Correlation Heuristics for Constraint Programming

Autor:	Wang, Ruiwei, Xia, Wei, Yap, Roland H. C.
Rok vydání:	2018
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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, dom/wdeg, 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. Comment: Paper presented at the 29th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2017, Boston, Massachusetts, USA, November 6-8, 2017
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1805.02205 Zobrazit plný text záznamu View this record from Arxiv