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.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.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
Gomes C, Selman B (2001) Algorithm portfolios. Artif Intell 126(1–2): 43–62
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
Hutter F, Hoos H, Leyton-Brown K, Stützle T (2009) Paramils: an automatic algorithm configuration framework. J Artif Intell Res 36: 267–306
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
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
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
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
Xu L, Hutter F, Hoos H, Leyton-Brown K (2008) SATzilla: portfolio-based algorithm selection for SAT. J Artif Intell Res 32: 565–606
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
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-011-9290-2