major modules, i.e. from the environmental state mapping, the knowledge-enhanced LLM decision-making, and the decision text to the agent behavior conversion. The results show that this method can effectively understand battlefield situations and formulate decision schemes, significantly reducing the dependence on large amounts of data while ensuring good interpretability.更多还原