Abstract

SAT technology has improved rapidly in recent years, to the point now where it can solve CNF problems of immense size. But solving CNF problems ignores one important fact: there are NO problems that are originally CNF. All the CNF that SAT solvers tackle is the result of modelling some real world problem, and mapping the high-level constraints and decisions modelling the problem into clauses on binary variables. But by throwing away the high level view of the problem SAT solving may have lost a lot of important insight into how the problem is best solved. In this talk I will hope to persuade you that by keeping the original high level model of the problem one can realise immense benefits in solving hard real world problems.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Hentenryck, P.V.: Constraint Satisfaction in Logic Programming. MIT Press (1989)Google Scholar
  2. 2.
    Marriott, K., Stuckey, P.: Programming with Constraints: an Introduction. MIT Press (1998)Google Scholar
  3. 3.
    Metodi, A., Codish, M., Stuckey, P.J.: Boolean equi-propagation for concise and efficient sat encodings of combinatorial problems. Journal of Artificial Intelligence Research 46, 303–341 (2013), http://www.jair.org/papers/paper3809.html MATHGoogle Scholar
  4. 4.
    Ohrimenko, O., Stuckey, P., Codish, M.: Propagation via lazy clause generation. Constraints 14(3), 357–391 (2009)MathSciNetMATHCrossRefGoogle Scholar
  5. 5.
    Abío, I., Stuckey, P.J.: Conflict directed lazy decomposition. In: Milano, M. (ed.) CP 2012. LNCS, vol. 7514, pp. 70–85. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  6. 6.
    Schutt, A., Feydy, T., Stuckey, P., Wallace, M.: Explaining the cumulative propagator. Constraints 16(3), 250–282 (2011)MathSciNetMATHCrossRefGoogle Scholar
  7. 7.
    Schutt, A., Feydy, T., Stuckey, P., Wallace, M.: Solving RCPSP/max by lazy clause generation. Journal of Scheduling (2012) (online first: August 2012), http://dx.doi.org/10.1007/s10951-012-0285-x
  8. 8.
    Schutt, A., Chu, G., Stuckey, P.J., Wallace, M.G.: Maximising the net present value for resource-constrained project scheduling. In: Beldiceanu, N., Jussien, N., Pinson, É. (eds.) CPAIOR 2012. LNCS, vol. 7298, pp. 362–378. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Schutt, A., Stuckey, P.J., Verden, A.R.: Optimal carpet cutting. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 69–84. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Nieuwenhuis, R., Oliveras, A., Tinelli, C.: Solving SAT and SAT Modulo Theories: From an abstract Davis–Putnam–Logemann–Loveland procedure to DPLL(T). J. ACM 53(6), 937–977 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Nethercote, N., Stuckey, P.J., Becket, R., Brand, S., Duck, G.J., Tack, G.: MiniZinc: Towards a standard CP modelling language. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 529–543. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  12. 12.
    Schulte, C., Lagerkvist, M., Tack, G.: Gecode, http://www.gecode.org/
  13. 13.
    Perron, L.: OR-tools: Operations research tools developed at Google, https://code.google.com/p/or-tools/
  14. 14.
    SCIP: Solving constraint integer programs, http://scip.zib.de/scip.shtml
  15. 15.
    Bofill, M., Palahí, M., Suy, J., Villaret, M.: Solving constraint satisfaction problems with SAT modulo theories. Constraints 17(3), 273–303 (2012)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Peter J. Stuckey
    • 1
  1. 1.National ICT Australia, Victoria Laboratory, Department of Computing and Information SystemsUniversity of MelbourneAustralia

Personalised recommendations