UBCSAT: An Implementation and Experimentation Environment for SLS Algorithms for SAT and MAX-SAT

  • Dave A. D. Tompkins
  • Holger H. Hoos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3542)


In this paper we introduce UBCSAT, a new implementation and experimentation environment for Stochastic Local Search (SLS) algorithms for SAT and MAX-SAT. Based on a novel triggered procedure architecture, UBCSAT provides implementations of numerous well-known and widely used SLS algorithms for SAT and MAX-SAT, including GSAT, WalkSAT, and SAPS; these implementations generally match or exceed the efficiency of the respective original reference implementations. Through numerous reporting and statistical features, including the measurement of run-time distributions, UBCSAT facilitates the advanced empirical analysis of these algorithms. New algorithm variants, SLS algorithms, and reporting features can be added to UBCSAT in a straightforward and efficient way. UBCSAT is implemented in C and runs on numerous platforms and operating systems; it is publicly and freely available at www.satlib.org/ubcsat.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Audemard, G., Le Berre, D., Roussel, O., Lynce, I., Marques-Silva, J.: OpenSAT: an open source SAT software project. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–509. Springer, Heidelberg (2004)Google Scholar
  2. 2.
    Fukunaga, A.: Efficient implementations of SAT local search. In: Hoos, H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542, pp. 287–292. Springer, Heidelberg (2005)Google Scholar
  3. 3.
    Gent, I.P., Walsh, T.: Towards an understanding of hill–climbing procedures for SAT. In: Proc. of the Eleventh Nat’l Conf. on Artificial Intelligence (AAAI 1993), pp. 28–33 (1993)Google Scholar
  4. 4.
    Gent, I.P., Walsh, T.: Unsatisfied variables in local search. In: Hybrid Problems, Hybrid Solutions, pp. 73–85 (1995)Google Scholar
  5. 5.
    Hansen, P., Jaumard, B.: Algorithms for the maximum satisfiability problem. Computing 44, 279–303 (1990)MATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Hoos, H.H.: On the run-time behaviour of stochastic local search algorithms for SAT. In: Proc. of the Sixteenth Nat’l Conf. on Artificial Intelligence (AAAI 1999), pp. 661–666 (1999)Google Scholar
  7. 7.
    Hoos, H.H.: An adaptive noise mechanism for WalkSAT. In: Proc. of the 18th Nat’l Conf. in Artificial Intelligence (AAAI 2002), pp. 655–660 (2002)Google Scholar
  8. 8.
    Hoos, H.H., Stützle, T.: Stochastic Local Search: Foundations and Applications. Morgan Kaufmann, San Francisco (2005)MATHGoogle Scholar
  9. 9.
    Hutter, F., Tompkins, D.A.D., Hoos, H.H.: Scaling and probabilistic smoothing: Efficient dynamic local search for SAT. In: Van Hentenryck, P. (ed.) CP 2002. LNCS, vol. 2470, pp. 233–248. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  10. 10.
    Matsumoto, M., Nishimura, T.: Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator. ACM Trans. on Modeling & Computer Simulation 8(1), 3–30 (1998)MATHCrossRefGoogle Scholar
  11. 11.
    Mazure, B., Saïs, L., Grégoire, É.: Tabu search for SAT. In: Proc. of the Fourteenth Nat’l Conf. on Artificial Intelligence (AAAI 1997), pp. 281–285 (1997)Google Scholar
  12. 12.
    McAllester, D., Selman, B., Kautz, H.: Evidence for invariants in local search. In: Proc. of the Fourteenth Nat’l Conf. on Artificial Intelligence (AAAI 1997), pp. 321–326 (1997)Google Scholar
  13. 13.
    Selman, B., Kautz, H.A., Cohen, B.: Noise strategies for improving local search. In: Proc. of the 12th Nat’l Conf. on Artificial Intelligence (AAAI 1994), pp. 337–343 (1994)Google Scholar
  14. 14.
    Selman, B., Levesque, H., Mitchell, D.: A new method for solving hard satisfiability problems. In: Proc. of the Tenth Nat’l Conf. on Artificial Intelligence (AAAI 1992), pp. 459–465 (1992)Google Scholar
  15. 15.
    Smyth, K., Hoos, H.H., Stützle, T.: Iterated robust tabu search for MAX-SAT. In: Proc. of the 16th Conf. of the Canadian Society for Computational Studies of Intelligence, pp. 129–144 (2003)Google Scholar
  16. 16.
    Tompkins, D.A.D., Hoos, H.H.: Warped landscapes and random acts of SAT solving. In: Proc. of the Eighth Int’l Symposium on Artificial Intelligence and Mathematics (ISAIM 2004) (2004)Google Scholar
  17. 17.
    Van Hentenryck, P., Michel, L.: Control abstractions for local search. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 65–80. Springer, Heidelberg (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Dave A. D. Tompkins
    • 1
  • Holger H. Hoos
    • 1
  1. 1.Department of Computer ScienceUniversity of British ColumbiaVancouverCanada

Personalised recommendations