Propagation in CSP and SAT

  • Yannis Dimopoulos
  • Kostas Stergiou
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4204)


Constraint Satisfaction Problems and Propositional Satisfiability, are frameworks widely used to represent and solve combinatorial problems. A concept of primary importance in both frameworks is that of constraint propagation. In this paper we study and compare the amount of propagation that can be achieved, using various methods, when translating a problem from one framework into another. Our results complement, extend, and tie together recent similar studies. We provide insight as to which translation is preferable, with respect to the strength of propagation in the original problem and the encodings.


Constraint Satisfaction Problem Propositional Variable Unit Clause Propositional Theory Direct Encode 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Bacchus, F.: Enhancing Davis Putnam with Extended Binary Clause Reasoning. In: Proceedings of AAAI 2002, pp. 613–619 (2002)Google Scholar
  2. 2.
    Bennaceur, H.: The satisfiability problem regarded as a constraint satisfaction problem. In: Proceedings of ECAI 1996, pp. 125–130 (1996)Google Scholar
  3. 3.
    Bennaceur, H.: A Comparison between SAT and CSP Techniques. Constraints 9, 123–138 (2004)MATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Bessière, C., Hebrard, E., Walsh, T.: Local consistencies in SAT. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 299–314. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  5. 5.
    Debruyne, R., Bessière, C.: Domain Filtering Consistencies. Journal of Artificial Intelligence Research 14, 205–230 (2001)MATHMathSciNetGoogle Scholar
  6. 6.
    Freeman, J.W.: Improvements to Propositional Satisfiability Search Algorithms. Ph.D thesis (1995)Google Scholar
  7. 7.
    Freuder, E.: A Sufficient Condition for Backtrack-bounded Search. JACM 32(4), 755–761 (1985)MATHCrossRefMathSciNetGoogle Scholar
  8. 8.
    Freuder, E., Elfe, C.: Neighborhood Inverse Consistency Preprocessing. In: Proceedings of AAAI 1996, pp. 202–208 (1996)Google Scholar
  9. 9.
    Gent, I.: Arc Consistency in SAT. In: Proceedings of ECAI 2002, pp. 121–125 (2002)Google Scholar
  10. 10.
    Kasif, S.: On the Parallel Complexity of Discrete Relaxation in Constraint Satisfaction Networks. Artificial Intelligence 45(3), 275–286 (1990)MATHCrossRefMathSciNetGoogle Scholar
  11. 11.
    Li, C.M., Anbulagan: Heuristics based on unit propagation for satisfiability problems. In: Proceedings of IJCAI 1997, pp. 366–371 (1997)Google Scholar
  12. 12.
    Prestwich, S.D.: Full dynamic substitutability by SAT encoding. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 512–526. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    van Gelder, A., Tsuji, Y.: Satisfiability testing with more reasoning and less guessing. In: Cliques, Coloring and Satisfiability, pp. 559–586 (1996)Google Scholar
  14. 14.
    Walsh, T.: SAT v CSP. In: Dechter, R. (ed.) CP 2000. LNCS, vol. 1894, pp. 441–456. Springer, Heidelberg (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Yannis Dimopoulos
    • 1
  • Kostas Stergiou
    • 2
  1. 1.Department of Computer ScienceUniversity of CyprusNicosiaCyprus
  2. 2.Department of Information and Communication Systems EngineeringUniversity of the AegeanSamosGreece

Personalised recommendations