Dsharp: Fast d-DNNF Compilation with sharpSAT

  • Christian Muise
  • Sheila A. McIlraith
  • J. Christopher Beck
  • Eric I. Hsu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7310)


Knowledge compilation is a compelling technique for dealing with the intractability of propositional reasoning. One particularly effective target language is Deterministic Decomposable Negation Normal Form (d-DNNF). We exploit recent advances in #SAT solving in order to produce a new state-of-the-art CNF → d-DNNF compiler: Dsharp. Empirical results demonstrate that Dsharp is generally an order of magnitude faster than c2d, the de facto standard for compiling to d-DNNF, while yielding a representation of comparable size.


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  1. 1.
    Beame, P., Kautz, H., Sabharwal, A.: Understanding the power of clause learning. In: International Joint Conference on Artificial Intelligence, vol. 18, pp. 1194–1201 (2003)Google Scholar
  2. 2.
    Chavira, M., Darwiche, A., Jaeger, M.: Compiling relational bayesian networks for exact inference. International Journal of Approximate Reasoning 42, 4–20 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Darwiche, A., Marquis, P.: A knowledge compilation map. Journal of Artificial Intelligence Research 17, 229–264 (2002)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Darwiche, A.: New advances in compiling CNF to decomposable negational normal form. In: Proceedings of European Conference on Artificial Intelligence (2004)Google Scholar
  5. 5.
    Huang, J., Darwiche, A.: DPLL with a trace: from SAT to knowledge compilation. In: International Joint Conference On Artificial Intelligence, pp. 156–162 (2005)Google Scholar
  6. 6.
    Jha, A., Suciu, D.: Knowledge compilation meets database theory: compiling queries to decision diagrams. In: Proceedings of the 14th International Conference on Database Theory, pp. 162–173. ACM (2011)Google Scholar
  7. 7.
    Muise, C., McIlraith, S.A., Beck, J.C., Hsu, E.: Fast d-DNNF compilation with sharpSAT. In: Workshop on Abstraction, Reformulation, and Approximation, AAAI 2010 (2010)Google Scholar
  8. 8.
    Palacios, H., Bonet, B., Darwiche, A., Geffner, H.: Pruning conformant plans by counting models on compiled d-DNNF representations. In: Proceedings of the 15th International Conference on Automated Planning and Scheduling, pp. 141–150 (2005)Google Scholar
  9. 9.
    Siddiqi, S., Huang, J.: Probabilistic sequential diagnosis by compilation. In: Tenth International Symposium on Artificial Intelligence and Mathematics (2008)Google Scholar
  10. 10.
    Thurley, M.: sharpSAT – Counting Models with Advanced Component Caching and Implicit BCP. In: Biere, A., Gomes, C.P. (eds.) SAT 2006. LNCS, vol. 4121, pp. 424–429. Springer, Heidelberg (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christian Muise
    • 1
  • Sheila A. McIlraith
    • 1
  • J. Christopher Beck
    • 2
  • Eric I. Hsu
    • 1
  1. 1.Department of Computer ScienceUniversity of TorontoTorontoCanada
  2. 2.Department of Mechanical & Industrial EngineeringUniversity of TorontoTorontoCanada

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