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Simple algorithm portfolio for SAT

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Abstract

The importance of algorithm portfolio techniques for SAT has long been noted, and a number of very successful systems have been devised, including the most successful one—SATzilla. However, all these systems are quite complex (to understand, reimplement, or modify). In this paper we present an algorithm portfolio for SAT that is extremely simple, but in the same time so efficient that it outperforms SATzilla. For a new SAT instance to be solved, our portfolio finds its k-nearest neighbors from the training set and invokes a solver that performs the best for those instances. The main distinguishing feature of our algorithm portfolio is the locality of the selection procedure—the selection of a SAT solver is based only on few instances similar to the input one. An open source tool that implements our approach is publicly available.

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References

  • Ansótegui C, Sellmann M, Tierney K (2009) A gender-based genetic algorithm for the automatic configuration of algorithms. In: Proceedings of the 15th international conference on principles and practice of constraint programming. Springer, New York, pp 142–157

  • Duda R, Hart P, Stork D (2001) Pattern classification. 2nd edn. Wiley-Interscience, New York

    MATH  Google Scholar 

  • Gomes C, Selman B (2001) Algorithm portfolios. Artif Intell 126(1–2): 43–62

    Article  MathSciNet  MATH  Google Scholar 

  • Horvitz E, Ruan Y, Gomes C, Kautz H, Selman B, Chickering DM (2001) A bayesian approach to tackling hard computational problems. In: UAI, pp 235–244

  • Huberman B, Lukose R, Hogg T (1997) An economic approach to hard computational problems. Science 27: 51–53

    Article  Google Scholar 

  • Hutter F, Hoos H, Leyton-Brown K, Stützle T (2009) Paramils: an automatic algorithm configuration framework. J Artif Intell Res 36: 267–306

    MATH  Google Scholar 

  • Kadioglu S, Malitsky Y, Sellmann M, Tierney K (2010) Isac–instance-specific algorithm configuration In: Proceeding of the 19th European conference on artificial intelligence. IOS press, pp 751–756

  • Lagoudakis M, Littman M (2001) Learning to select branching rules in the dpll procedure for satisfiability. Electron Notes Discret Math 9: 344–359

    Article  Google Scholar 

  • Malitsky Y, Sabharwal A, Samulowitz H, Sellmann M (2011) Non-model-based algorithm portfolios for SAT. In: Theory and applications of satisfiability testing (SAT), pp 369–370

  • Marić F (2009) Formalization and implementation of modern sat solvers. J Autom Reason 43: 81–119

    Article  MATH  Google Scholar 

  • Nikolić M, Marić F, Janičić P (2009) Instance-based selection of policies for sat solvers. In: Kullmann O Theory and applications of satisfiability testing—SAT 2009, Vol 5584 of lecture notes in computer science. Springer, Berlin, pp 326–340

  • Nudelman E, Brown KL, Hoos HH, Devkar A, Shoham Y (2004) Understanding random SAT: beyond the clauses-to-variables ratio. In: Wallace M (ed) Principles and practice of constraint programming—CP 2004. Vol 3258 of lecture notes in computer science. Springer, Berlin, pp 438–452

    Chapter  Google Scholar 

  • Samulowitz H, Memisevic R (2007) Learning to solve QBF. In: Proceedings of the twenty-second AAAI conference on artificial intelligence. AAAI Press, Vancouver, British Columbia, Canada, pp 255–260

  • Silverthorn B, Miikkulainen R (2010) Latent class models for algorithm portfolio methods. In: Proceedings of the twenty-fourth AAAI conference on artificial intelligence, pp 167–172

  • Tomovic A, Janicic P, Keselj V (2006) n-Gram-based classification and unsupervised hierarchical clustering of genome sequences. Comput Methods Program Biomed 81(2): 137–153

    Article  Google Scholar 

  • Xu L, Hutter F, Hoos H, Leyton-Brown K (2008) SATzilla: portfolio-based algorithm selection for SAT. J Artif Intell Res 32: 565–606

    MATH  Google Scholar 

  • Xu L, Hutter F, Hoos H, Leyton-Brown K (2009) SATzilla2009: an automatic algorithm portfolio for SAT. In: SAT 2009 competitive events booklet, pp 53–55

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Correspondence to Mladen Nikolić.

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Nikolić, M., Marić, F. & Janičić, P. Simple algorithm portfolio for SAT. Artif Intell Rev 40, 457–465 (2013). https://doi.org/10.1007/s10462-011-9290-2

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  • DOI: https://doi.org/10.1007/s10462-011-9290-2

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