Generating Implied Boolean Constraints Via Singleton Consistency

  • Roman Barták
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4612)

Abstract

Though there exist some rules of thumb for design of good models for solving constraint satisfaction problems, the modeling process still belongs more to art than to science. Moreover, as new global constraints and search techniques are being developed, the modeling process is becoming even more complicated and a lot of effort and experience is required from the user. Hence (semi-) automated tools for improving efficiency of constraint models are highly desirable. The paper presents a low-information technique for discovering implied Boolean constraints in the form of equivalences, exclusions, and dependencies for any constraint model with (some) Boolean variables. The technique is not only completely independent of the constraint model (therefore a low-information technique), but it is also easy to implement because it is based on ideas of singleton consistency. Despite its simplicity, the proposed technique proved itself to be surprisingly efficient in our experiments.

Keywords

implied constraints reformulation singleton consistency SAT 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barták, R.: A Flexible Constraint Model for Validating Plans with Durative Actions. In: Planning, Scheduling and Constraint Satisfaction: From Theory to Practice. Frontiers in Artificial Intelligence and Applications, vol. 117, pp. 39–48. IOS Press, Amsterdam (2005)Google Scholar
  2. 2.
    Barták, R., Čepek, O.: Temporal Networks with Alternatives: Complexity and Model. In: FLAIRS 2007. Proceedings of the Twentieth International Florida AI Research Society Conference, AAAI Press (2007)Google Scholar
  3. 3.
    Barták, R., Čepek, O., Surynek, P.: Modelling Alternatives in Temporal Networks. In: CI-Sched 2007. Proceedings of the 2007 IEEE Symposium on Computational Intelligence in Scheduling, pp. 129–136. IEEE Press (2007)Google Scholar
  4. 4.
    Beck, J.Ch., Fox, M.S.: Scheduling Alternative Activities. In: Proceedings of the National Conference on Artificial Intelligence, pp. 680–687. AAAI Press (1999)Google Scholar
  5. 5.
    Debruyne, R., Bessière, C.: Some Practicable Filtering Techniques for the Constraint Satisfaction Problem. In: IJCAI. Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence, pp. 412–417. Morgan Kaufmann, San Francisco (1997)Google Scholar
  6. 6.
    Dechter, R.: Learning while searching in constraint satisfaction problems. In: Proceedings of the Fifth National Conference on Artificial Intelligence, pp. 178–183. AAAI Press (1986)Google Scholar
  7. 7.
    Dechter, R., Meiri, I., Pearl, J.: Temporal Constraint Networks. Artificial Intelligence 49, 61–95 (1991)MATHCrossRefGoogle Scholar
  8. 8.
    Fages, F.: CLP versus LS on log-based reconciliation problems for nomadic applications. In: Proceedings of ERCIM/CompulogNet Workshop on Constraints, Praha (2001)Google Scholar
  9. 9.
    Hoos, H.H., Stützle, T.: SATLIB: An Online Resource for Research on SAT. In: SAT 2000, pp. 283–292. IOS Press, Amsterdam (2000), SATLIB is available online at www.satlib.org Google Scholar
  10. 10.
    Mariot, K., Stuckey, P.J.: Programming with Constraints: An Introduction. The MIT Press, Cambridge (1998)Google Scholar
  11. 11.
    Pardalos, P.M., Qian, T., Resende, M.G.: A greedy randomized adaptive search procedure for the feedback vertex set problem. Journal of Combinatorial Optimization 2, 399–412 (1999)MATHCrossRefGoogle Scholar
  12. 12.
    Pipatsrisawat, T., Darwiche, A.: RSat Solver, version 1.03 (accesessed, March 2007), http://reasoning.cs.ucla.edu/rsat/
  13. 13.
    Smith, B.: Modelling. A chapter in Handbook of Constraint Programming, pp. 377–406. Elsevier, Amsterdam (2006)Google Scholar
  14. 14.
    Stallman, R.M., Sussman, G.J.: Forward reasoning and dependency-directed backtracking in a system for computer-aided circuit analysis. Artificial Intelligence 9, 135–196 (1997)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Roman Barták
    • 1
  1. 1.Charles University in Prague, Faculty of Mathematics and Physics, Malostranské nám. 2/25, 118 00 Praha 1Czech Republic

Personalised recommendations