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ModelSeeker: Extracting Global Constraint Models from Positive Examples

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Data Mining and Constraint Programming

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10101))

Abstract

We describe a system which generates finite domain constraint models from positive example solutions, for highly structured problems. The system is based on the global constraint catalog, providing the library of constraints that can be used in modeling, and the Constraint Seeker tool, which finds a ranked list of matching constraints given one or more sample call patterns.

We have tested the modeler with 230 examples, ranging from 4 to 6,500 variables, using between 1 and 7,000 samples. These examples come from a variety of domains, including puzzles, sports-scheduling, packing & placement, and design theory. When comparing against manually specified “canonical” models for the examples, we achieve a hit rate of 50%, processing the complete benchmark set in less than one hour on a laptop. Surprisingly, in many cases the system finds usable candidate lists even when working with a single, positive example.

The second author is supported by EU FET grant ICON (project number 284715).

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Notes

  1. 1.

    This chapter is an extended version of reference [8].

References

  1. Akgun, O., Frisch, A.M., Gent, I.P., Hussain, B.S., Jefferson, C., Kotthoff, L., Miguel, I., Nightingale, P.: Automated symmetry breaking and model selection in Conjure. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 107–116. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40627-0_11

    Chapter  Google Scholar 

  2. Beldiceanu, N., Carlsson, M., Rampon, J.: Global constraint catalog, 2nd edn. (revision a). Technical report T2012:03, SICS (2012)

    Google Scholar 

  3. Beldiceanu, N., Simonis, H.: A constraint seeker: finding and ranking global constraints from examples. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 12–26. Springer, Heidelberg (2011). doi:10.1007/978-3-642-23786-7_4

    Google Scholar 

  4. Beldiceanu, N., Simonis, H.: Using the global constraint seeker for learning structured constraint models: a first attempt. In: The 10th International Workshop on Constraint Modelling and Reformulation (ModRef 2011), Perugia, Italy, pp. 20–34, September 2011

    Google Scholar 

  5. Beldiceanu, N., Carlsson, M., Douence, R., Simonis, H.: Using finite transducers for describing and synthesising structural time-series constraints. Constraints 21(1), 22–40 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  6. Beldiceanu, N., Carlsson, M., Flener, P., Pearson, J.: On the reification of global constraints. Constraints 18(1), 1–6 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Beldiceanu, N., Ifrim, G., Lenoir, A., Simonis, H.: Describing and generating solutions for the EDF unit commitment problem with the ModelSeeker. In: Schulte, C. (ed.) Principles and Practice of Constraint Programming. LNCS, vol. 8124, pp. 733–748. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40627-0_54

    Chapter  Google Scholar 

  8. Beldiceanu, N., Simonis, H.: A model seeker: extracting global constraint models from positive examples. In: Milano, M. (ed.) CP 2012. Lecture Notes in Computer Science, vol. 7514, pp. 141–157. Springer, Heidelberg (2012). doi:10.1007/978-3-642-33558-7_13

    Chapter  Google Scholar 

  9. Berlekamp, E.R., Conway, J.H., Guy, R.K.: Winning Ways for Your Mathematical Plays, vol. 4, 2nd edn. A K Peters/CRC Press, Natick (2004)

    MATH  Google Scholar 

  10. Bessière, 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). doi:10.1007/978-3-540-30201-8_12

    Google Scholar 

  11. 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). doi:10.1007/11564096_8

    Chapter  Google Scholar 

  12. Bessiere, C., Coletta, R., Petit, T.: Acquiring parameters of implied global constraints. In: Beek, P. (ed.) CP 2005. LNCS, vol. 3709, pp. 747–751. Springer, Heidelberg (2005). doi:10.1007/11564751_57

    Chapter  Google Scholar 

  13. Bose, R.C., Shrikhande, S.S., Parker, E.T.: Further results on the construction of mutually orthogonal latin squares and the falsity of Euler’s conjecture. Can. J. Math. 12, 189–203 (1960)

    Article  MathSciNet  MATH  Google Scholar 

  14. Carlier, J., Pinson, E.: An algorithm for solving the job shop problem. Manag. Sci. 35, 164–176 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  15. Charnley, J., Colton, S., Miguel, I.: Automatic generation of implied constraints. In: Brewka, G., Coradeschi, S., Perini, A., Traverso, P. (eds.) ECAI. Frontiers in Artificial Intelligence and Applications, vol. 141, pp. 73–77. IOS Press, Amsterdam (2006)

    Google Scholar 

  16. Drakakis, K.: A review of Costas arrays. J. Appl. Math. 2006, 1–32 (2006)

    Article  MathSciNet  MATH  Google Scholar 

  17. Dudeney, H.E.: Amusements in Mathematics. Dover, New York (1917)

    Google Scholar 

  18. Flener, P., Frisch, A., Hnich, B., Kiziltan, Z., Miguel, I., Walsh, T.: Matrix modelling. Technical report 2001–023, Department of Information Technology, Uppsala University, September 2001

    Google Scholar 

  19. Haynes, T.W., Hedetniemi, S.T., Slater, P.J.: Fundamentals of Domination in Graphs. Monographs and Textbooks in Pure and Applied Mathematics. Marcel Dekker, New York (1998)

