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Learn all about Multi-Agent Path Finding (MAPF)


H. Ma, S. Koenig, N. Ayanian, L. Cohen, W. Hoenig, S. Kumar, T. Uras, H. Xu, C. Tovey and G. Sharon. Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios. In Proceedings of the IJCAI-16 Workshop on Multi-Agent Path Finding, 2016.

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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(last updated in 2022)