Autonomous Planning and Processing Framework for Complex Tasks Based on Large Language Models
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摘要: 随着深度学习和自然语言处理技术的进步, 大语言模型(Large language models, LLMs)展现出巨大潜力. 尽管如此, 它们在处理复杂任务时仍存在局限性, 特别是在任务需要结合规划及外部工具调用的场合. 面向这一挑战, 提出国内首个以军事游戏为背景的中文的复杂任务规划与执行数据集(Complex task planning and execution dataset, CTPaE), 以及一个基于LLMs的自主复杂任务规划 (Complex task planning, CTP) 处理框架AutoPlan. 该框架可以对复杂任务进行自主规划得到元任务序列, 并使用递进式ReAct提示 (Progressive ReAct prompting,PRP) 方法对已规划的元任务逐步执行. 该框架的有效性通过在CTPaE上的实验及与其他经典算法的比较分析得到了验证. 项目地址:
https://github.com/LDLINGLINGLING/AutoPlan .Abstract: With the advancement of deep learning and natural language processing technologies, large language models (LLMs) have shown significant potential. Despite their power, they still face limitations when dealing with complex tasks, especially when the tasks require integrative planning and the invocation of external tools. In response to this challenge, this paper proposes the first domestic dataset for complex task planning and execution with a military game context, the Chinese complex task planning and execution dataset (CTPaE), and a new framework for autonomous complex task planning (CTP) using LLMs named AutoPlan. The framework is capable of autonomously planning complex tasks to generate a sequence of meta-tasks, and employs a progressive ReAct prompting (PRP) method to gradually execute the planned meta-tasks. The effectiveness of the framework has been validated through experiments on the CTPaE and comparative analysis with other classic algorithms. The link of project:https://github.com/LDLINGLINGLING/AutoPlan .-
Key words:
- Large language models (LLMs) /
- tool-use /
- multi-hop reasoning /
- deep learning
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图 1 复杂任务处理框架AutoPlan示意图
Fig. 1 Diagram of AutoPlan framework for complex task processing
图 2 元任务之间的逻辑关系示意图
Fig. 2 Diagram illustrating the logical relationships between meta-tasks
表 1 元任务的属性
Table 1 Properties of meta-tasks
任务属性 符号表示 属性描述 所在位置 $ s_{i} $ 在序列中的逻辑关系 工具需求 $ a_{i} $ 执行该任务的工具需求 参数配置 $ p_{i} $ 调用工具时的参数配置 运行结果 $ r_{i} $ 该任务的运行结果 
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表 2 CTPaE涉及的工具名称和功能介绍
Table 2 The name and function introduction of the tools involved in the CTPaE
工具名称 功能 google_search 通用搜索引擎, 可访问互联网、查询信息等 military_information_search 军事搜索引擎, 可访问军事内部网络、查询情报等 address_book 获取如电话、邮箱、地址等个人信息 email 发送和接收邮件 image_gen 根据输入的文本生成图像 situation_display 输入目标位置坐标和显示范围、当前敌我双方的战场态势图像, 并生成图片 calendar 获取当前时间和日期 map_search 可以查询地图上所有单位位置信息的工具, 返回所有敌军的位置信息 knowledge_graph 通过武器装备知识图谱获取各类武器装备的信息 math_formulation 可以通过Python的eval(·)函数计算出输入的字符串表达式结果并返回 weapon_launch 武器发射按钮是可以启动指定武器打击指定目标位置的工具 distance_calculation 可以计算给定目标单位之间的距离
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表 3 与相关方法在CTPaE上的性能比较
Table 3 Performance comparison with related methods on the CTPaE
方法 规模 (B) 评价指标(%) TSR TCR PT ST ReAct 1.8 7.99 30.30 39.23 34.50 14 37.37 90.00 60.57 48.99 72 39.24 76.40 68.33 60.04 TPTU 1.8 0.60 18.80 33.07 24.92 14 36.13 87.30 60.19 48.30 72 39.84 76.80 68.14 59.96 AutoPlan 1.8 18.70 45.70 91.11 48.15 14 52.30 94.70 90.81 79.24 72 87.02 99.90 99.34 97.09
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表 4 不同任务规划方法性能比较
Table 4 Performance comparison of different task planning methods
方法 规模 (B) 评价指标(%) TSR TCR PT ST 不进行规划 1.8 7.99 30.30 39.23 34.50 14 37.37 90.00 60.57 48.99 72 39.24 76.40 68.33 60.04 TPTU 1.8 0.60 18.80 33.07 24.92 14 36.13 87.30 60.19 48.30 72 39.84 76.80 68.14 59.96 CTP 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 人工规划 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
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表 5 不同任务执行策略性能比较
Table 5 Performance comparison of different task execution strategies
任务规划方法 任务执行方法 规模 (B) 评价指标(%) TSR TCR PT ST 人工规划 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)
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