    MATH  Google Scholar 

  20. Van Hentenryck, P., Michel, L.: Constraint-Based Local Search. MIT Press, Boston (2005)

    MATH  Google Scholar 

  21. Henz, M.: Scheduling a major college basketball conference - revisited. Oper. Res. 49, 163–168 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  22. Henz, M., Müller, T., Thiel, S.: Global constraints for round robin tournament scheduling. Eur. J. Oper. Res. 153(1), 92–101 (2004)

    Article  MathSciNet  MATH  Google Scholar 

  23. Hernández, B.M.: The systematic generation of channelled models in constraint satisfaction. PhD thesis, University of York, York, YO10 5DD, UK, Department of Computer Science (2007)

    Google Scholar 

  24. Hooker, J.N.: Integrated Methods for Optimization. Springer Science + Business Media LLC, New York (2007)

    MATH  Google Scholar 

  25. Lallouet, A., Lopez, M., Martin, L., Vrain, C.: On learning constraint problems. In: ICTAI, vol. 1, pp. 45–52. IEEE Computer Society (2010)

    Google Scholar 

  26. Leo, K., Mears, C., Tack, G., Garcia de la Banda, M.: Globalizing constraint models. In: Schulte, C. (ed.) CP 2013. LNCS, vol. 8124, pp. 432–447. Springer, Heidelberg (2013). doi:10.1007/978-3-642-40627-0_34

    Chapter  Google Scholar 

  27. Maher, M.J.: Open constraints in a boundable world. In: van Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 163–177. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  28. Marriott, K., Nethercote, N., Rafeh, R., Stuckey, P.J., de la Banda, M.G., Wallace, M.: The design of the Zinc modelling language. Constraints 13(3), 229–267 (2008)

    Article  MathSciNet  MATH  Google Scholar 

  29. Nemhauser, G., Trick, M.: Scheduling a major college basketball conference. Oper. Res. 46, 1–8 (1998)

    Article  Google Scholar 

  30. O’Sullivan, B.: Automated modelling and solving in constraint programming. In: Fox, M., Poole, D. (eds.) AAAI, pp. 1493–1497. AAAI Press, Palo Alto (2010)

    Google Scholar 

  31. Petkovic, M.S.: Famous Puzzles of Great Mathematicians. American Mathematical Society, Providence (2009)

    Book  MATH  Google Scholar 

  32. Razakarison, N., Carlsson, M., Beldiceanu, N., Simonis, H.: GAC for a linear inequality and an atleast constraint with an application to learning simple polynomials. In: Helmert, M., Röger, G. (eds.) Proceedings of the Sixth Annual Symposium on Combinatorial Search, SOCS 2013, Leavenworth, Washington, USA, 11–13 July 2013. AAAI Press (2013)

    Google Scholar 

  33. Roussel, O., Lecoutre, C.: XML representation of constraint networks format XCSP 2.1. Technical report arXiv:0902.2362v1, Universite Lille-Nord de France, Artois (2009)

  34. Schreuder, J.A.M.: Combinatorial aspects of construction of competition Dutch professional football leagues. Discret. Appl. Math. 35(3), 301–312 (1992)

    Article  MATH  Google Scholar 

  35. Smith, B.M., Brailsford, S.C., Hubbard, P.M., Williams, H.P.: The progressive party problem: integer linear programming and constraint programming compared. Constraints 1(1/2), 119–138 (1996)

    Article  MathSciNet  Google Scholar 

  36. Walser, J.P.: Domain-independent local search for linear integer optimization. PhD thesis, Technical Faculty of the University des Saarlandes, Saarbruecken, Germany, October 1998

    Google Scholar 

  37. Watkins, J.J.: Across the Board: The Mathematics of Chessboard Problems. Princeton University Press, Princeton (2004)

    Book  MATH  Google Scholar 

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Acknowledgement

The help of Hakan Kjellerstrand in finding example problems is gratefully acknowledged.

Author information

Authors and Affiliations

  1. TASC Team (INRIA/CNRS), Mines Nantes, 44307, Nantes, France

    Nicolas Beldiceanu

  2. Insight Centre for Data Analytics, Department of Computer Science, University College Cork, Cork, Ireland

    Helmut Simonis

Authors
  1. Nicolas Beldiceanu
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  2. Helmut Simonis
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Correspondence to Helmut Simonis .

Editor information

Editors and Affiliations

  1. Université Montpellier 2, Montpellier, France

    Christian Bessiere

  2. KU Leuven, Heverlee, Belgium

    Luc De Raedt

  3. University of British Columbia, Vancouver, British Columbia, Canada

    Lars Kotthoff

  4. Université Catholique de Louvain, Louvain-la-Neuve, Belgium

    Siegfried Nijssen

  5. University College Cork, Cork, Ireland

    Barry O'Sullivan

  6. University of Pisa, Pisa, Italy

    Dino Pedreschi

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Beldiceanu, N., Simonis, H. (2016). ModelSeeker: Extracting Global Constraint Models from Positive Examples. In: Bessiere, C., De Raedt, L., Kotthoff, L., Nijssen, S., O'Sullivan, B., Pedreschi, D. (eds) Data Mining and Constraint Programming. Lecture Notes in Computer Science(), vol 10101. Springer, Cham. https://doi.org/10.1007/978-3-319-50137-6_4

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  • DOI: https://doi.org/10.1007/978-3-319-50137-6_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50136-9

  • Online ISBN: 978-3-319-50137-6

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