Time-Bounded Query Generator for Constraint Acquisition

  • Hajar Ait Addi
  • Christian Bessiere
  • Redouane Ezzahir
  • Nadjib LazaarEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10848)


QuAcq is a constraint acquisition algorithm that assists a non-expert user to model her problem as a constraint network. QuAcq generates queries as examples to be classified as positive or negative. One of the drawbacks of QuAcq is that generating queries can be time-consuming. In this paper we present Tq-gen, a time-bounded query generator. Tq-gen is able to generate a query in a bounded amount of time. We rewrite QuAcq to incorporate the Tq-gen generator. This leads to a new algorithm called T-quacq. We propose several strategies to make T-quacq efficient. Our experimental analysis shows that thanks to the use of Tq-gen, T-quacq dramatically improves the basic QuAcq in terms of time consumption, and sometimes also in terms of number of queries.


  1. 1.
    Arcangioli, R., Bessiere, C., Lazaar, N.: Multiple constraint acquisition. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, pp. 698–704 (2016)Google Scholar
  2. 2.
    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
  3. 3.
    Bessiere, C., Coletta, R., Hebrard, E., Katsirelos, G., Lazaar, N., Narodytska, N., Quimper, C., Walsh, T.: Constraint acquisition via partial queries. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence, IJCAI 2013, Beijing, China, pp. 475–481 (2013)Google Scholar
  4. 4.
    Bessiere, C., Coletta, R., O’Sullivan, B., Paulin, M.: Query-driven constraint acquisition. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence, IJCAI 2007, Hyderabad, India, pp. 50–55 (2007)Google Scholar
  5. 5.
    Bessiere, C., et al.: New approaches to constraint acquisition. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O’Sullivan, B., Pedreschi, D. (eds.) Data Mining and Constraint Programming. LNCS (LNAI), vol. 10101, pp. 51–76. Springer, Cham (2016). Scholar
  6. 6.
    Bessiere, C., Lazaar, N., Koriche, F., O’Sullivan, B.: Constraint acquisition. In: Artificial Intelligence (2017, in Press)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Freuder, E.C., Wallace, R.J.: Suggestion strategies for constraint-based matchmaker agents. Int. J. Artif. Intell. Tools 11(1), 3–18 (2002)CrossRefGoogle Scholar
  8. 8.
    Jefferson, C., Akgun, O.: CSPLib: a problem library for constraints (1999).
  9. 9.
    Lallemand, C., Gronier, G.: Enhancing user experience during waiting time in HCI: contributions of cognitive psychology. In: Proceedings of the Designing Interactive Systems Conference, DIS 2012, pp. 751–760. ACM, New York (2012).
  10. 10.
    Lallouet, A., Lopez, M., Martin, L., Vrain, C.: On learning constraint problems. In: Proceedings of the 22nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2010, Arras, France, pp. 45–52 (2010)Google Scholar
  11. 11.
    Petrie, K.E., Smith, B.M.: Symmetry breaking in graceful graphs. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 930–934. Springer, Heidelberg (2003). Scholar
  12. 12.
    Shchekotykhin, K.M., Friedrich, G.: Argumentation based constraint acquisition. In: Proceedings of the Ninth IEEE International Conference on Data Mining, ICDM 2009, Miami, FL, pp. 476–482 (2009)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Hajar Ait Addi
    • 1
  • Christian Bessiere
    • 2
  • Redouane Ezzahir
    • 1
  • Nadjib Lazaar
    • 2
    Email author
  1. 1.LISTI/ENSAUniversity of Ibn ZohrAgadirMorocco
  2. 2.LIRMM, University of Montpellier, CNRSMontpellierFrance

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