Effective Modeling with Constraints

  • Roman Barták
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3392)


Constraint programming provides a declarative approach to solving combinatorial (optimization) problems. The user just states the problem as a constraint satisfaction problem (CSP) and a generic solver finds a solution without additional programming. However, in practice, the situation is more complicated because there usually exist several ways how to model the problem as a CSP, that is using variables, their domains, and constraints. In fact, different constraint models may lead to significantly different running times of the solver so constraint modeling is a crucial part of problem solving. This paper describes some known approaches to efficient modeling with constraints in a tutorial-like form. The primary audience is practitioners, especially in logic programming, that would like to use constraints in their projects but do not have yet deep knowledge of constraint satisfaction techniques.


Assignment Problem Constraint Satisfaction Constraint Programming Constraint Satisfaction Problem Global Constraint 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Baptiste, P., Le Pape, C.: Edge-finding constraint propagation algorithms for disjunctive and cumulative scheduling. In: Proceedings of the Fifteenth Workshop of the U.K. Planning Special Interest Group (1996)Google Scholar
  2. 2.
    Barták, R.: On-line Guide to Constraint Programming, Prague (1998), http://kti.mff.cuni.cz/~bartak/constraints/
  3. 3.
    Carlsson, M., Ottosson, G., Carlsson, B.: An Open-Ended Finite Domain Constraint Solver. In: Hartel, P.H., Kuchen, H. (eds.) PLILP 1997. LNCS, vol. 1292. Springer, Heidelberg (1997)CrossRefGoogle Scholar
  4. 4.
    Freuder, E.C.: In Pursuit of the Holy Grail. Constraints: An International Journal 2, 57–61 (1997)CrossRefGoogle Scholar
  5. 5.
    Golomb rulers: some results (2003), http://www.research.ibm.com/people/s/shearer/grtab.html
  6. 6.
    Kumar, V.: Algorithms for Constraint Satisfaction Problems: A Survey. AI Magazine 13(1), 32–44 (1992)Google Scholar
  7. 7.
    Mariot, K., Stuckey, P.J.: Programming with Constraints: An Introduction. The MIT Press, Cambridge (1998)Google Scholar
  8. 8.
    Régin, J.-C.: A filtering algorithm for constraints of difference in CSPs. In: Proceedings of 12th National Conference on Artificial Intelligence (1994)Google Scholar
  9. 9.
    SICStus Prolog 3.11.2 User’s Manual Google Scholar
  10. 10.
    Smith, B.: Reducing Symmetry in a Combinatorial Design Problem. In: Proceedings of CP-AI-OR 2001, pp. 351–359. Wye College, UK (2001)Google Scholar
  11. 11.
    Tsang, E.: Foundations of Constraint Satisfaction. Academic Press, London (1995)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Roman Barták
    • 1
  1. 1.Faculty of Mathematics and Physics, Institute for Theoretical Computer ScienceCharles UniversityPragueCzech Republic

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