Skip to main content

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 5732))

Abstract

Today’s models for propagation-based constraint solvers require propagators as implementations of constraints to be at least contracting and monotonic. These models do not comply with reality: today’s constraint programming systems actually use non-monotonic propagators. This paper introduces the first realistic model of constraint propagation by assuming a propagator to be weakly monotonic (complying with the constraint it implements). Weak monotonicity is shown to be the minimal property that guarantees constraint propagation to be sound and complete. The important insight is that weak monotonicity makes propagation in combination with search well behaved. A case study suggests that non-monotonicity can be seen as an opportunity for more efficient propagation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Baptiste, P., Le Pape, C.: A theoretical and experimental comparison of constraint propagation techniques for disjunctive scheduling. In: IJCAI, pp. 600–606 (1995)

    Google Scholar 

  2. Menana, J., Demassey, S.: Sequencing and counting with the multicost-regular constraint. In: van Hoeve, W.-J., Hooker, J.N. (eds.) CPAIOR 2009. LNCS, vol. 5547, pp. 178–192. Springer, Heidelberg (2009)

    Google Scholar 

  3. Katriel, I.: Expected-case analysis for delayed filtering. In: Beck, J.C., Smith, B.M. (eds.) CPAIOR 2006. LNCS, vol. 3990, pp. 119–125. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  4. Stergiou, K.: Heuristics for dynamically adapting propagation. In: ECAI, pp. 485–489 (2008)

    Google Scholar 

  5. Katriel, I., Van Hentenryck, P.: Randomized filtering algorithms. Technical Report CS-06-09, Brown University, Providence, RI, USA (2006)

    Google Scholar 

  6. Mehta, D., van Dongen, M.R.C.: Probabilistic consistency boosts MAC and SAC. In: IJCAI, pp. 143–148 (2007)

    Google Scholar 

  7. Sellmann, M.: Approximated consistency for Knapsack constraints. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 679–693. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Ward, M.: The closure operators of a lattice. Annals of Mathematics 43(2), 191–196 (1942)

    Article  MathSciNet  MATH  Google Scholar 

  9. Tarski, A.: Fundamentale Begriffe der Methodologie der deduktiven Wissenschaften. I. Monatshefte für Mathematik 37(1), 361–404 (1930)

    Article  MathSciNet  Google Scholar 

  10. Tarski, A.: V. In: Logic, semantics, metamathematics, 2nd edn., pp. 60–109. Hackett Publishing Company (1983)

    Google Scholar 

  11. Saraswat, V.A., Rinard, M.C., Panangaden, P.: Semantic foundations of concurrent constraint programming. In: POPL, pp. 333–352 (1991)

    Google Scholar 

  12. Benhamou, F., McAllester, D.A., Van Hentenryck, P.: CLP(Intervals) revisited. In: ILPS, pp. 124–138. The MIT Press, Cambridge (1994)

    Google Scholar 

  13. Van Hentenryck, P., Saraswat, V.A., Deville, Y.: Constraint processing in cc(FD). Technical report, Brown University (1991)

    Google Scholar 

  14. Apt, K.R.: Principles of Constraint Programming. Cambridge University Press, Cambridge (2003)

    Book  MATH  Google Scholar 

  15. Schulte, C., Stuckey, P.J.: Efficient constraint propagation engines. ACM Trans. Program. Lang. Syst. 31(1), 2:1–2:43 (2008)

    Article  Google Scholar 

  16. Benhamou, F.: Heterogeneous Constraint Solving. In: Hanus, M., Rodríguez-Artalejo, M. (eds.) ALP 1996. LNCS, vol. 1139, pp. 62–76. Springer, Heidelberg (1996)

    Chapter  Google Scholar 

  17. Müller, T.: Constraint Propagation in Mozart. Doctoral dissertation, Universität des Saarlandes, Saarbrücken, Germany (2001)

    Google Scholar 

  18. Schulte, C.: Programming Constraint Services. LNCS (LNAI), vol. 2302. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  19. Perron, L.: Search procedures and parallelism in constraint programming. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 346–361. Springer, Heidelberg (1999)

    Google Scholar 

  20. Choi, C.W., Henz, M., Ng, K.B.: Components for state restoration in tree search. In: Walsh, T. (ed.) CP 2001. LNCS, vol. 2239, pp. 240–255. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  21. Michel, L., Van Hentenryck, P.: A decomposition-based implementation of search strategies. ACM Trans. Comput. Logic 5(2), 351–383 (2004)

    Article  MathSciNet  Google Scholar 

  22. Carlsson, M.: Personal communication (February 2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2009 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Schulte, C., Tack, G. (2009). Weakly Monotonic Propagators. In: Gent, I.P. (eds) Principles and Practice of Constraint Programming - CP 2009. CP 2009. Lecture Notes in Computer Science, vol 5732. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04244-7_56

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-04244-7_56

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-04243-0

  • Online ISBN: 978-3-642-04244-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics