iSat: Structure Visualization for SAT Problems

  • Ezequiel Orbe
  • Carlos Areces
  • Gabriel Infante-López
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7180)


We present iSat, a Python command line tool to analyze and find structure in propositional satisfiability problems. iSat offers an interactive shell to control propositional SAT solvers and generate graph representations of the internal structure of the search space explored by them for visualization, with the final aim of providing a unified environment for propositional solving experimentation. iSat was designed to enable simple integration of both new SAT solvers and new visualization graphs and statistics with a minimum of coding overhead.


Clique Number Command Line Tool Interactive Shell Structure Visualization Solver Instance 
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.
    Davis, M., Logemann, G., Loveland, D.: A machine program for theorem-proving. Comm. of the ACM 5(7), 394–397 (1962)MathSciNetzbMATHCrossRefGoogle Scholar
  2. 2.
    Davis, M., Putnam, H.: A computing procedure for quantification theory. J. of the ACM 7(3), 201–215 (1960)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Eén, N., Sörensson, N.: An Extensible SAT-solver. In: Giunchiglia, E., Tacchella, A. (eds.) SAT 2003. LNCS, vol. 2919, pp. 502–518. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  4. 4.
    Heule, M.: SmArT solving: Tools and techniques for satisfiability solvers. PhD thesis, TU Delft (2008)Google Scholar
  5. 5.
    Heule, M., van Maaren, H.: March_dl: Adding adaptive heuristics and a new branching strategy. J. on Sat., Boolean Modeling and Comp. 2, 47–59 (2006)zbMATHGoogle Scholar
  6. 6.
    Kautz, H., Selman, B.: Planning as satisfiability. In: Proc. of ECAI 1992. John Wiley and Sons, Inc. (1992)Google Scholar
  7. 7.
    Marques-Silva, J., Sakallah, K.: Robust search algorithms for test pattern generation. In: Proc. of the Fault-Tolerant Computing Symp. IEEE (1997)Google Scholar
  8. 8.
    Moskewicz, M., Madigan, C., Zhao, Y., Zhang, L., Malik, S.: Chaff: Engineering an efficient SAT solver. In: Proc. of the 38th Design Automation Conf. (2001)Google Scholar
  9. 9.
    Nudelman, E., Leyton-Brown, K., Hoos, H., Devkar, A., Shoham, Y.: Understanding Random SAT: Beyond the Clauses-to-Variables Ratio. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 438–452. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Sinz, C., Dieringer, E.-M.: DPvis – A Tool to Visualize the Structure of SAT Instances. In: Bacchus, F., Walsh, T. (eds.) SAT 2005. LNCS, vol. 3569, pp. 257–268. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  11. 11.
    Soos, M.: CryptoMiniSat — a SAT solver for cryptographic problems (2009),

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ezequiel Orbe
    • 1
  • Carlos Areces
    • 1
  • Gabriel Infante-López
    • 1
  1. 1.Grupo de Procesamiento de Lenguaje Natural FaMAFUniversidad Nacional de CórdobaArgentina

Personalised recommendations