TY - GEN
T1 - Meta-agent conflict-based search for optimal multi-agent path finding
AU - Sharon, Guni
AU - Stern, Roni
AU - Felner, Ariel
AU - Sturtevant, Nathan
PY - 2012
Y1 - 2012
N2 - The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. It is possible to solve this problem optimally with algorithms that are based on the A* algorithm. Recently, we proposed an alternative algorithm called Conflict-Based Search (CBS) (Sharon et al. 2012), which was shown to outperform the A*-based algorithms in some cases. CBS is a two-level algorithm. At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. While in some cases CBS is very efficient, in other cases it is worse than A*-based algorithms. This paper focuses on the latter case by generalizing CBS to Meta-Agent CBS (MA-CBS). The main idea is to couple groups of agents into meta-agents if the number of internal conflicts between them exceeds a given bound. MACBS acts as a framework that can run on top of any complete MAPF solver. We analyze our new approach and provide experimental results demonstrating that it outperforms basic CBS and other A*-based optimal solvers in many cases.
AB - The task in the multi-agent path finding problem (MAPF) is to find paths for multiple agents, each with a different start and goal position, such that agents do not collide. It is possible to solve this problem optimally with algorithms that are based on the A* algorithm. Recently, we proposed an alternative algorithm called Conflict-Based Search (CBS) (Sharon et al. 2012), which was shown to outperform the A*-based algorithms in some cases. CBS is a two-level algorithm. At the high level, a search is performed on a tree based on conflicts between agents. At the low level, a search is performed only for a single agent at a time. While in some cases CBS is very efficient, in other cases it is worse than A*-based algorithms. This paper focuses on the latter case by generalizing CBS to Meta-Agent CBS (MA-CBS). The main idea is to couple groups of agents into meta-agents if the number of internal conflicts between them exceeds a given bound. MACBS acts as a framework that can run on top of any complete MAPF solver. We analyze our new approach and provide experimental results demonstrating that it outperforms basic CBS and other A*-based optimal solvers in many cases.
UR - http://www.scopus.com/inward/record.url?scp=84893404910&partnerID=8YFLogxK
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AN - SCOPUS:84893404910
SN - 9781577355847
T3 - Proceedings of the 5th Annual Symposium on Combinatorial Search, SoCS 2012
SP - 97
EP - 104
BT - Proceedings of the 5th Annual Symposium on Combinatorial Search, SoCS 2012
T2 - 5th International Symposium on Combinatorial Search, SoCS 2012
Y2 - 19 July 2012 through 21 July 2012
ER -