Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios
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-world scenarios and four research directions that address them. We emphasize the importance...
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Main Authors | , , , , , , , , , |
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Format | Journal Article |
Language | English |
Published |
17.02.2017
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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-world scenarios and
four research directions that address them. We emphasize the importance of
addressing these issues as opposed to developing faster methods for the
standard formulation of the MAPF problem. |
---|---|
AbstractList | 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-world scenarios and
four research directions that address them. We emphasize the importance of
addressing these issues as opposed to developing faster methods for the
standard formulation of the MAPF problem. |
Author | Hoenig, Wolfgang Tovey, Craig Ayanian, Nora Cohen, Liron Koenig, Sven Kumar, T. K. Satish Xu, Hong Sharon, Guni Ma, Hang Uras, Tansel |
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BackLink | https://doi.org/10.48550/arXiv.1702.05515$$DView paper in arXiv |
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Snippet | Multi-agent path finding (MAPF) is well-studied in artificial intelligence,
robotics, theoretical computer science and operations research. We discuss
issues... |
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SubjectTerms | Computer Science - Artificial Intelligence Computer Science - Multiagent Systems Computer Science - Robotics |
Title | Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios |
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