Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (1): 80-86.doi: 10.11896/jsjkx.240900075

• Technology Research and Application of Large Language Model • Previous Articles     Next Articles

COA Generation Based on Pre-trained Large Language Models

YAN Yusong1, ZHOU Yuan2, WANG Cong2, KONG Shengqi1, WANG Quan2, LI Minne2, WANG Zhiyuan2   

  1. 1 College of Computer,National University of Defense Technology,Changsha 410005,China
    2 Intelligent Game and Decision Lab,Beijing 100000,China
  • Received:2024-09-12 Revised:2024-10-14 Online:2025-01-15 Published:2025-01-09
  • About author:YAN Yusong,born in 2001,Ph.D candidate.His main research interests include reinforcement and intelligent decision and so on.
    ZHOU Yuan,born in 1993,Ph.D,assistant researcher.Her main research interests include machine learning and intelligent decision.
  • Supported by:
    Young Scientists Fund of the National Natural Science Foundation of China(62102442) and National Natural Science Foundation of China(62402500).

PDF (PC)

273

Abstract: Focusing on empowering the command and control(C2) procedure of generative AI,we analyze the challenges of course of action(COA) generation in C2 and the prospects of pre-trained large language models(LLMs).Then,a COA generation me-thod based on pre-trained LLMs,COA-Gen,is proposed.Firstly,a multi-round generation framework is designed to align the generated plans with objectives.Secondly,a multi-factor prompt templates is constructed to integrate vast amounts of multi-source information.Lastly,knowledge-augmented generation technology is introduced to improve the generation quality of the few-shot military domain.To validate the effectiveness of the generated plans,an emulation environment based on the StarCraft II engine and the “Tiger Claw” scenario is established.The results show the robustness of the method and its alignment with the commander’s intention.The feasibility of using LLMs for COA generation has been verified.Additionally,different pre-trained models exhibit varying performances in the same task,indicating that the choice of model in real-world applications can lead to action plans with different styles,thereby affect the ultimate outcomes.

Key words: Large language model, Generative AI, Intelligent decision-making, Command and control, Course of action

CLC Number: 

