Skip to main content

SAT-Based Horn Least Upper Bounds

  • Conference paper
  • First Online:
Theory and Applications of Satisfiability Testing -- SAT 2015 (SAT 2015)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9340))

  • 1637 Accesses

Abstract

Knowledge compilation and approximation finds a wide range of practical applications. One relevant task in this area is to compute the Horn least upper bound (Horn LUB) of a propositional theory F. The Horn LUB is the strongest Horn theory entailed by F. This paper studies this problem and proposes two new algorithms that rely on making successive calls to a SAT solver. The algorithms are analyzed theoretically and evaluated empirically. The results show that the proposed methods are complementary and enable computing Horn LUBs for instances with a non-negligible number of variables.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 54.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 69.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Audemard, G., Lagniez, J.-M., Simon, L.: Improving glucose for incremental SAT solving with assumptions: application to MUS extraction. In: Järvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962, pp. 309–317. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  2. Bacchus, F., Davies, J., Tsimpoukelli, M., Katsirelos, G.: Relaxation search: a simple way of managing optional clauses. In: AAAI, pp. 835–841 (2014)

    Google Scholar 

  3. Belov, A., Heule, M.J.H., Marques-Silva, J.: MUS extraction using clausal proofs. In: Sinz, C., Egly, U. (eds.) SAT 2014. LNCS, vol. 8561, pp. 48–57. Springer, Heidelberg (2014)

    Google Scholar 

  4. Belov, A., Janota, M., Lynce, I., Marques-Silva, J.: Algorithms for computing minimal equivalent subformulas. Artif. Intell. 216, 309–326 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  5. Belov, A., Lynce, I., Marques-Silva, J.: Towards efficient MUS extraction. AI Commun. 25(2), 97–116 (2012)

    MathSciNet  MATH  Google Scholar 

  6. Ben-Eliyahu, R., Dechter, R.: On computing minimal models. Ann. Math. Artif. Intell. 18(1), 3–27 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  7. Biere, A., Heule, M., van Maaren, H., Walsh, T. (eds.): Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press (2009)

    Google Scholar 

  8. Boufkhad, Y.: Algorithms for propositional KB approximation. In: AAAI, pp. 280–285 (1998)

    Google Scholar 

  9. Boufkhad, Y., Grégoire, É., Marquis, P., Mazure, B., Sais, L.: Tractable cover compilations. In: IJCAI, pp. 122–127 (1997)

    Google Scholar 

  10. Bradley, A.R., Manna, Z.: Checking safety by inductive generalization of counterexamples to induction. In: FMCAD, pp. 173–180 (2007)

    Google Scholar 

  11. Cadoli, M.: Semantical and computational aspects of Horn approximations. In: IJCAI, pp. 39–45 (1993)

    Google Scholar 

  12. Cadoli, M., Donini, F.M.: A survey on knowledge compilation. AI Commun. 10(3–4), 137–150 (1997)

    Google Scholar 

  13. Cadoli, M., Donini, F.M., Liberatore, P., Schaerf, M.: Space efficiency of propositional knowledge representation formalisms. J. Artif. Intell. Res. (JAIR) 13, 1–31 (2000)

    MathSciNet  MATH  Google Scholar 

  14. Cadoli, M., Scarcello, F.: Semantical and computational aspects of Horn approximations. Artif. Intell. 119(1–2), 1–17 (2000)

    Article  MathSciNet  MATH  Google Scholar 

  15. Castell, T.: Computation of prime implicates and prime implicants by a variant of the Davis and Putnam procedure. In: ICTAI, pp. 428–429 (1996)

    Google Scholar 

  16. Cook, S.A.: The complexity of theorem-proving procedures. In: STOC, pp. 151–158 (1971)

    Google Scholar 

  17. Darwiche, A., Marquis, P.: A knowledge compilation map. J. Artif. Intell. Res. (JAIR) 17, 229–264 (2002)

    MathSciNet  MATH  Google Scholar 

  18. del Val, A.: An analysis of approximate knowledge compilation. In: IJCAI, pp. 830–836 (1995)

    Google Scholar 

  19. del Val, A.: First order LUB approximations: characterization and algorithms. Artif. Intell. 162(1–2), 7–48 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  20. Dowling, W.F., Gallier, J.H.: Linear-time algorithms for testing the satisfiability of propositional Horn formulae. J. Log. Program. 1(3), 267–284 (1984)

    Article  MathSciNet  MATH  Google Scholar 

  21. Grégoire, É., Lagniez, J.M., Mazure, B.: An experimentally efficient method for (MSS, coMSS) partitioning. In: AAAI, pp. 2666–2673 (2014)

    Google Scholar 

  22. Ignatiev, A., Morgado, A., Manquinho, V.M., Lynce, I., Marques-Silva, J.: Progression in maximum satisfiability. In: ECAI, pp. 453–458 (2014)

    Google Scholar 

  23. Itai, A., Makowsky, J.A.: Unification as a complexity measure for logic programming. J. Log. Program. 4(2), 105–117 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  24. Janota, M., Lynce, I., Marques-Silva, J.: Algorithms for computing backbones of propositional formulae. AI Commun. 28(2), 161–177 (2015)

    MathSciNet  MATH  Google Scholar 

  25. Junker, U.: QuickXplain: preferred explanations and relaxations for over-constrained problems. In: AAAI, pp. 167–172 (2004)

