Skip to main content

Adaptation in a CBR-Based Solver Portfolio for the Satisfiability Problem

  • Conference paper

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7466))

Abstract

The satisfiability problem was amongst the very first problems proven to be NP-Complete. It arises in many real world domains such as hardware verification, planning, scheduling, configuration and telecommunications. Recently, there has been growing interest in using portfolios of solvers for this problem. In this paper we present a case-based reasoning approach to SAT solving. A key challenge is the adaptation phase, which we focus on in some depth. We present a variety of adaptation approaches, some heuristic, and one that computes an optimal Kemeny ranking over solvers in our portfolio. Our evaluation over three large case bases of problem instances from artificial, hand-crafted and industrial domains, shows the power of a CBR approach, and the importance of the adaptation schemes used.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

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

    Google Scholar 

  2. Gomes, C.P., Selman, B.: Algorithm Portfolios. Artificial Intelligence 126(1-2), 43–62 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  3. O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using Case-based Reasoning in an Algorithm Portfolio for Constraint Solving. In: Proceedings of AICS (2008)

    Google Scholar 

  4. Xu, L., Hutter, F., Hoos, H.H., Leyton-Brown, K.: SATzilla: Portfolio-based Algorithm Selection for SAT. Journal of Artificial Intelligence Research, 565–606 (June 2008)

    Google Scholar 

  5. Pulina, L., Tacchella, A.: A Multi-engine Solver for Quantified Boolean Formulas. In: Bessière, C. (ed.) CP 2007. LNCS, vol. 4741, pp. 574–589. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  6. Davis, M., Logemann, G., Loveland, D.: A Machine Program for Theorem Proving. Communications of the ACM 5(7), 394–397 (1962)

    Article  MathSciNet  MATH  Google Scholar 

  7. Fürnkranz, J., Hüllermeier, E.: Pairwise Preference Learning and Ranking. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) ECML 2003. LNCS (LNAI), vol. 2837, pp. 145–156. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  8. Dwork, C., Kumar, R., Naor, M., Sivakumar, D.: Rank Aggregation Methods for the Web. In: Proceedings of the 10th International Conference on World Wide Web, WWW 2001, pp. 613–622. ACM, New York (2001)

    Chapter  Google Scholar 

  9. Brinker, K., Fürnkranz, J., Hüllermeier, E.: A Unified Model for Multilabel Classification and Ranking. In: Proceedings of the 2006 European Conference on Artificial Intelligence, ECAI 2006, pp. 489–493. IOS Press (2006)

    Google Scholar 

  10. Brinker, K., Hüllermeier, E.: Case-based Multilabel Ranking. In: Proceedings of the 20th International Joint Conference on Artificial intelligence, IJCAI 2007, pp. 702–707. Morgan Kaufmann Publishers Inc., San Francisco (2007)

    Google Scholar 

  11. Brinker, K., Hüllermeier, E.: Case-Based Label Ranking. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) ECML 2006. LNCS (LNAI), vol. 4212, pp. 566–573. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  12. Gärtner, T., Vembu, S.: Label Ranking Algorithms: A Survey. In: Johannes Fürnkranz, E.H. (ed.) Preference Learning. Springer (2010)

    Google Scholar 

  13. Pacuit, E.: Voting Methods. In: Zalta, E.N. (ed.) The Stanford Encyclopedia of Philosophy, Winter 2011 edn. (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Hurley, B., O’Sullivan, B. (2012). Adaptation in a CBR-Based Solver Portfolio for the Satisfiability Problem. In: Agudo, B.D., Watson, I. (eds) Case-Based Reasoning Research and Development. ICCBR 2012. Lecture Notes in Computer Science(), vol 7466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32986-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32986-9_13

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32985-2

  • Online ISBN: 978-3-642-32986-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics