2 Search Results for "Petit, Matthieu"

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DOI: 10.4230/LIPIcs.CP.2025.40

An Efficient and Uniform CSP Solution Generator Generator

Authors: Ghiles Ziat and Martin Pépin

Published in: LIPIcs, Volume 340, 31st International Conference on Principles and Practice of Constraint Programming (CP 2025)

Abstract

Constraint-based random testing is a powerful technique which aims at generating random test cases to verify functional properties of a program. Its objective is to determine whether a function satisfies a given property for every possible input. This approach requires firstly defining the property to satisfy, then secondly to provide a "generator of inputs" able to feed the program with the inputs generated. Besides, function inputs often need to satisfy certain constraints to ensure the function operates correctly, which makes the crafting of such a generator a hard task. In this paper, we are interested in the problem of manufacturing a uniform and efficient generator for the solutions of a CSP. In order to do that, we propose a specialized solving method that produces a well-suited representation for random sampling. Our solving method employs a dedicated propagation scheme based on the hypergraph representation of a CSP, and a custom split heuristic called birdge-first that emphasizes the interests of our propagation scheme. The generators we build are general enough to handle a wide range of use-cases. They are moreover uniform by construction, iterative and self-improving. We present a prototype built upon the AbSolute constraint solving library and demonstrate its performances on several realistic examples.

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Ghiles Ziat and Martin Pépin. An Efficient and Uniform CSP Solution Generator Generator. In 31st International Conference on Principles and Practice of Constraint Programming (CP 2025). Leibniz International Proceedings in Informatics (LIPIcs), Volume 340, pp. 40:1-40:18, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025)

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@InProceedings{ziat_et_al:LIPIcs.CP.2025.40,
  author =	{Ziat, Ghiles and P\'{e}pin, Martin},
  title =	{{An Efficient and Uniform CSP Solution Generator Generator}},
  booktitle =	{31st International Conference on Principles and Practice of Constraint Programming (CP 2025)},
  pages =	{40:1--40:18},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-380-5},
  ISSN =	{1868-8969},
  year =	{2025},
  volume =	{340},
  editor =	{de la Banda, Maria Garcia},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2025.40},
  URN =		{urn:nbn:de:0030-drops-239010},
  doi =		{10.4230/LIPIcs.CP.2025.40},
  annote =	{Keywords: Constraint Programming, Property-based Testing}
}

Document

DOI: 10.4230/LIPIcs.ICLP.2011.220

Bayesian Annotation Networks for Complex Sequence Analysis

Authors: Henning Christiansen, Christian Theil Have, Ole Torp Lassen, and Matthieu Petit

Published in: LIPIcs, Volume 11, Technical Communications of the 27th International Conference on Logic Programming (ICLP'11) (2011)

Abstract

Probabilistic models that associate annotations to sequential data are widely used in computational biology and a range of other applications. Models integrating with logic programs provide, furthermore, for sophistication and generality, at the cost of potentially very high computational complexity. A methodology is proposed for modularization of such models into sub-models, each representing a particular interpretation of the input data to be analysed. Their composition forms, in a natural way, a Bayesian network, and we show how standard methods for prediction and training can be adapted for such composite models in an iterative way, obtaining reasonable complexity results. Our methodology can be implemented using the probabilistic-logic PRISM system, developed by Sato et al, in a way that allows for practical applications.

Cite as

Henning Christiansen, Christian Theil Have, Ole Torp Lassen, and Matthieu Petit. Bayesian Annotation Networks for Complex Sequence Analysis. In Technical Communications of the 27th International Conference on Logic Programming (ICLP'11). Leibniz International Proceedings in Informatics (LIPIcs), Volume 11, pp. 220-230, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2011)

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@InProceedings{christiansen_et_al:LIPIcs.ICLP.2011.220,
  author =	{Christiansen, Henning and Theil Have, Christian and Torp Lassen, Ole and Petit, Matthieu},
  title =	{{Bayesian Annotation Networks for Complex Sequence Analysis}},
  booktitle =	{Technical Communications of the 27th International Conference on Logic Programming (ICLP'11)},
  pages =	{220--230},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-939897-31-6},
  ISSN =	{1868-8969},
  year =	{2011},
  volume =	{11},
  editor =	{Gallagher, John P. and Gelfond, Michael},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.ICLP.2011.220},
  URN =		{urn:nbn:de:0030-drops-31649},
  doi =		{10.4230/LIPIcs.ICLP.2011.220},
  annote =	{Keywords: Probabilistic Logic Bayesian Sequence Analysis}
}

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2 Search Results for "Petit, Matthieu"

An Efficient and Uniform CSP Solution Generator Generator

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Bayesian Annotation Networks for Complex Sequence Analysis

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