A Mining-Based Compression Approach for Constraint Satisfaction Problems

TL;DR

This paper introduces a novel CSP compression method using itemset mining to exploit structural properties, enabling more efficient problem solving without altering constraint structure.

Contribution

It extends mining-based SAT compression techniques to CSPs, applying itemset mining to reduce problem size while preserving structure, unlike previous methods.

Findings

Effective CSP size reduction demonstrated

Preserves original constraint structure

Comparable or improved solving efficiency

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.