Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios. Ma, H., Koenig, S., Ayanian, N., Cohen, L., Hoenig, W., Kumar, S., Uras, T., Xu, H., Tovey, C., & Sharon, G. In Proceedings of the IJCAI-16 Workshop on Multi-Agent Path Finding, 2016. abstract bibtex Multi-agent path finding (MAPF) is well-studied in artificial intelligence, robotics, theoretical computer science and operations research. We discuss issues that arise when generalizing MAPF methods to real-w orld scenarios and four research directions that address them. We emphasize the importance of addressing these issues as opposed to dev eloping faster methods for the standard formulation of the MAPF problem.
@INPROCEEDINGS{SKoen16i,
AUTHOR= "H. Ma and S. Koenig and N. Ayanian and L. Cohen and W. Hoenig and S. Kumar and T. Uras and H. Xu and C. Tovey and G. Sharon",
TITLE= "Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios",
BOOKTITLE= "Proceedings of the IJCAI-16 Workshop on Multi-Agent Path Finding",
YEAR= "2016",
PDF= "http://idm-lab.org/bib/abstracts/papers/ijcai16-workshop.pdf",
FLAGS= ":2016:,:svenkoenig:,:hangma:,:lironcohen:,:satishkumar:,:tanseluras:,:hongxu:,:noraayanian:,:wolfganghoenig:,:gunisharon:",
ABSTRACT=
"Multi-agent path finding (MAPF) is well-studied in artificial intelligence,
robotics, theoretical computer science and operations research. We discuss
issues that arise when generalizing MAPF methods to real-w orld scenarios and
four research directions that address them. We emphasize the importance of
addressing these issues as opposed to dev eloping faster methods for the
standard formulation of the MAPF problem. "
}
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