Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (3): 248-259.doi: 10.11896/jsjkx.241100068

• Artificial Intelligence • Previous Articles     Next Articles

Generative Task Network:New Paradigm for Autonomic Task Planning and Execution Based on LLM

HUANG Xueqin, ZHANG Sheng, ZHU Xianqiang, ZHANG Qianzhen, ZHU Cheng   

  1. National Key Laboratory of Information Systems Engineering,National University of Defense Technology,Changsha 410073,China
  • Received:2024-11-12 Revised:2024-12-25 Online:2025-03-15 Published:2025-03-07
  • About author:HUANG Xueqin,born in 1997,Ph.D student.His main research interests include large language model and intelligent task planning.
    ZHU Cheng,born in 1976,Ph.D,principal investigator.His main research interests include command and control,and intelligent decision-making.
  • Supported by:
    National Defense Basic Research Program(WDZC20235250412) and National University of Defense Technology Research Fund(ZK20-30).

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Abstract: Owing to the development of generative artificial intelligence,the intelligent planning technology of unmanned systems is set to undergo a new transformation.This paper first analyzes the shortcomings of traditional intelligent task planning paradigms in terms of generalization,transferability and coherence.In response,it proposes a new paradigm for task planning and execution based on large language models,namely generative task network.This method enables unmanned systems to autonomously discover tasks,intelligently plan,and automatically execute them,forming a closed-loop from problem to solution.It also endows the task planning process of unmanned systems with the advantages of generalization and ease of transfer.This paper then introduces the concept of the generative task network,defines its key elements and models its process,and subsequently designs a ge-neral application architecture.Finally,an application analysis is conducted taking the aviation materials warehouse of N Airlines as the scenario,effectively enhancing the intelligence and automation levels of unmanned systems in warehouse management.

Key words: Unmanned systems, Large language model, Task planning, Task execution, Generative task network

CLC Number: 