  • TP399
[1]ZHANG Y X.Research on Modeling and Optimization Methods for Military Mission Planning under Uncertainty[D].Changsha:National University of Defense Technology,2014.
[2]WAYTOWICH N,HARE J,GOECKS V G,et al.Learning to guide multiple heterogeneous actors from a single human de-monstration via automatic curriculum learning in StarCraft II[C]//Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV.SPIE,2022,12113:283-293.
[3]GOODFELLOW I,POUGET-ABADIE J,MIRZA M,et al.Ge-nerative adversarial networks[J].Communications of the ACM,2020,63(11):139-144.
[4]KINGMA D P,WELLING M.Auto-encoding variational bayes[J].arXiv:1312.6114,2013.
[5]BROWN T,MANN B,RYDER N,et al.Language models are few-shot learners[J].Advances in Neural Information Proces-sing Systems,2020,33:1877-1901.
[6]BAI J,BAI S,CHU Y,et al.Qwen technical report[J].arXiv:2309.16609,2023.
[7]TOUVRON H,LAVRIL T,IZACARD G,et al.Llama:Openand efficient foundation language models[J].arXiv:2302.13971,2023.
[8]ZENG A,LIU X,DU Z,et al.Glm-130b:An open bilingual pre-trained model[J].arXiv:2210.02414,2022.
[9]WEI J,WANG X,SCHUURMANS D,et al.Chain-of-thoughtprompting elicits reasoning in large language models[J].Advances in Neural Information Processing Systems,2022,35:24824-24837.
[10]SHINN N,CASSANO F,GOPINATH A,et al.Reflexion:Language agents with verbal reinforcement learning[J].Advances in Neural Information Processing Systems,2024,36:8634-8652.
[11]HUANG Y,HUANG J.A Survey on Retrieval-AugmentedText Generation for Large Language Models[J].arXiv:2404.10981,2024.
[12]FIKES R E,NILSSON N J.STRIPS:A new approach to the application of theorem proving to problem solving[J].Artificial Intelligence,1971,2:189-208.
[13]TATE A,DRABBLE B,DALTON J.The use of condition types to restrict search in an AI planner[C]//Proceedings of the AAAI Conference on Artificial Intelligence.AAAI,1994:1129-1134.
[14]SARCIA S A.Organizing Structures and Information for Deve-loping AI-enabled Military Decision-Making Systems[C]//2023 IEEE International Workshop on Technologies for Defense and Security(TechDefense).IEEE,2023:455-460.
[15]SCHWARTZ P J,O’NEILL D V,BENTZ M E,et al.AI-enabled wargaming in the military decision making process[C]//Artificial Intelligence And Machine Learning for Multi-Domain Operations Applications II.SPIE,2020,11413:118-134.
[16]LUO J Z,SUN Y L,QIAN Z Z,et al.Overview and Prospect of Artificial Intelligence Large Models[J].Radio Engineering,2023,53(11):2461-2472.
[17]BAI J,BAI S,YANG S,et al.Qwen-vl:A versatile vision-lan-guage model for understanding,localization,text reading,and beyond[J].arXiv:2308.12966,2023.
[18]WANG G,XIE Y,JIANG Y,et al.Voyager:An open-ended embodied agent with large language models[J].arXiv:2305.16291,2023.
[19]AHN M,BROHAN A,BROWN N,et al.Do as I can,not as I say:Grounding language in robotic affordances[J].arXiv:2204.01691,2022.
[20]LAMPARTH M,CORSO A,GANZ J,et al.Human vs.ma-chine:Language models and wargames[J].arXiv:2403.03407,2024.
[21]GOECKS V G,WAYTOWICH N.Coa-gpt:Generative pre-trained transformers for accelerated course of action development in military operations[C]//2024 International Conference on Military Communication and Information Systems.IEEE,2024:1-10.
[22]HU S,HUANG T,LIU L.Pok\′eLLMon:A Human-Parity Agent for Pok\′emon Battles with Large Language Models[J].arXiv:2402.01118,2024.
[23]MNIH V.Asynchronous Methods for Deep ReinforcementLearning[J].arXiv:1602.01783,2016.
[1] WANG Yanning, ZHANG Fengdi, XIAO Dengmin, SUN Zhongqi. Multi-agent Pursuit Decision-making Method Based on Hybrid Imitation Learning [J]. Computer Science, 2025, 52(1): 323-330.
[2] ZENG Zefan, HU Xingchen, CHENG Qing, SI Yuehang, LIU Zhong. Survey of Research on Knowledge Graph Based on Pre-trained Language Models [J]. Computer Science, 2025, 52(1): 1-33.
[3] DUN Jingbo, LI Zhuo. Survey on Transmission Optimization Technologies for Federated Large Language Model Training [J]. Computer Science, 2025, 52(1): 42-55.
[4] ZHENG Mingqi, CHEN Xiaohui, LIU Bing, ZHANG Bing, ZHANG Ran. Survey of Chain-of-Thought Generation and Enhancement Methods in Prompt Learning [J]. Computer Science, 2025, 52(1): 56-64.
[5] LI Jiahui, ZHANG Mengmeng, CHEN Honghui. Large Language Models Driven Framework for Multi-agent Military Requirement Generation [J]. Computer Science, 2025, 52(1): 65-71.
[6] LI Tingting, WANG Qi, WANG Jiakang, XU Yongjun. SWARM-LLM:An Unmanned Swarm Task Planning System Based on Large Language Models [J]. Computer Science, 2025, 52(1): 72-79.
[7] CHENG Zhiyu, CHEN Xinglin, WANG Jing, ZHOU Zhongyuan, ZHANG Zhizheng. Retrieval-augmented Generative Intelligence Question Answering Technology Based on Knowledge Graph [J]. Computer Science, 2025, 52(1): 87-93.
[8] LIU Changcheng, SANG Lei, LI Wei, ZHANG Yiwen. Large Language Model Driven Multi-relational Knowledge Graph Completion Method [J]. Computer Science, 2025, 52(1): 94-101.
[9] WANG Xuxian, HUANG Jinhua, ZHAI You, LI Chu’nan, WANG Yu, ZHANG Yupeng, ZHANG Yipeng, YANG Liqun, LI Zhoujun. Survey of Detection Techniques for Domain Generation Algorithm [J]. Computer Science, 2024, 51(8): 371-378.
[10] LIU Yumeng, ZHAO Yijing, WANG Bicong, WANG Chao, ZHANG Baomin. Advances in SQL Intelligent Synthesis Technology [J]. Computer Science, 2024, 51(7): 40-48.
[11] LI Zhanqi, WU Xinwei, ZHANG Lei, LIU Quanzhou, XIE Hui, XIONG Deyi. Automatic Test Case Generation Method for Automotive Electronic Control System Verification [J]. Computer Science, 2024, 51(12): 63-70.
[12] BAI Jianghao, PIAO Yong. Ensemble Learning Based Open Source License Detection and Compatibility Assessment [J]. Computer Science, 2024, 51(12): 79-86.
[13] ZHU Yangfu, LI Meiling, TAN Jiachen, WU Bin. Study on Text-based Personality Detection－A Review [J]. Computer Science, 2024, 51(12): 209-222.
[14] MA Qimin, LI Xiangmin, ZHOU Yaqian. Large Language Model-based Method for Mobile App Accessibility Enhancement [J]. Computer Science, 2024, 51(12): 223-233.
[15] ZHANG Jinying, WANG Tiankun, YAO Changying, XIE Hua, CHAI Linzheng, LIU Shukai, LI Tongliang, LI Zhoujun. Construction and Evaluation of Intelligent Question Answering System for Electric Power Knowledge Base Based on Large Language Model [J]. Computer Science, 2024, 51(12): 286-292.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.