A More Compact Translation of Pseudo-Boolean Constraints into CNF Such That Generalized Arc Consistency Is Maintained
In this paper we answer the open question for the existence of a more compact encoding from Pseudo-Boolean constraints into CNF that maintains generalized arc consistency by unit propagation, formalized by Bailleux et al. in [21]. In contrast to other encodings our approach is defined in an abstract...
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Published in | KI 2014: Advances in Artificial Intelligence pp. 123 - 134 |
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Main Authors | , , |
Format | Book Chapter |
Language | English |
Published |
Cham
Springer International Publishing
2014
|
Series | Lecture Notes in Computer Science |
Subjects | |
Online Access | Get full text |
ISBN | 3319112058 9783319112053 |
ISSN | 0302-9743 1611-3349 |
DOI | 10.1007/978-3-319-11206-0_13 |
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Abstract | In this paper we answer the open question for the existence of a more compact encoding from Pseudo-Boolean constraints into CNF that maintains generalized arc consistency by unit propagation, formalized by Bailleux et al. in [21]. In contrast to other encodings our approach is defined in an abstract way and we present a concrete instantiation, resulting in a space complexity of $\mathcal{O}(n^2 \text{\,log}^2(n)\text{\,log}(w_{\mathsf{max}}))$ clauses in contrast to $\mathcal{O}(n^3 \text{\,log}(n)\text{\,log}(w_{\mathsf{max}}))$ clauses generated by the previously best known encoding that maintains generalized arc consistency. |
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AbstractList | In this paper we answer the open question for the existence of a more compact encoding from Pseudo-Boolean constraints into CNF that maintains generalized arc consistency by unit propagation, formalized by Bailleux et al. in [21]. In contrast to other encodings our approach is defined in an abstract way and we present a concrete instantiation, resulting in a space complexity of $\mathcal{O}(n^2 \text{\,log}^2(n)\text{\,log}(w_{\mathsf{max}}))$ clauses in contrast to $\mathcal{O}(n^3 \text{\,log}(n)\text{\,log}(w_{\mathsf{max}}))$ clauses generated by the previously best known encoding that maintains generalized arc consistency. |
Author | Philipp, Tobias Manthey, Norbert Steinke, Peter |
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DOI | 10.1007/978-3-319-11206-0_13 |
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Editor | Lutz, Carsten Thielscher, Michael |
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Notes | Original Abstract: In this paper we answer the open question for the existence of a more compact encoding from Pseudo-Boolean constraints into CNF that maintains generalized arc consistency by unit propagation, formalized by Bailleux et al. in [21]. In contrast to other encodings our approach is defined in an abstract way and we present a concrete instantiation, resulting in a space complexity of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{O}(n^2 \text{\,log}^2(n)\text{\,log}(w_{\mathsf{max}}))$\end{document} clauses in contrast to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$\mathcal{O}(n^3 \text{\,log}(n)\text{\,log}(w_{\mathsf{max}}))$\end{document} clauses generated by the previously best known encoding that maintains generalized arc consistency. |
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PublicationSeriesSubtitle | Lecture Notes in Artificial Intelligence |
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PublicationSubtitle | 37th Annual German Conference on AI, Stuttgart, Germany, September 22-26, 2014. Proceedings |
PublicationTitle | KI 2014: Advances in Artificial Intelligence |
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SubjectTerms | Auxiliary Variable Conjunctive Normal Form Constraint Programming Propositional Variable Unit Propagation |
Title | A More Compact Translation of Pseudo-Boolean Constraints into CNF Such That Generalized Arc Consistency Is Maintained |
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