Advertisement

DPvis – A Tool to Visualize the Structure of SAT Instances

  • Carsten Sinz
  • Edda-Maria Dieringer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3569)

Abstract

We present DPvis, a Java tool to visualize the structure of SAT instances and runs of the DPLL (Davis-Putnam-Logemann-Loveland) procedure. DPvis uses advanced graph layout algorithms to display the problem’s internal structure arising from its variable dependency (interaction) graph. DPvis is also able to generate animations showing the dynamic change of a problem’s structure during a typical DPLL run. Besides implementing a simple variant of the DPLL algorithm on its own, DPvis also features an interface to MiniSAT, a state-of-the-art DPLL implementation. Using this interface, runs of MiniSAT can be visualized—including the generated search tree and the effects of clause learning. DPvis is supposed to help in teaching the DPLL algorithm and in gaining new insights in the structure (and hardness) of SAT instances.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sinz, C.: Visualizing the internal structure of SAT instances (preliminary report). In: Hoos, H.H., Mitchell, D.G. (eds.) SAT 2004. LNCS, vol. 3542. Springer, Heidelberg (2005)Google Scholar
  2. 2.
    Rish, I., Dechter, R.: Resolution versus search: Two strategies for SAT. J. Automated Reasoning 24, 225–275 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Fruchterman, T., Reingold, E.: Graph drawing by force-directed placement. Software – Practice and Experience 21, 1129–1164 (1991)CrossRefGoogle Scholar
  4. 4.
    Aspvall, M., Plass, M., Tarjan, R.: A linear-time algorithm for testing the trurh of certain quantified boolean formulas. Information Processing Letters 8 (1979)Google Scholar
  5. 5.
    Park, T., Van Gelder, A.: Partitioning methods for satisfiability testing on large formulas. Information and Computation 162, 179–184 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  6. 6.
    Slater, A.: Visualisation of satisfiability problems using connected graphs (2004), http://rsise.anu.edu.au/~andrews/problem2graph
  7. 7.
    Monasson, R., Zecchina, R., Kirkpatrick, S., Selman, B., Troyansky, L.: Determining computational complexity from characteristic ’phase transitions’. Nature 400, 133–137 (1999)CrossRefMathSciNetGoogle Scholar
  8. 8.
    Di Battista, G., Eades, P., Tamassia, R., Tollis, I.: Algorithms for automatic graph drawing: An annotated bibliography. Computational Geometry 4, 235–282 (1994)zbMATHCrossRefMathSciNetGoogle Scholar
  9. 9.
    Eades, P.: A heuristic for graph drawing. Congressus Numerantium 42, 149–160 (1984)MathSciNetGoogle Scholar
  10. 10.
    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
  11. 11.
    Singer, J., Gent, I., Smaill, A.: Backbone fragility and the local search cost peak. Journal of Artificial Intelligence Research 12, 235–270 (2000)zbMATHMathSciNetGoogle Scholar
  12. 12.
    Selman, B.: Algorithmic adventures at the interface of computer science, statistical physics, and combinatorics. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 9–12. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Carsten Sinz
    • 1
  • Edda-Maria Dieringer
    • 1
  1. 1.Symbolic Computation Group, WSI for Computer ScienceUniversity of TübingenTübingenGermany

Personalised recommendations