Efficient Methods for Constraint Acquisition

  • Dimosthenis C. TsourosEmail author
  • Kostas Stergiou
  • Panagiotis G. Sarigiannidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11008)


Constraint acquisition systems such as QuAcq and MultiAcq can assist non-expert users to model their problems as constraint networks by classifying (partial) examples as positive or negative. For each negative example, the former focuses on one constraint of the target network, while the latter can learn a maximum number of constraints. Two bottlenecks of the acquisition process where both these algorithms encounter problems are the large number of queries required to reach convergence, and the high cpu times needed to generate queries, especially near convergence. We propose methods that deal with both these issues. The first one is an algorithm that blends the main idea of MultiAcq into QuAcq resulting in a method that learns as many constraints as MultiAcq does after a negative example, but with a lower complexity. The second is a technique that helps reduce the number of queries significantly. The third is based on the use of partial queries to cut down the time required for convergence. Experiments demonstrate that our resulting algorithm, which integrates all the new techniques, does not only generate considerably fewer queries than QuAcq and MultiAcq, but it is also by far faster than both of them, both in average query generation time and in total run time.


Constraint acquisition Learning Modeling 


  1. 1.
    Freuder, E.C.: Modeling: the final frontier. In: The First International Conference on The Practical Application of Constraint Technologies and Logic Programming (PACLP), London, pp. 15–21 (1999)Google Scholar
  2. 2.
    De Raedt, L., Passerini, A., Teso, S.: Learning constraints from examples. In: Proceedings in Thirty-Second AAAI Conference on Artificial Intelligence (2018)Google Scholar
  3. 3.
    Bessiere, C., Koriche, F., Lazaar, N., O’Sullivan, B.: Constraint acquisition. Artif. Intell. 244, 315–342 (2017)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Bessiere, C., Coletta, R., Freuder, E.C., O’Sullivan, B.: Leveraging the learning power of examples in automated constraint acquisition. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 123–137. Springer, Heidelberg (2004). Scholar
  5. 5.
    Bessiere, C., Coletta, R., Koriche, F., O’Sullivan, B.: A SAT-based version space algorithm for acquiring constraint satisfaction problems. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 23–34. Springer, Heidelberg (2005). Scholar
  6. 6.
    Lallouet, A., Lopez, M., Martin, L., Vrain, C.: On learning constraint problems. In: 2010 22nd IEEE International Conference on Tools with Artificial Intelligence (ICTAI), vol. 1, pp. 45–52. IEEE (2010)Google Scholar
  7. 7.
    Beldiceanu, N., Simonis, H.: A model seeker: extracting global constraint models from positive examples. In: Milano, M. (ed.) CP 2012. LNCS, pp. 141–157. Springer, Heidelberg (2012). Scholar
  8. 8.
    Freuder, E.C., Wallace, R.J.: Suggestion strategies for constraint-based matchmaker agents. In: Maher, M., Puget, J.-F. (eds.) CP 1998. LNCS, vol. 1520, pp. 192–204. Springer, Heidelberg (1998). Scholar
  9. 9.
    Bessiere, C., Coletta, R., O’Sullivan, B., Paulin, M., et al.: Query-driven constraint acquisition. IJCAI 7, 50–55 (2007)Google Scholar
  10. 10.
    Angluin, D.: Queries and concept learning. Mach. Learn. 2(4), 319–342 (1988)MathSciNetGoogle Scholar
  11. 11.
    Bessiere, C., Coletta, R., Hebrard, E., Katsirelos, G., Lazaar, N., Narodytska, N., Quimper, C.G., Walsh, T., et al.: Constraint acquisition via partial queries. IJCAI 13, 475–481 (2013)Google Scholar
  12. 12.
    Arcangioli, R., Bessiere, C., Lazaar, N.: Multiple constraint aquisition. In: IJCAI: International Joint Conference on Artificial Intelligence, pp. 698–704 (2016)Google Scholar
  13. 13.
    Paulin, M., Bessiere, C., Sallantin, J.: Automatic design of robot behaviors through constraint network acquisition. In: 20th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2008, vol. 1, pp. 275–282. IEEE (2008)Google Scholar
  14. 14.
    Bessiere, C., Koriche, F.: Non learnability of constraint networks with membership queries. Technical report, Technical report, Coconut, Montpellier, France (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Dimosthenis C. Tsouros
    • 1
    Email author
  • Kostas Stergiou
    • 1
  • Panagiotis G. Sarigiannidis
    • 1
  1. 1.Department of Informatics and Telecommunications EngineeringUniversity of Western MacedoniaKozaniGreece

Personalised recommendations