Abstract:

To address the problems of great difficulty in intelligent decision-making and insufficient dynamism in task planning caused by the complex adversarial environment and strong uncertainty in wargaming tasks, this paper proposed a hierarchical Agent collaborative decision-making framework based on large and small model synergy. Through a multi-level structure, the hierarchical decoupling and dynamic coordination of battlefield tasks were achieved. A memory management module was constructed, and a query optimization mechanism driven by large language models was introduced to dynamically perceive the decision-making process and query intent, completing the semantic reconstruction and context completion of raw queries. A time-driven two-stage task planning process was designed to achieve global task planning formulation, original task evaluation, and dynamic task update, respectively. Experimental results indicate that the DeepSeek-V3-centered decision-making model exhibits good task planning capability and instruction-following capability under this framework.

Key words: large language model, retrieval-augmented generation, intelligent decision-making, task planning, strategy generation, wargaming