Journal of System Simulation
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-V 3-centered decision-making model exhibits good task planning capability and instruction-following capability under this framework.
Recommended Citation
Liu, Yingang; Ma, Ming; and Zhang, Ronghua
(2026)
"Dynamic Task Planning for Wargaming Based on Large Language Models,"
Journal of System Simulation: Vol. 38:
Iss.
5, Article 5.
DOI: 10.16182/j.issn1004731x.joss.25-1096
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol38/iss5/5
First Page
1187
Last Page
1204
CLC
TP391.9
Recommended Citation
Liu Yingang, Ma Ming, Zhang Ronghua. Dynamic Task Planning for Wargaming Based on Large Language Models[J]. Journal of System Simulation, 2026, 38(5): 1187-1204.
DOI
10.16182/j.issn1004731x.joss.25-1096
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