A CSP Search Algorithm with Reduced Branching Factor

  • Igor Razgon
  • Amnon Meisels
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3978)


This paper presents an attempt to construct a ”practical” CSP algorithm that assigns a variable with 2 values at every step. Such a strategy has been successfully used for construction of ”theoretical” constraint solvers because it decreases twice the base of the exponent of the upper bound of the search algorithm.

We present a solver based on the strategy. The pruning mechanism of the algorithm resembles Forward Checking (FC), therefore we term it 2FC. According to our experimental evaluation, 2FC outperforms FC on graph coloring problems and on non-dense instances of randomly generated CSPs.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Jeavons, P., Cohen, D., Cooper, M.: Constraints, consistency, and closure. Artificial Intelligence 101, 251–265 (1998)CrossRefzbMATHGoogle Scholar
  2. 2.
    Eppstein, D.: Improved algorithms for 3-coloring, 3-edge coloring and constraint satisfaction. In: SODA 2001, pp. 329–337 (2001)Google Scholar
  3. 3.
    Angelsmark, O., Jonsson, P.: Improved algorithms for counting solutions in constraint satisfaction problems. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 81–95. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  4. 4.
    Dechter, R.: Constraint Processing. Morgan Kaufmann Publishers, San Francisco (2003)zbMATHGoogle Scholar
  5. 5.
    Haralick, R.M., Elliott, G.: Increasing tree search efficiency for constraint satisfaction problems. Artificial Intelligence 14, 263–313 (1980)CrossRefGoogle Scholar
  6. 6.
    Prosser, P.: An empirical study of phase transition in binary constraint satisfaction problems. Artificial Intelligence 81, 81–109 (1996)CrossRefGoogle Scholar
  7. 7.
    Caramia, M., Dell’Olmo, P.: Constraint propagation in graph coloring. Journal of Heuristics 8, 83–107 (2002)CrossRefzbMATHGoogle Scholar
  8. 8.
    Meisels, A., Schaerf, A.: Modelling and solving employee timetabling problems. Annals of Mathematics and Artificial Intelligence 39, 41–59 (2003)CrossRefzbMATHGoogle Scholar
  9. 9.
    Dechter, R.: Bucket elimination: A unifying framework for reasoning. Artificial Intelligence 113, 41–85 (1999)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Igor Razgon
    • 1
  • Amnon Meisels
    • 1
  1. 1.Department of Computer ScienceBen-Gurion University of the NegevBeer-ShevaIsrael

Personalised recommendations