Iterative-Deepening Conflict-Based Search

被引：0
|
作者：
Boyarski, Eli [1 ]
Felner, Ariel [1 ]
Harabor, Daniel [2 ]
Stuckey, Peter J. [2 ]
Cohen, Liron [3 ]
Li, Jiaoyang [3 ]
Koenig, Sven [3 ]
机构：
[1] Ben Gurion Univ Negev, Beer Sheva, Israel
[2] Monash Univ, Clayton, Vic, Australia
[3] Univ Southern Calif, Los Angeles, CA 90007 USA
来源：
PROCEEDINGS OF THE TWENTY-NINTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE | 2020年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Conflict-Based Search (CBS) is a leading algorithm for optimal Multi-Agent Path Finding (MAPF). CBS variants typically compute MAPF solutions using some form of A* search. However, they often do so under strict time limits so as to avoid exhausting the available memory. In this paper, we present IDCBS, an iterative-deepening variant of CBS which can be executed without exhausting the memory and without strict time limits. IDCBS can be substantially faster than CBS due to incremental methods that it uses when processing CBS nodes.
引用
收藏
页码：4084 / 4090
页数：7
相关论文
共 50 条
12345