Constraint Programming viewed as Rule-based Programming
Krzysztof R. Apt, Eric Monfroy

TL;DR
This paper demonstrates how constraint programming can be fully represented as rule-based programming using first-order formulas, introducing rule-based notions of local consistency and implementing them via CHR rules.
Contribution
It introduces a rule-based framework for constraint programming, defining rule consistency and arc consistency through automatically generated rules, and shows how to implement this approach in constraint logic programming.
Findings
Rule-based approach effectively models constraint satisfaction problems.
Rule consistency is weaker than arc consistency but useful for Boolean constraints.
Automated rule generation enables practical implementation in CHR.
Abstract
We study here a natural situation when constraint programming can be entirely reduced to rule-based programming. To this end we explain first how one can compute on constraint satisfaction problems using rules represented by simple first-order formulas. Then we consider constraint satisfaction problems that are based on predefined, explicitly given constraints. To solve them we first derive rules from these explicitly given constraints and limit the computation process to a repeated application of these rules, combined with labeling.We consider here two types of rules. The first type, that we call equality rules, leads to a new notion of local consistency, called {\em rule consistency} that turns out to be weaker than arc consistency for constraints of arbitrary arity (called hyper-arc consistency in \cite{MS98b}). For Boolean constraints rule consistency coincides with the closure…
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Taxonomy
TopicsConstraint Satisfaction and Optimization · Data Management and Algorithms · Semantic Web and Ontologies
