Quantified Constraint Handling Rules

In this article, the QCSP (Quantified Constraint Satisfaction Problems) framework is shifted to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic generation of quantifiers and access to user-defined constraints. QCSP offers a natural framework to express PSPACE problems as finite two-players games. But to define a QCSP model, the alternation of quantifiers must be formerly known and cannot be built dynamically even if the worst case will not occur. To overcome this issue, the new QCHR formalism, that allows to generate quantifiers dynamically during the solving, is defined. QCHR models exhibit state-of-the-art performances on a priori fixed alternation of quantifiers and outperforms previous QCSP approaches when the generation of quantifiers is dynamic.

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Publication Details

Journal
ACM Transactions on Computational Logic
Published
2025-04-08
DOI
https://doi.org/10.1145/3728640
Citations
1
Primary Topic
Constraint Satisfaction and Optimization
Type
article
Field-Weighted Citation Impact
1.49
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article

Quantified Constraint Handling Rules

Igor Stéphan, Vincent Barichard
1 citations
ACM Transactions on Computational Logic
Constraint Satisfaction and Optimization
1.49
article

Quantified Constraint Handling Rules

Igor Stéphan, Vincent Barichard
article en
1 citations

Abstract

In this article, the QCSP (Quantified Constraint Satisfaction Problems) framework is shifted to the QCHR (Quantified Constraint Handling Rules) framework by enabling dynamic generation of quantifiers and access to user-defined constraints. QCSP offers a natural framework to express PSPACE problems as finite two-players games. But to define a QCSP model, the alternation of quantifiers must be formerly known and cannot be built dynamically even if the worst case will not occur. To overcome this issue, the new QCHR formalism, that allows to generate quantifiers dynamically during the solving, is defined. QCHR models exhibit state-of-the-art performances on a priori fixed alternation of quantifiers and outperforms previous QCSP approaches when the generation of quantifiers is dynamic.

ACM Transactions on Computational Logic
Université d'Angers (FR)
Openalex Percentile: Top 19%
Constraint Satisfaction and Optimization
1.49
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