    Google Scholar 

  26. Kautz, H.A., Selman, B.: An empirical evaluation of knowledge compilation by theory approximation. In: AAAI, pp. 155–161 (1994)

    Google Scholar 

  27. Lagniez, J.-M., Biere, A.: Factoring out assumptions to speed Up MUS extraction. In: Järvisalo, M., Van Gelder, A. (eds.) SAT 2013. LNCS, vol. 7962, pp. 276–292. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  28. Langlois, M., Sloan, R.H., Turán, G.: Horn upper bounds and renaming. In: Marques-Silva, J., Sakallah, K.A. (eds.) SAT 2007. LNCS, vol. 4501, pp. 80–93. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  29. Langlois, M., Sloan, R.H., Turán, G.: Horn upper bounds and renaming. JSAT 7(1), 1–15 (2009)

    MathSciNet  MATH  Google Scholar 

  30. Marques-Silva, J., Heras, F., Janota, F., Previti, A., Belov, A.: On computing minimal correction subsets. In: IJCAI (2013)

    Google Scholar 

  31. Marques-Silva, J., Ignatiev, A., Morgado, A., Manquinho, V.M., Lynce, I.: Efficient autarkies. In: ECAI, pp. 603–608 (2014)

    Google Scholar 

  32. Marques-Silva, J., Janota, M., Belov, A.: Minimal sets over monotone predicates in boolean formulae. In: Sharygina, N., Veith, H. (eds.) CAV 2013. LNCS, vol. 8044, pp. 592–607. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  33. Marquis, P.: Knowledge compilation using theory prime implicates. In: IJCAI, pp. 837–845 (1995)

    Google Scholar 

  34. Marquis, P.: Consequence finding algorithms. In: Handbook of Defeasible Reasoning and Uncertainty Management Systems, pp. 41–145 (2000)

    Google Scholar 

  35. Martins, R., Manquinho, V., Lynce, I.: Open-WBO: a modular MaxSAT solver’. In: Sinz, C., Egly, U. (eds.) SAT 2014. LNCS, vol. 8561, pp. 438–445. Springer, Heidelberg (2014)

    Google Scholar 

  36. Mencía, C., Previti, A., Marques-Silva, J.: Literal-based MCS extraction. In: IJCAI, pp. 1973–1979 (2015)

    Google Scholar 

  37. Minoux, M.: LTUR: A simplified linear-time unit resolution algorithm for Horn formulae and computer implementation. Inf. Process. Lett. 29(1), 1–12 (1988)

    Article  MathSciNet  MATH  Google Scholar 

  38. Nadel, A., Ryvchin, V., Strichman, O.: Efficient MUS extraction with resolution. In: FMCAD, pp. 197–200 (2013)

    Google Scholar 

  39. Narodytska, N., Bacchus, F.: Maximum satisfiability using core-guided MaxSAT resolution. In: AAAI, pp. 2717–2723 (2014)

    Google Scholar 

  40. Palopoli, L., Pirri, F., Pizzuti, C.: Algorithms for selective enumeration of prime implicants. Artif. Intell. 111(1–2), 41–72 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  41. Previti, A., Ignatiev, A., Morgado, A., Marques-Silva, J.: Prime compilation of non-clausal formulae. In: IJCAI, pp. 1980–1987 (2015)

    Google Scholar 

  42. Reiter, R.: A theory of diagnosis from first principles. Artif. Intell. 32(1), 57–95 (1987)

    Article  MathSciNet  MATH  Google Scholar 

  43. Rosa, E.D., Giunchiglia, E., Maratea, M.: Solving satisfiability problems with preferences. Constraints 15(4), 485–515 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  44. Rymon, R.: An SE-tree-based prime implicant generation algorithm. Ann. Math. Artif. Intell. 11(1–4), 351–366 (1994)

    Article  MATH  Google Scholar 

  45. Schachte, P., Søndergaard, H.: Boolean approximation revisited. In: Miguel, I., Ruml, W. (eds.) SARA 2007. LNCS (LNAI), vol. 4612, pp. 329–343. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  46. Schachte, P., Søndergaard, H., Whiting, L., Henshall, K.: Information loss in knowledge compilation: A comparison of boolean envelopes. Artif. Intell. 174(9–10), 585–596 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  47. Schrag, R.: Compilation for critically constrained knowledge bases. In: AAAI, pp. 510–515 (1996)

    Google Scholar 

  48. Selman, B., Kautz, H.A.: Knowledge compilation using Horn approximations. In: AAAI, pp. 904–909 (1991)

    Google Scholar 

  49. Selman, B., Kautz, H.A.: Knowledge compilation and theory approximation. J. ACM 43(2), 193–224 (1996)

    Article  MathSciNet  MATH  Google Scholar 

  50. Simon, L., del Val, A.: Efficient consequence finding. In: IJCAI, pp. 359–370 (2001)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Carlos MencĂ­a .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

MencĂ­a, C., Previti, A., Marques-Silva, J. (2015). SAT-Based Horn Least Upper Bounds. In: Heule, M., Weaver, S. (eds) Theory and Applications of Satisfiability Testing -- SAT 2015. SAT 2015. Lecture Notes in Computer Science(), vol 9340. Springer, Cham. https://doi.org/10.1007/978-3-319-24318-4_30

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24318-4_30

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24317-7

  • Online ISBN: 978-3-319-24318-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics