Streaming Multi-agent Pathfinding

Tang, Mingkai; Gan, Lu; Zhang, Kaichen

doi:10.24963/ijcai.2025/31

Streaming Multi-agent Pathfinding

Mingkai Tang, Lu Gan, Kaichen Zhang

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Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence. Main Track. Pages 267-274.

https://doi.org/10.24963/ijcai.2025/31

Keywords

Planning and Scheduling: PS: Search in planning and scheduling
Agent-based and Multi-agent Systems: MAS: Multi-agent planning

Abstract

The task of the multi-agent pathfinding (MAPF) problem is to navigate a team of agents from their start point to the goal points. However, this setup is unsuitable in the assembly line scenario, which is periodic with a long working hour. To address this issue, the study formalizes the streaming MAPF (S-MAPF) problem, which assumes that the agents in the same agent stream have a periodic start time and share the same action sequence. The proposed solution, Agent Stream Conflict-Based Search (ASCBS), is designed to tackle this problem by incorporating a cyclic vertex/edge constraint to handle conflicts. Additionally, this work explores the potential usage of the disjoint splitting strategy within ASCBS. Experimental results indicate that ASCBS surpasses traditional MAPF solvers in terms of runtime for scenarios with prolonged working hours.

Mingkai Tang, Lu Gan, and Kaichen Zhang. 2025. Streaming Multi-agent Pathfinding. In Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence, IJCAI-25. IJCAI, 267–274. https://doi.org/10.24963/ijcai.2025/31

@inproceedings{ijcai2025p31,
  title     = {Streaming Multi-agent Pathfinding},
  author    = {Tang, Mingkai and Gan, Lu and Zhang, Kaichen},
  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     = {267--274},
  year      = {2025},
  month     = {8},
  note      = {Main Track},
  doi       = {10.24963/ijcai.2025/31},
  url       = {https://doi.org/10.24963/ijcai.2025/31},
}

TY  - CPAPER
TI  - Streaming Multi-agent Pathfinding
AU  - Tang, Mingkai
AU  - Gan, Lu
AU  - Zhang, Kaichen
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  - 267
EP  - 274
DO  - 10.24963/ijcai.2025/31
UR  - https://doi.org/10.24963/ijcai.2025/31
ER  -