Improving Efficiency of Answer Set Planning with Rough Solutions from Large Language Models for Robotic Task Planning

Lin, Xinrui; Wu, Yangfan; Yang, Huanyu; Huang, Yuting; Zhang, Yu; Ji, Jianmin; Zhang, Yanyong

doi:10.24963/ijcai.2025/509

Improving Efficiency of Answer Set Planning with Rough Solutions from Large Language Models for Robotic Task Planning

Xinrui Lin, Yangfan Wu, Huanyu Yang, Yuting Huang, Yu Zhang, Jianmin Ji, Yanyong Zhang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 4570-4578.

https://doi.org/10.24963/ijcai.2025/509

Keywords

Knowledge Representation and Reasoning: KRR: Logic programming
Knowledge Representation and Reasoning: KRR: Applications
Knowledge Representation and Reasoning: KRR: Non-monotonic reasoning

Abstract

Answer Set Programming (ASP) planning can be used to refine the rough solutions generated by Large Language Models (LLMs) to handle specific restrictions of actions, i.e., reconstruct the rough solutions to be executable, for robotic task planning. However, it is still challenging to efficiently solve ASP programs that have multiple variables with large domains, which prevents the above application of ASP planning from real-world task planning problems. In this paper, we consider how to reduce the domains of variables without losing possible solutions for ASP planning, while given these rough solutions from LLMs. Based on the above reduction, we introduce CLMASP, an approach that couples LLMs with ASP for robotic task planning. We evaluate CLMASP on the VirtualHome platform for common indoor tasks, demonstrating a significant improvement in the executable rate from under 10% to nearly 90% and reducing average ASP planning time from over 2 hours to under 5 seconds. Code is available at https://github.com/CLMASP/CLMASP.

Xinrui Lin, Yangfan Wu, Huanyu Yang, Yuting Huang, Yu Zhang, Jianmin Ji, and Yanyong Zhang. 2025. Improving Efficiency of Answer Set Planning with Rough Solutions from Large Language Models for Robotic Task Planning. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 4570–4578. https://doi.org/10.24963/ijcai.2025/509

@inproceedings{ijcai2025p509,
  title     = {Improving Efficiency of Answer Set Planning with Rough Solutions from Large Language Models for Robotic Task Planning},
  author    = {Lin, Xinrui and Wu, Yangfan and Yang, Huanyu and Huang, Yuting and Zhang, Yu and Ji, Jianmin and Zhang, Yanyong},
  booktitle = {Proceedings of the Thirty-Fourth International Joint Conference on
               Artificial Intelligence, {IJCAI-25}},
  publisher = {International Joint Conferences on Artificial Intelligence Organization},
  editor    = {James Kwok},
  pages     = {4570--4578},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/509},
  url       = {https://doi.org/10.24963/ijcai.2025/509},
}

TY  - CPAPER
TI  - Improving Efficiency of Answer Set Planning with Rough Solutions from Large Language Models for Robotic Task Planning
AU  - Lin, Xinrui
AU  - Wu, Yangfan
AU  - Yang, Huanyu
AU  - Huang, Yuting
AU  - Zhang, Yu
AU  - Ji, Jianmin
AU  - Zhang, Yanyong
T2  - Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25
PB  - International Joint Conferences on Artificial Intelligence Organization
PY  - 2025
SP  - 4570
EP  - 4578
DO  - 10.24963/ijcai.2025/509
UR  - https://doi.org/10.24963/ijcai.2025/509
ER  -