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arxiv: 1702.05515 · v1 · pith:G7X2N3Q2new · submitted 2017-02-17 · 💻 cs.AI · cs.MA· cs.RO

Overview: Generalizations of Multi-Agent Path Finding to Real-World Scenarios

Hang Ma , Sven Koenig , Nora Ayanian , Liron Cohen , Wolfgang Hoenig , T. K. Satish Kumar , Tansel Uras , Hong Xu
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Craig Tovey Guni Sharon
This is my paper
classification 💻 cs.AI cs.MAcs.RO
keywords mapffindingissuesmethodsmulti-agentpathreal-worldresearch
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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.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. On the Tour Towards DPLL(MAPF) and Beyond

    cs.AI 2019-07 unverdicted novelty 2.0

    Discusses the research steps needed to create a fully integrated DPLL(MAPF) solver for optimal multi-agent path finding via SMT, contrasting it with current loose integrations.