Multi-Agent Path Finding - An Overview

被引:48
|
作者
Stern, Roni [1 ]
机构
[1] Ben Gurion Univ Negev, Beer Sheva, Israel
来源
ARTIFICIAL INTELLIGENCE | 2019年 / 11866卷
关键词
Multi-Agent Pathfinding; Heuristic search; SEARCH;
D O I
10.1007/978-3-030-33274-7_6
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:96 / 115
页数:20
相关论文
共 50 条
  • [1] Multi-Agent Path Finding with Deadlines
    Ma, Hang
    Wagner, Glenn
    Feiner, Ariel
    Li, Jiaoyang
    Kumar, T. K. Satish
    Koenig, Sven
    [J]. PROCEEDINGS OF THE TWENTY-SEVENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2018, : 417 - 423
  • [2] Incremental multi-agent path finding
    Semiz, Fatih
    Polat, Faruk
    [J]. FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE, 2021, 116 : 220 - 233
  • [3] Robust Multi-Agent Path Finding
    Atzmon, Dor
    Stern, Roni
    Felner, Ariel
    Wagner, Glenn
    Bartak, Roman
    Zhou, Neng-Fa
    [J]. PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS (AAMAS' 18), 2018, : 1862 - 1864
  • [4] Multi-Agent Path Finding on Ozobots
    Bartak, Roman
    Krasicenko, Ivan
    Svancara, Jiri
    [J]. PROCEEDINGS OF THE TWENTY-EIGHTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, 2019, : 6491 - 6493
  • [5] Robust Multi-Agent Path Finding and Executing
    Atzmon, Dor
    Stern, Roni Tzvi
    Felner, Ariel
    Wagner, Glenn
    Bartak, Roman
    Zhou, Neng-Fa
    [J]. JOURNAL OF ARTIFICIAL INTELLIGENCE RESEARCH, 2020, 67 : 549 - 579
  • [6] Multi-agent path finding with mutex propagation
    Zhang, Han
    Li, Jiaoyang
    Surynek, Pavel
    Kumar, T. K. Satish
    Koenig, Sven
    [J]. ARTIFICIAL INTELLIGENCE, 2022, 311
  • [7] Multi-Agent Path Finding with Kinematic Constraints
    Honig, Wolfgang
    Kumar, T. K. Satish
    Cohen, Liron
    Ma, Hang
    Xu, Hong
    Ayanian, Nora
    Koenig, Sven
    [J]. TWENTY-SIXTH INTERNATIONAL CONFERENCE ON AUTOMATED PLANNING AND SCHEDULING (ICAPS 2016), 2016, : 477 - 485
  • [8] Multi-agent Path Finding with Capacity Constraints
    Surynek, Pavel
    Kumar, T. K. Satish
    Koenig, Sven
    [J]. ADVANCES IN ARTIFICIAL INTELLIGENCE, AI*IA 2019, 2019, 11946 : 235 - 249
  • [9] Adversarial Multi-Agent Path Finding is Intractable
    Ivanova, Marika
    Surynek, Pavel
    [J]. 2021 IEEE 33RD INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2021), 2021, : 481 - 486
  • [10] Robust multi-agent path finding and executing
    Atzmon, Dor
    Stern, Roni
    Felner, Ariel
    Wagner, Glenn
    Barták, Roman
    Zhou, Neng-Fa
    [J]. Journal of Artificial Intelligence Research, 2020, 67 : 549 - 579