Tracking Progress in Multi-Agent Path Finding

Bojie Shen, Zhe Chen, Muhammad Aamir Cheema, Daniel D. Harabor, Peter J. Stuckey
Computer Science, Artificial Intelligence, Artificial Intelligence (cs.AI), Robotics (cs.RO)
2023-05-14 16:00:00
Multi-Agent Path Finding (MAPF) is an important core problem for many new and emerging industrial applications. Many works appear on this topic each year, and a large number of substantial advancements and performance improvements have been reported. Yet measuring overall progress in MAPF is difficult: there are many potential competitors, and the computational burden for comprehensive experimentation is prohibitively large. Moreover, detailed data from past experimentation is usually unavailable. In this work, we introduce a set of methodological and visualisation tools which can help the community establish clear indicators for state-of-the-art MAPF performance and which can facilitate large-scale comparisons between MAPF solvers. Our objectives are to lower the barrier of entry for new researchers and to further promote the study of MAPF, since progress in the area and the main challenges are made much clearer.
PDF: Tracking Progress in Multi-Agent Path Finding.pdf
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