A Mining-Based Compression Approach for Constraint Satisfaction Problems
Artificial Intelligence
2013-05-16 v1
Abstract
In this paper, we propose an extension of our Mining for SAT framework to Constraint satisfaction Problem (CSP). We consider n-ary extensional constraints (table constraints). Our approach aims to reduce the size of the CSP by exploiting the structure of the constraints graph and of its associated microstructure. More precisely, we apply itemset mining techniques to search for closed frequent itemsets on these two representation. Using Tseitin extension, we rewrite the whole CSP to another compressed CSP equivalent with respect to satisfiability. Our approach contrast with previous proposed approach by Katsirelos and Walsh, as we do not change the structure of the constraints.
Cite
@article{arxiv.1305.3321,
title = {A Mining-Based Compression Approach for Constraint Satisfaction Problems},
author = {Said Jabbour and Lakhdar Sais and Yakoub Salhi},
journal= {arXiv preprint arXiv:1305.3321},
year = {2013}
}
Comments
arXiv admin note: substantial text overlap with arXiv:1304.4415