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              基于大語言模型的復雜任務自主規劃處理框架

              秦龍 武萬森 劉丹 胡越 尹全軍 陽東升 王飛躍

              秦龍, 武萬森, 劉丹, 胡越, 尹全軍, 陽東升, 王飛躍. 基于大語言模型的復雜任務自主規劃處理框架. 自動化學報, 2024, 50(4): 862?872 doi: 10.16383/j.aas.c240088
              引用本文: 秦龍, 武萬森, 劉丹, 胡越, 尹全軍, 陽東升, 王飛躍. 基于大語言模型的復雜任務自主規劃處理框架. 自動化學報, 2024, 50(4): 862?872 doi: 10.16383/j.aas.c240088
              Qin Long, Wu Wan-Sen, Liu Dan, Hu Yue, Yin Quan-Jun, Yang Dong-Sheng, Wang Fei-Yue. Autonomous planning and processing framework for complex tasks based on large language models. Acta Automatica Sinica, 2024, 50(4): 862?872 doi: 10.16383/j.aas.c240088
              Citation: Qin Long, Wu Wan-Sen, Liu Dan, Hu Yue, Yin Quan-Jun, Yang Dong-Sheng, Wang Fei-Yue. Autonomous planning and processing framework for complex tasks based on large language models. Acta Automatica Sinica, 2024, 50(4): 862?872 doi: 10.16383/j.aas.c240088

              基于大語言模型的復雜任務自主規劃處理框架

              doi: 10.16383/j.aas.c240088
              基金項目: 國家自然科學基金(62103420, 62103425, 62103428, 62306329), 湖南省自然科學基金(2023JJ40676, 2021JJ40697, 2021JJ40702), 國防科技大學青年自主創新基金(ZK-2023-31)資助
              詳細信息
                作者簡介:

                秦龍:國防科技大學系統工程學院副研究員. 2014年獲得國防科技大學博士學位. 主要研究方向為復雜系統建模與仿真. E-mail: qldbx2007@sina.com

                武萬森:國防科技大學系統工程學院博士研究生. 2018年獲得國防科技大學學士學位. 主要研究方向為視覺語言多模態. 本文通信作者. E-mail: wuwansen14@nudt.edu.cn

                劉丹:國防科技大學系統工程學院算法工程師. 主要研究方向為大語言模型, 自然語言處理. E-mail: 15616297890@163.com

                胡越:國防科技大學系統工程學院講師. 2021年獲得國防科技大學博士學位. 主要研究方向為智能啟發式搜索與系統仿真. E-mail: huyue11@nudt.edu.cn

                尹全軍:國防科技大學系統工程學院研究員. 2005年獲得國防科技大學博士學位. 主要研究方向為行為建模, 云仿真. E-mail: yin_quanjun@163.com

                陽東升:暨南大學公共/應急管理學院教授. 主要研究方向為指揮控制理論與方法. E-mail: ydsh_chsh@163.com

                王飛躍:中國科學院自動化研究所復雜系統管理與控制國家重點實驗室研究員. 主要研究方向為智能系統和復雜系統的建模、分析與控制. E-mail: feiyue.wang@ia.ac.cn

              Autonomous Planning and Processing Framework for Complex Tasks Based on Large Language Models

              Funds: Supported by National Natural Science Foundation of China (62103420, 62103425, 62103428, 62306329), Natural Science Foundation of Hunan Province (2023JJ40676, 2021JJ40697, 2021JJ40702), and Youth Independent Innovation Fundation of National University of Defense Technology (ZK-2023-31)
              More Information
                Author Bio:

                QIN Long Associate researcher at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2014. His research interest covers modeling and simulation of complex systems

                WU Wan-Sen Ph.D. candidate at the College of Systems Engineering, National University of Defense Technology. He received his bachelor degree from National University of Defense Technology in 2018. His main research interest is vision-and-language multi-modality. Corresponding author of this paper

                LIU Dan Algorithm engineer at the College of Systems Engineering, National University of Defense Technology. His research interest covers large language models and natural language processing

                HU Yue Lecturer at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2021. His research interest covers intelligent heuristic search and system simulation

                YIN Quan-Jun Researcher at the College of Systems Engineering, National University of Defense Technology. He received his Ph.D. degree from National University of Defense Technology in 2005. His research interest covers behavior modeling and cloud simulation

                YANG Dong-Sheng Professor at the School of Public Management/Emergency Management, Jinan University. His research interest covers theories and methods of command and control

                WANG Fei-Yue Professor at the State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences. His research interest covers modeling, analysis, and control of intelligent systems and complex systems

              • 摘要: 隨著深度學習和自然語言處理技術的進步, 大語言模型(Large language models, LLMs)展現出巨大潛力. 盡管如此, 它們在處理復雜任務時仍存在局限性, 特別是在任務需要結合規劃及外部工具調用的場合. 面向這一挑戰, 提出國內首個以軍事游戲為背景的中文的復雜任務規劃與執行數據集(Complex task planning and execution dataset, CTPaE), 以及一個基于LLMs的自主復雜任務規劃 (Complex task planning, CTP) 處理框架AutoPlan. 該框架可以對復雜任務進行自主規劃得到元任務序列, 并使用遞進式ReAct提示 (Progressive ReAct prompting,PRP) 方法對已規劃的元任務逐步執行. 該框架的有效性通過在CTPaE上的實驗及與其他經典算法的比較分析得到了驗證. 項目地址: https://github.com/LDLINGLINGLING/AutoPlan.
              • 圖  1  復雜任務處理框架AutoPlan示意圖

