Abstract
The Multi-Agent Pathfinding (MAPF) problem is the fundamental problem of planning paths for multiple agents, where the key constraint is that the agents will be able to follow these paths concurrently without colliding with each other. Applications of MAPF include automated warehouses and autonomous vehicles. Research on MAPF has been flourishing in the past couple of years. Different MAPF research papers make different assumptions, e.g., whether agents can traverse the same road at the same time, and have different objective functions, e.g., minimize makespan or sum of agents' actions costs. These assumptions and objectives are sometimes implicitly assumed or described informally. This makes it difficult to establish appropriate baselines for comparison in research papers, as well as making it difficult for practitioners to find the papers relevant to their concrete application. This paper aims to fill this gap and support researchers and practitioners by providing a unifying terminology for describing common MAPF assumptions and objectives. In addition, we also provide pointers to two MAPF benchmarks. In particular, we introduce a new grid-based benchmark for MAPF, and demonstrate experimentally that it poses a challenge to contemporary MAPF algorithms.
Original language | English |
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Title of host publication | Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019 |
Editors | Pavel Surynek, William Yeoh |
Publisher | AAAI press |
Pages | 151-158 |
Number of pages | 8 |
ISBN (Electronic) | 9781577358084 |
State | Published - 2019 |
Externally published | Yes |
Event | 12th International Symposium on Combinatorial Search, SoCS 2019 - Napa, United States Duration: 16 Jul 2019 → 17 Jul 2019 |
Publication series
Name | Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019 |
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Conference
Conference | 12th International Symposium on Combinatorial Search, SoCS 2019 |
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Country/Territory | United States |
City | Napa |
Period | 16/07/19 → 17/07/19 |
Bibliographical note
Funding Information:This research is supported by ISF grants no. 210/17 to Roni Stern and #844/17 to Ariel Felner and Eyal Shimony, by BSF grant #2017692 and by NSF grant #1815660.
Publisher Copyright:
Copyright © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.