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Guided Bottom-Up Interactive Constraint Acquisition

Authors Dimosthenis C. Tsouros , Senne Berden , Tias Guns

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Author Details

Dimosthenis C. Tsouros
  • KU Leuven, Belgium
Senne Berden
  • KU Leuven, Belgium
Tias Guns
  • KU Leuven, Belgium

Funding

This research received funding from the European Research Council (ERC) under the EU Horizon 2020 research and innovation programme (Grant No 101002802, CHAT-Opt) and from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101070149, project Tuples

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Dimosthenis C. Tsouros, Senne Berden, and Tias Guns. Guided Bottom-Up Interactive Constraint Acquisition. In 29th International Conference on Principles and Practice of Constraint Programming (CP 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 280, pp. 36:1-36:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)
https://doi.org/10.4230/LIPIcs.CP.2023.36

Abstract

Constraint Acquisition (CA) systems can be used to assist in the modeling of constraint satisfaction problems. In (inter)active CA, the system is given a set of candidate constraints and posts queries to the user with the goal of finding the right constraints among the candidates. Current interactive CA algorithms suffer from at least two major bottlenecks. First, in order to converge, they require a large number of queries to be asked to the user. Second, they cannot handle large sets of candidate constraints, since these lead to large waiting times for the user. For this reason, the user must have fairly precise knowledge about what constraints the system should consider. In this paper, we alleviate these bottlenecks by presenting two novel methods that improve the efficiency of CA. First, we introduce a bottom-up approach named GrowAcq that reduces the maximum waiting time for the user and allows the system to handle much larger sets of candidate constraints. It also reduces the total number of queries for problems in which the target constraint network is not sparse. Second, we propose a probability-based method to guide query generation and show that it can significantly reduce the number of queries required to converge. We also propose a new technique that allows the use of openly accessible CP solvers in query generation, removing the dependency of existing methods on less well-maintained custom solvers that are not publicly available. Experimental results show that our proposed methods outperform state-of-the-art CA methods, reducing the number of queries by up to 60%. Our methods work well even in cases where the set of candidate constraints is 50 times larger than the ones commonly used in the literature.

Subject Classification

ACM Subject Classification
  • Computing methodologies → Discrete space search
  • Computing methodologies → Supervised learning by classification
  • Theory of computation → Constraint and logic programming
  • Computing methodologies → Active learning settings
Keywords
  • Constraint acquisition
  • Constraint learning
  • Active learning
  • Modelling

