Rectangle Search: An Anytime Beam Search

被引:0
|
作者
Lemons, Sofia [1 ,2 ]
Ruml, Wheeler [1 ]
Holte, Rob [3 ]
Linares Lopez, Carlos [4 ]
机构
[1] Univ New Hampshire, Durham, NH 03824 USA
[2] Earlham Coll, Richmond, IN 47374 USA
[3] Univ Alberta, Alberta Machine Intelligence Inst Amii, Edmonton, AB, Canada
[4] Univ Carlos III Madrid, Comp Sci & Engn Dept, Madrid, Spain
来源
THIRTY-EIGHTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOL 38 NO 18 | 2024年
关键词
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中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Anytime heuristic search algorithms try to find a (potentially suboptimal) solution as quickly as possible and then work to find better and better solutions until an optimal solution is obtained or time is exhausted. The most widely-known anytime search algorithms are based on best-first search. In this paper, we propose a new algorithm, rectangle search, that is instead based on beam search, a variant of breadth-first search. It repeatedly explores alternatives at all depth levels and is thus best-suited to problems featuring deep local minima. Experiments using a variety of popular search benchmarks suggest that rectangle search is competitive with fixed-width beam search and often performs better than the previous best anytime search algorithms.
引用
收藏
页码:20751 / 20758
页数:8
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