                Fig.  1  Diagram of AutoPlan framework for complex task processing

                圖  2  元任務之間的邏輯關系示意圖

                Fig.  2  Diagram illustrating the logical relationships between meta-tasks

                圖  3  每條樣本需要調用工具的次數統計

                Fig.  3  Statistics on the number of tools used for each sample

                圖  4  指令長度分析

                Fig.  4  Analysis of instruction length

                圖  5  AutoPlan總體框架示意圖

                Fig.  5  The diagram of the overall framework of AutoPlan

                表  1  元任務的屬性

                Table  1  Properties of meta-tasks

                任務屬性 符號表示 屬性描述
                所在位置 $ s_{i} $ 在序列中的邏輯關系
                工具需求 $ a_{i} $ 執行該任務的工具需求
                參數配置 $ p_{i} $ 調用工具時的參數配置
                運行結果 $ r_{i} $ 該任務的運行結果
                下載: 導出CSV

                表  2  CTPaE涉及的工具名稱和功能介紹

                Table  2  The name and function introduction of the tools involved in the CTPaE

                工具名稱 功能
                google_search 通用搜索引擎, 可訪問互聯網、查詢信息等
                military_information_search 軍事搜索引擎, 可訪問軍事內部網絡、查詢情報等
                address_book 獲取如電話、郵箱、地址等個人信息
                email 發送和接收郵件
                image_gen 根據輸入的文本生成圖像
                situation_display 輸入目標位置坐標和顯示范圍、當前敵我雙方的戰場態勢圖像, 并生成圖片
                calendar 獲取當前時間和日期
                map_search 可以查詢地圖上所有單位位置信息的工具, 返回所有敵軍的位置信息
                knowledge_graph 通過武器裝備知識圖譜獲取各類武器裝備的信息
                math_formulation 可以通過Python的eval(·)函數計算出輸入的字符串表達式結果并返回
                weapon_launch 武器發射按鈕是可以啟動指定武器打擊指定目標位置的工具
                distance_calculation 可以計算給定目標單位之間的距離
                下載: 導出CSV

                表  3  與相關方法在CTPaE上的性能比較

                Table  3  Performance comparison with related methods on the CTPaE

                方法規模 (B)評價指標(%)
                TSRTCRPTST
                ReAct1.87.9930.3039.2334.50
                1437.3790.0060.5748.99
                7239.2476.4068.3360.04
                TPTU1.80.6018.8033.0724.92
                1436.1387.3060.1948.30
                7239.8476.8068.1459.96
                AutoPlan1.818.7045.7091.1148.15
                1452.3094.7090.8179.24
                7287.0299.9099.3497.09
                下載: 導出CSV

                表  4  不同任務規劃方法性能比較

                Table  4  Performance comparison of different task planning methods

                方法規模 (B)評價指標(%)
                TSRTCRPTST
                不進行規劃1.87.9930.3039.2334.50
                1437.3790.0060.5748.99
                7239.2476.4068.3360.04
                TPTU1.80.6018.8033.0724.92
                1436.1387.3060.1948.30
                7239.8476.8068.1459.96
                CTP1.87.2730.2039.2334.50
                1437.5489.9060.6349.02
                7239.4876.9068.0159.82
                人工規劃1.88.1743.3839.2434.50
                1447.7092.0583.5472.21
                7261.6997.6086.7880.75
                下載: 導出CSV

                表  5  不同任務執行策略性能比較

                Table  5  Performance comparison of different task execution strategies

                任務規劃方法 任務執行方法 規模 (B) 評價指標(%)
                TSR TCR PT ST
                人工規劃 ReAct 1.8 8.17 43.38 39.24 34.50
                14 47.70 92.05 83.54 72.21
                72 61.69 97.60 86.78 80.75
                PRP 1.8 18.39 (+10.22) 45.60 (+2.22) 91.15 (+51.91) 48.28 (+13.78)
                14 53.29 (+5.59) 94.70 (+2.65) 91.07 (+7.53) 79.44 (+7.23)
                72 86.43 (+24.74) 99.90 (+2.30) 99.47 (+12.69) 97.89 (+17.14)
                CTP ReAct 1.8 7.27 30.20 39.23 34.50
                14 37.54 89.90 60.63 49.02
                72 39.48 76.90 68.01 59.82
                PRP 1.8 18.70 (+11.43) 45.70 (+15.50) 91.11 (+51.88) 48.15 (+13.65)
                14 52.30 (+14.76) 94.70 (+4.80) 90.81 (+30.18) 79.24 (+30.22)
                72 87.02 (+47.54) 99.90 (+23.00) 99.34 (+31.33) 97.09 (+37.27)
                下載: 導出CSV
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