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Constraint Acquisition Based on Solution Counting

Authors Christopher Coulombe, Claude-Guy Quimper

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Christopher Coulombe
  • Université Laval, Québec, Canada
Claude-Guy Quimper
  • Université Laval, Québec, Canada

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Christopher Coulombe and Claude-Guy Quimper. Constraint Acquisition Based on Solution Counting. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 15:1-15:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.CP.2022.15

Abstract

We propose CABSC, a system that performs Constraint Acquisition Based on Solution Counting. In order to learn a Constraint Satisfaction Problem (CSP), the user provides positive examples and a Meta-CSP, i.e. a model of a combinatorial problem whose solution is a CSP. This Meta-CSP allows listing the potential constraints that can be part of the CSP the user wants to learn. It also allows stating the parameters of the constraints, such as the coefficients of a linear equation, and imposing constraints over these parameters. The CABSC reads the Meta-CSP using an augmented version of the language MiniZinc and returns the CSP that accepts the fewest solutions among the CSPs accepting all positive examples. This is done using a branch and bound where the bounding mechanism makes use of a model counter. Experiments show that CABSC is successful at learning constraints and their parameters from positive examples.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Modeling methodologies
Keywords
  • Constraint acquisition
  • CSP
  • Model counting
  • Solution counting

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References

  1. Hajar Ait Addi and Redouane Ezzahir. P_a-quacq: Algorithm for constraint acquisition system. In Smart Data and Computational Intelligence, pages 249-256, 2019. Google Scholar
  2. Hajar Ait Addi, Christian Bessiere, Redouane Ezzahir, and Nadjib Lazaar. Time-bounded query generator for constraint acquisition. In Integration of Constraint Programming, Artificial Intelligence, and Operations Research (CPAIOR 2018), pages 1-17, 2018. Google Scholar
  3. Robin Arcangioli and Nadjib Lazaar. Multiple constraint acquisition. In Proceedings of the 2015 International Conference on Constraints and Preferences for Configuration and Recommendation and Intelligent Techniques for Web Personalization, pages 16-20, 2015. Google Scholar
  4. Nicolas Beldiceanu and Évelyne Contejean. Introducing global constraints in chip. Mathematical and Computer Modelling, 20(12):97-123, 1994. Google Scholar
  5. Nicolas Beldiceanu and Helmut Simonis. A constraint seeker: Finding and ranking global constraints from examples. In Proceedings of the 17th International Conference on Principles and Practice of Constraint Programming (CP 2011), pages 12-26, 2011. Google Scholar
  6. Nicolas Beldiceanu and Helmut Simonis. A model seeker: Extracting global constraint models from positive examples. In Proceedings of the 18th International Conference on Principles and Practice of Constraint Programming (CP 2012), pages 141-157, 2012. Google Scholar
  7. Christian Bessiere, Clément Carbonnel, Anton Dries, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Nina Narodytska, Claude-Guy Quimper, Kostas Stergiou, Dimosthenis C. Tsouros, and Toby Walsh. Partial queries for constraint acquisition. Technical Report abs/2003.06649, CoRR, 2020. URL: http://arxiv.org/abs/2003.06649.
  8. Christian Bessiere, Remi Coletta, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Nina Narodytska, Claude-Guy Quimper, and Toby Walsh. Constraint acquisition via partial queries. In Francesca Rossi, editor, Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI-13), pages 475-481, 2013. Google Scholar
  9. Supratik Chakraborty, Kuldeep S. Meel, and Moshe Y. Vardi. Algorithmic improvements in approximate counting for probabilistic inference: From linear to logarithmic sat calls. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pages 3569-3576, 2016. Google Scholar
  10. Abderrazak Daoudi, Younes Mechqrane, Christian Bessiere, Nadjib Lazaar, and El-Houssine Bouyakhf. Constraint acquisition with recommendation queries. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI-16), pages 720-726, 2016. Google Scholar
  11. Niklas Eén and Niklas Sörensson. Translating pseudo-boolean constraints into sat. Journal on Satisfiability, Boolean Modeling and Computation, 2(1-4):1-26, 2006. Google Scholar
  12. Nicholas Nethercote, Peter J. Stuckey, Ralph Becket, Sebastian Brand, Gregory J. Duck, and Guido Tack. Minizinc: Towards a standard cp modelling language. In Proceedings of the 13th International Conference on Principles and Practice of Constraint Programming (CP 2007), pages 529-543, 2007. Google Scholar
  13. Émilie Picard-Cantin, Mathieu Bouchard, Claude-Guy Quimper, and Jason Sweeney. Learning parameters for the sequence constraint from solutions. In Proceedings of the 22nd International Conference on Principles and Practice of Constraint Programming (CP 2016), pages 405-420, 2016. Google Scholar
  14. Émilie Picard-Cantin, Mathieu Bouchard, Claude-Guy Quimper, and Jason Sweeney. Learning the parameters of global constraints using branch-and-bound. In Proceedings of the 23rd International Conference on Principles and Practice of Constraint Programming (CP 2017), pages 512-528, 2017. Google Scholar
  15. Jean-Charles Régin, Mohamed Rezgui, and Arnaud Malapert. Embarrassingly parallel search. In Proceedings of the 19th International Conference on Principles and Practice of Constraint Programming (CP 2013), pages 596-610, 2013. Google Scholar
  16. Francesca Rossi, Peter van Beek, and Toby Walsh, editors. Handbook of Constraint Programming, volume 2 of Foundations of Artificial Intelligence. Elsevier, 2006. Google Scholar
  17. Shubham Sharma, Subhajit Roy, Mate Soos, and Kuldeep S. Meel. Ganak: A scalable probabilistic exact model counter. In Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI-19), pages 1169-1176, 2019. Google Scholar
  18. Mate Soos and Kuldeep S. Meel. Bird: Engineering an efficient cnf-xor sat solver and its applications to approximate model counting. In Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI-19), pages 1592-1599, 2019. Google Scholar
  19. Dimosthenis C. Tsouros, Kostas Stergiou, and Christian Bessiere. Structure-driven multiple constraint acquisition. In Proceedings of the 25th International Conference on Principles and Practice of Constraint Programming (CP 2019), pages 709-725, 2019. Google Scholar
  20. Dimosthenis C. Tsouros, Kostas Stergiou, and Panagiotis G. Sarigiannidis. Efficient methods for constraint acquisition. In Proceedings of the 24th International Conference on Principles and Practice of Constraint Programming (CP 2018), pages 373-388, 2018. Google Scholar
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