Correlation Heuristics for Constraint Programming

被引：1

作者：

Wang, Ruiwei ^{[1
]}

Xia, Wei ^{[1
]}

Yap, Roland H. C. ^{[1
]}

机构：

[1] Natl Univ Singapore, Sch Comp, Singapore, Singapore

来源：

2017 IEEE 29TH INTERNATIONAL CONFERENCE ON TOOLS WITH ARTIFICIAL INTELLIGENCE (ICTAI 2017) | 2017年

关键词：

SATISFACTION PROBLEMS; SEARCH;

D O I：

10.1109/ICTAI.2017.00159

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Effective general-purpose search strategies are an important component in Constraint Programming. We introduce a new idea, namely, using correlations between variables to guide search. Variable correlations are measured and maintained by using domain changes during constraint propagation. We propose two variable heuristics based on the correlation matrix, crbs-sum and crbs-max. We evaluate our correlation heuristics with well known heuristics, namely, domiwdeg, impact-based search and activity-based search. Experiments on a large set of benchmarks show that our correlation heuristics are competitive with the other heuristics, and can be the fastest on many series.

引用

页码：1037 / 1041

页数：5

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