TY - GEN
T1 - Multi-agent path finding – an overview
AU - Stern, Roni
N1 - Funding Information:
Supported by ISF grant 210/17 to Roni Stern.
Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019/10/14
Y1 - 2019/10/14
N2 - Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. In recent years, there has been a growing interest in MAPF in the Artificial Intelligence (AI) research community. This interest is partially because real-world MAPF applications, such as warehouse management, multi-robot teams, and aircraft management, are becoming more prevalent. In this overview, we discuss several possible definitions of the MAPF problem. Then, we survey MAPF algorithms, starting with fast but incomplete algorithms, then fast, complete but not optimal algorithms, and finally optimal algorithms. Then, we describe approximately optimal algorithms and conclude with non-classical MAPF and pointers for future reading and future work.
AB - Multi-Agent Pathfinding (MAPF) is the problem of finding paths for multiple agents such that every agent reaches its goal and the agents do not collide. In recent years, there has been a growing interest in MAPF in the Artificial Intelligence (AI) research community. This interest is partially because real-world MAPF applications, such as warehouse management, multi-robot teams, and aircraft management, are becoming more prevalent. In this overview, we discuss several possible definitions of the MAPF problem. Then, we survey MAPF algorithms, starting with fast but incomplete algorithms, then fast, complete but not optimal algorithms, and finally optimal algorithms. Then, we describe approximately optimal algorithms and conclude with non-classical MAPF and pointers for future reading and future work.
KW - Heuristic search
KW - Multi-Agent Pathfinding
UR - http://www.scopus.com/inward/record.url?scp=85076105481&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-33274-7_6
DO - 10.1007/978-3-030-33274-7_6
M3 - Conference contribution
AN - SCOPUS:85076105481
SN - 9783030332730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 96
EP - 115
BT - Artificial Intelligence - 5th RAAI Summer School, 2019, Tutorial Lectures
A2 - Osipov, Gennady S.
A2 - Panov, Aleksandr I.
A2 - Yakovlev, Konstantin S.
PB - Springer
T2 - 5th RAAI Summer School on Artificial Intelligence, 2019
Y2 - 4 July 2019 through 7 July 2019
ER -