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References

  1. Hajar Ait Addi, Christian Bessiere, Redouane Ezzahir, and Nadjib Lazaar. Time-bounded query generator for constraint acquisition. In International Conference on the Integration of Constraint Programming, Artificial Intelligence, and Operations Research, pages 1-17. Springer, 2018. Google Scholar
  2. Dana Angluin. Queries and concept learning. Machine learning, 2(4):319-342, 1988. Google Scholar
  3. Robin Arcangioli, Christian Bessiere, and Nadjib Lazaar. Multiple constraint aquisition. In IJCAI: International Joint Conference on Artificial Intelligence, pages 698-704, 2016. Google Scholar
  4. Nicolas Beldiceanu and Helmut Simonis. A model seeker: Extracting global constraint models from positive examples. In Principles and practice of constraint programming, pages 141-157. Springer, 2012. Google Scholar
  5. Senne Berden, Mohit Kumar, Samuel Kolb, and Tias Guns. Learning max-sat models from examples using genetic algorithms and knowledge compilation. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022), 2022. Google Scholar
  6. Christian Bessiere, Clement Carbonnel, Anton Dries, Emmanuel Hebrard, George Katsirelos, Nina Narodytska, Claude-Guy Quimper, Kostas Stergiou, Dimosthenis C Tsouros, and Toby Walsh. Learning constraints through partial queries. Artificial Intelligence, 319:103896, 2023. Google Scholar
  7. Christian Bessiere, Remi Coletta, Eugene C Freuder, and Barry O’Sullivan. Leveraging the learning power of examples in automated constraint acquisition. In International Conference on Principles and Practice of Constraint Programming, pages 123-137. Springer, 2004. Google Scholar
  8. Christian Bessiere, Remi Coletta, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Nina Narodytska, Claude-Guy Quimper, Toby Walsh, et al. Constraint acquisition via partial queries. In IJCAI, volume 13, pages 475-481, 2013. Google Scholar
  9. Christian Bessiere, Remi Coletta, Frédéric Koriche, and Barry O’Sullivan. A sat-based version space algorithm for acquiring constraint satisfaction problems. In European Conference on Machine Learning, pages 23-34. Springer, 2005. Google Scholar
  10. Christian Bessiere, Remi Coletta, Barry O'Sullivan, Mathias Paulin, et al. Query-driven constraint acquisition. In IJCAI, volume 7, pages 50-55, 2007. Google Scholar
  11. Christian Bessiere, Frédéric Koriche, Nadjib Lazaar, and Barry O'Sullivan. Constraint acquisition. Artificial Intelligence, 244:315-342, 2017. Google Scholar
  12. Eugene C Freuder. Modeling: the final frontier. In The First International Conference on The Practical Application of Constraint Technologies and Logic Programming (PACLP), London, pages 15-21, 1999. Google Scholar
  13. Eugene C Freuder. Progress towards the holy grail. Constraints, 23(2):158-171, 2018. Google Scholar
  14. Eugene C Freuder and Barry O’Sullivan. Grand challenges for constraint programming. Constraints, 19(2):150-162, 2014. Google Scholar
  15. Eugene C Freuder and Richard J Wallace. Suggestion strategies for constraint-based matchmaker agents. In International Conference on Principles and Practice of Constraint Programming, pages 192-204. Springer, 1998. Google Scholar
  16. Tias Guns. Increasing modeling language convenience with a universal n-dimensional array, cppy as python-embedded example. In Proceedings of the 18th workshop on Constraint Modelling and Reformulation at CP (Modref 2019), volume 19, 2019. Google Scholar
  17. Mohit Kumar et al. Acquiring integer programs from data. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. IJCAI, 2019. Google Scholar
  18. Mohit Kumar, Samuel Kolb, and Tias Guns. Learning constraint programming models from data using generate-and-aggregate. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2022. Google Scholar
  19. Arnaud Lallouet, Matthieu Lopez, Lionel Martin, and Christel Vrain. On learning constraint problems. In Tools with Artificial Intelligence (ICTAI), 2010 22nd IEEE International Conference on, volume 1, pages 45-52. IEEE, 2010. Google Scholar
  20. Nadjib Lazaar. Parallel constraint acquisition. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, pages 3860-3867, 2021. Google Scholar
  21. Michele Lombardi, Michela Milano, and Andrea Bartolini. Empirical decision model learning. Artificial Intelligence, 244:343-367, 2017. Google Scholar
  22. Steven D Prestwich. Robust constraint acquisition by sequential analysis. Frontiers in Artificial Intelligence and Applications, 325:355-362, 2020. Google Scholar
  23. Steven D Prestwich, Eugene C Freuder, Barry O'Sullivan, and David Browne. Classifier-based constraint acquisition. Annals of Mathematics and Artificial Intelligence, pages 1-20, 2021. Google Scholar
  24. Kostyantyn Shchekotykhin and Gerhard Friedrich. Argumentation based constraint acquisition. In Data Mining, 2009. ICDM'09. Ninth IEEE International Conference on, pages 476-482. IEEE, 2009. Google Scholar
  25. Dimosthenis C Tsouros and Kostas Stergiou. Efficient multiple constraint acquisition. Constraints, 25(3):180-225, 2020. Google Scholar
  26. Dimosthenis C Tsouros and Kostas Stergiou. Learning max-csps via active constraint acquisition. In 27th International Conference on Principles and Practice of Constraint Programming (CP 2021). Schloss Dagstuhl-Leibniz-Zentrum für Informatik, 2021. Google Scholar
  27. Dimosthenis C Tsouros, Kostas Stergiou, and Christian Bessiere. Structure-driven multiple constraint acquisition. In International Conference on Principles and Practice of Constraint Programming, pages 709-725. Springer, 2019. Google Scholar
  28. Dimosthenis C Tsouros, Kostas Stergiou, and Christian Bessiere. Omissions in constraint acquisition. In International Conference on Principles and Practice of Constraint Programming, pages 935-951. Springer, 2020. Google Scholar
  29. Dimosthenis C. Tsouros, Kostas Stergiou, and Panagiotis G. Sarigiannidis. Efficient methods for constraint acquisition. In 24th International Conference on Principles and Practice of Constraint Programming, 2018. Google Scholar
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