TY - GEN
T1 - Multi-agent pathfinding
T2 - 12th International Symposium on Combinatorial Search, SoCS 2019
AU - Stern, Roni
AU - Sturtevant, Nathan R.
AU - Felner, Ariel
AU - Koenig, Sven
AU - Ma, Hang
AU - Walker, Thayne T.
AU - Li, Jiaoyang
AU - Atzmon, Dor
AU - Cohen, Liron
AU - Satish Kumar, T. K.
AU - Boyarski, Eli
AU - Barták, Roman
N1 - Publisher Copyright: Copyright © 2019, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85086832729&partnerID=8YFLogxK
M3 - Conference contribution
T3 - Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019
SP - 151
EP - 158
BT - Proceedings of the 12th International Symposium on Combinatorial Search, SoCS 2019
A2 - Surynek, Pavel
A2 - Yeoh, William
Y2 - 16 July 2019 through 17 July 2019
ER -