  • TP182
[1]OpenAI:Hello GPT-4o[EB/OL].(2024-05-14) [2024-09-01].https://openai.com/index/hello-gpt-4o/.
[2]ZENG T G A,XU B,WANG B,et al.ChatGLM:A Family of Large Language Models from GLM-130B to GLM-4 All Tools[J].arXiv:2406.12793,2024.
[3]FIORI L,DOSHI A,MARTINEZ E,et al.The Use of Un-manned Aerial Systems in Marine Mammal Research[J].Remote Sensing.2017,9(6):543.
[4]RAMESH P S,MURUGA L J J V.Evaluation of design criteria for mini unmanned aircraft systems(UAS) applications[J].Aircraft Engineering and Aerospace Technology,2022,94(3):327-335.
[5]SUN Z,WANG Q,CHEN L,et al.Unmanned Technology-Based Civil-Military Intelligent Logistics System:From Construction to Integration[J].Journal of Beijing Institute of Technology,2022,31(2):140-151.
[6]RIZK Y,AWAD M,TUNSTEL E W.Cooperative Heterogeneous Multi-Robot Systems:A Survey[J].ACM Computing Surveys,2020,52(2):1-31.
[7]WU X,XIAO B,CAO L,et al.Optimal Transport and ModelPredictive Control-based Simultaneous Task Assignment and Trajectory Planning for Unmanned System Swarm[J].Journal of Intelligent & Robotic Systems,2024(1):28.
[8]ZHU Z L,MA G B,HUANG P,et al.Establishment and Solution of Multi-satellite Imaging Scheduling Model Based on Task Decomposition[J].Spacecraft Engineering,2018(2):6-13.
[9]BI W,ZHANG M,CHEN H,et al.Cooperative task allocation method for air-sea heterogeneous unmanned system with an application to ocean environment information monitoring[J].Ocean Engineering,2024,309(Part 2):118496.
[10]LIU Q,HUANG K,LIU H,et al.Multi-Domain Collaborative Task Allocation and Conflict Resolution in Unmanned Systems under Complex Constraints[J].Unmanned Systems,2024,7(2):1-11.
[11]LIU H,LIAO Y,SHEN C S A J.Task Allocation of Heterogeneous Multi-Unmanned Systems Based on Improved Sheep Flock Optimization Algorithm[J].Future Internet,2024,16(4):1-20.
[12]SAMIEI A,ISMAIL S,SUN L.Cluster-based hungarian ap-proach to task allocation for unmanned aerial vehicles[C]//2019 IEEE National Aerospaceand Electronics Conference(NAECON).IEEE,2019:148-154.
[13]KARAMAN S,SHIMA T,FRAZZOLI E.Effective task assignment for complex uav operations using genetic algorithms[C]//AIAA Guidance,Navigation,and Control Conference.2013:6211.
[14]HAKSAR R N,SCHWAGER M.Distributed deep reinforce-ment learning for fighting forest fires with a network of aerial robots[C]//2018 IEEE/RSJ International Conference on Intelligent Robots andSystems(IROS).IEEE,2018:1067-1074.
[15]FU X,FENG P,GAO X.Swarm UAVs task and resource dynamic assignment algorithm based on task sequence mechanism[J].IEEE Access:Practical Innovations,Open Solutions,2019,7:41090-41100.
[16]WU Y,GOU J,JI H,et al.Hierarchical mission replanning for multiple UAV formations performing tasks in dynamic situation[J].Computer Communications,2023,200:132-148.
[17]SHAO T,ZHANG H,CHENG K,et al.Review of replanning in hierarchical task network[J].Systems Engineering and Electronics.2020,42(12):2833-2846.
[18]WARNER S M,ROYSET J O.Optimizing Surveillance Satellites for the Synthetic Theater Operations Research Model[J].Military Operations Research,2024,29(1):31-44.
[19]WEBER G,THOMAS J J,SAUCEDO J A M,et al.Correction to:Preface:Special Issue on Modeling,Simulation,and Optimization in Operational Research[J].Journal of the Operations Research Society of China,2024,12(2):548.
[20]GEORGIEVSKI I,AIELLO M.HTN planning:Overview,comparison,and beyond[J].Artificial Intelligence,2015,222(May):124-156.
[21]MORISSET B,GHALLAB M.Learning how to combine sensorymotor modalities for a robust behavior[C]//the International Seminar on Advances in Plan-Based Control of Robotic Agents.Heidelberg,Berlin:Springer,2002:157-178.
[22]SIRIN E,PARSIA B,WU D,et al.HTN planning for web service composition using SHOP2[J].Web Semantics:Science,Services and Agents on the World Wide Web,2004,1(4):377-396.
[23]VASWANI A,SHAZEER N,PARMAR N,et al.Attention isall you need[C]//Proceedings of the 31st International Confe-rence on Neural Information Processing Systems(NIPS’17).2017:6000-6010.
[24]BROWN T B,MANN B,RYDER N,et al.Language Models are Few-Shot Learners[J].arXiv:2005.14165,2020.
[25]TOUVRON H,LAVRIL T,IZACARD G,et al.LLaMA:Open and Efficient Foundation Language Models[J].arXiv:2302.13971,2023.
[26]TOUVRON H,MARTIN L,STONE K.Llama 2:Open Foundation and Fine-Tuned Chat Models[J].arXiv:2307.09288,2023.
[27]ZHANG Y,LI Y,CUI L,et al.Siren’s Song in the AI Ocean:A Survey on Hallucination in Large Language Models[J].arXiv:2309.01219,2023.
[28]PENG B,GALLEY M,HE P,et al.Check Your Facts and Try Again:Improving Large Language Models with External Knowledge and Automated Feedback[J].arXiv:2302.12813,2023.
[29]WANG L,MA C,FENG X,et al.A Survey on Large Language Model based Autonomous Agents[J].arXiv:2308.11432,2023.
[30]YAO S,ZHAO J,YU D,et al.ReAct:Synergizing Reasoningand Acting in Language Models[J].arXiv:2210.03629,2022.
[31]CHEN Z,ZHOU K,ZHANG B,et al.ChatCoT:Tool-Augmented Chain-of-Thought Reasoning on Chat-based Large Language Models[J].arXiv:2305.14323.2023.
[32]YAO S,YU D,ZHAO J,et al.Tree of thoughts:Deliberateproblem solving with large language models[J].arXiv:2305.10601,2023.
[33]ZHUANG Y,CHEN X,YU T,et al.Toolchain*:Efficient action space navigation in large language models with a* search[J].arXiv:2310.13227,2023.
[34]DAGAN G,KELLER F,LASCARIDES A.Dynamic planning with aLLM[J].arXiv:2308.06391,2023.
[35]SONG C H,WU J,WASHINGTON C,et al.LLM-planner:Few-shot grounded planning for embodied agents with large language models[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2023:2998-3009.
[36]CHEN W,SU Y,ZUO J,et al.Agentverse:Facilitating multi-agent collaboration and exploring emergent behaviors[C]//The Twelfth International Conference on Learning Representations.2023.
[37]TEAM X.XAgent:An autonomous agent for complex task solving [EB/OL].https://github.com/OpenBMB/XAgent.
[38]WU Q,BANSAL G,ZHANG J,et al.Autogen:Enabling next-gen llm applications via multi-agent conversation framework[J].arXiv:2308.08155,2023.
[39]PALANTIR.AIP for defense[EB/OL].https://www.palantir.com/platforms/aip/defense/.
[40]CORVEY W.Exploratory models of human-ai teams(emhat)[EB/OL].https://www.darpa.mil/program/exploratory-moldels-of-human-ai-teams
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