ML Anthology

Implicitly Coordinated Multi-Agent Path Finding Under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract)

Bernhard Nebel, Thomas Bolander, Thorsten Engesser, Robert Mattmüller
IJCAI 2019 pp. 6372-6376
doi:10.24963/IJCAI.2019/890 /ijcai/2019/nebel2019ijcai-implicitly/

Abstract

In multi-agent path finding, it is usually assumed that planning is performed centrally and that the destinations of the agents are common knowledge. We will drop both assumptions and analyze under which conditions it can be guaranteed that the agents reach their respective destinations using implicitly coordinated plans without communication.

PDF IJCAI Semantic Scholar

Cite

Text

Nebel et al. "Implicitly Coordinated Multi-Agent Path Finding Under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2019. doi:10.24963/IJCAI.2019/890

Markdown

[Nebel et al. "Implicitly Coordinated Multi-Agent Path Finding Under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract)." International Joint Conference on Artificial Intelligence, 2019.](https://mlanthology.org/ijcai/2019/nebel2019ijcai-implicitly/) doi:10.24963/IJCAI.2019/890

BibTeX

@inproceedings{nebel2019ijcai-implicitly,
  title     = {{Implicitly Coordinated Multi-Agent Path Finding Under Destination Uncertainty: Success Guarantees and Computational Complexity (Extended Abstract)}},
  author    = {Nebel, Bernhard and Bolander, Thomas and Engesser, Thorsten and Mattmüller, Robert},
  booktitle = {International Joint Conference on Artificial Intelligence},
  year      = {2019},
  pages     = {6372-6376},
  doi       = {10.24963/IJCAI.2019/890},
  url       = {https://mlanthology.org/ijcai/2019/nebel2019ijcai-implicitly/}
}