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aspeed: Solver scheduling via answer set programming1

Published online by Cambridge University Press:  17 February 2014

HOLGER HOOS ,
ROLAND KAMINSKI ,
MARIUS LINDAUER  and
TORSTEN SCHAUB
HOLGER HOOS
Affiliation:
Department of Computer Science, University of British Columbia, Vancouver, Canada (e-mail: [email protected])
ROLAND KAMINSKI
Affiliation:
Institute of Informatics, University of Potsdam, Potsdam, Germany (e-mail: [email protected])
MARIUS LINDAUER
Affiliation:
Institute of Informatics, University of Potsdam, Potsdam, Germany (e-mail: [email protected])
TORSTEN SCHAUB
Affiliation:
Institute of Informatics, University of Potsdam, Potsdam, Germany (e-mail: [email protected])
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Abstract

Although Boolean Constraint Technology has made tremendous progress over the last decade, the efficacy of state-of-the-art solvers is known to vary considerably across different types of problem instances, and is known to depend strongly on algorithm parameters. This problem was addressed by means of a simple, yet effective approach using handmade, uniform, and unordered schedules of multiple solvers in ppfolio, which showed very impressive performance in the 2011 Satisfiability Testing (SAT) Competition. Inspired by this, we take advantage of the modeling and solving capacities of Answer Set Programming (ASP) to automatically determine more refined, that is, nonuniform and ordered solver schedules from the existing benchmarking data. We begin by formulating the determination of such schedules as multi-criteria optimization problems and provide corresponding ASP encodings. The resulting encodings are easily customizable for different settings, and the computation of optimum schedules can mostly be done in the blink of an eye, even when dealing with large runtime data sets stemming from many solvers on hundreds to thousands of instances. Also, the fact that our approach can be customized easily enabled us to swiftly adapt it to generate parallel schedules for multi-processor machines.

Keywords

algorithm schedulesanswer set programmingportfolio-based solving
Type
Regular Papers
Information
Theory and Practice of Logic Programming , Volume 15 , Issue 1 , January 2015 , pp. 117 - 142
Copyright
Copyright © Cambridge University Press 2014 

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Footnotes

1

Extended version of aspeed: ASP based Solver Scheduling published in 28th International Conference on Logic Programming 2012 (ICLP'12).

References

Andres, B., Kaufmann, B., Matheis, O. and Schaub, T. 2012. Unsatisfiability-based optimization in clasp. In Technical Communications of the Twenty-Eighth International Conference on Logic Programming (ICLP'12), Dovier, A. and Costa, V. Santos, Eds., Leibniz International Proceedings in Informatics (LIPIcs), vol. 17. Curran, Miami, FL, 212–221.Google Scholar
Ansótegui, C., Sellmann, M. and Tierney, K. 2009. A gender-based genetic algorithm for the automatic configuration of algorithms. In Proceedings of the Fifteenth International Conference on Principles and Practice of Constraint Programming (CP'09), Gent, I., Ed., Lecture Notes in Computer Science, vol. 5732. Springer-Verlag, Berlin, Germany, 142–157.Google Scholar
Baral, C. 2003. Knowledge Representation, Reasoning and Declarative Problem Solving. Cambridge University Press, Cambridge, UK.Google Scholar
Biere, A., Heule, M., van Maaren, H. and Walsh, T., Eds. 2009. Handbook of Satisfiability. Frontiers in Artificial Intelligence and Applications, vol. 185. IOS Press, Amsterdam, Netherlands.Google Scholar
Calimeri, F., Ianni, G., Ricca, F., Alviano, M., Bria, A., Catalano, G., Cozza, S., Faber, W., Febbraro, O., Leone, N., Manna, M., Martello, A., Panetta, C., Perri, S., Reale, K., Santoro, M., Sirianni, M., Terracina, G. and Veltri, P. 2011. The third answer set programming competition: Preliminary report of the system competition track. In Proceedings of the Eleventh International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR11), Lecture Notes in Artificial Intelligence, vol. 6645. Springer-Verlag, Berlin, Germany, 388–403.Google Scholar
Coelho, H., Studer, R. and Wooldridge, M., Eds. 2010. Proceedings of the Nineteenth European Conference on Artificial Intelligence (ECAI'10). IOS Press, Amsterdam, Netherlands.Google Scholar
Delgrande, J. and Faber, W., Eds. 2011. Proceedings of the Eleventh International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR'11), Lecture Notes in Artificial Intelligence, vol. 6645. Springer-Verlag, Berlin, Germany.Google Scholar
Gagliolo, M. and Schmidhuber, J. 2006. Learning dynamic algorithm portfolios. Annals of Mathematics and Artificial Intelligence 47, 3–4, 295–328.Google Scholar
Gebruers, C., Guerri, A., Hnich, B. and Milano, M. 2004. Making choices using structure at the instance level within a case based reasoning framework. In Proceedings of the First Conference on Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems (CPAIOR'04), Régin, J. and Rueher, M., Eds., Lecture Notes in Computer Science, vol. 3011. Springer-Verlag, Berlin, Germany, 380–386.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Schneider, M. 2011a. Potassco: The Potsdam answer set solving collection. AI Communications 24, 2, 107–124.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Ostrowski, M., Schaub, T. and Thiele, S. 2010. A user's guide to gringo, clasp, clingo, and iclingo. Accessed 3 February 2014. https://sourceforge.net/projects/potassco/files/potassco_guide/2010-10-04/ Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B. and Schaub, T. 2012a. Answer Set Solving in Practice. Synthesis Lectures on Artificial Intelligence and Machine Learning. Morgan and Claypool, Florida.Google Scholar
Gebser, M., Kaminski, R., Kaufmann, B., Schaub, T., Schneider, M. and Ziller, S. 2011b. A portfolio solver for answer set programming: Preliminary report. In Proceedings of the Eleventh International Conference on Logic Programming and Nonmonotonic Reasoning (LPNMR11), Lecture Notes in Artificial Intelligence, vol. 6645. Springer-Verlag, Berlin, Germany, 352–357.Google Scholar
Gebser, M., Kaufmann, B. and Schaub, T. 2012b. Conflict-driven answer set solving: From theory to practice. Artificial Intelligence 187–188, 52–89.Google Scholar
Gent, I., Jefferson, C., Kotthoff, L., Miguel, I., Moore, N., Nightingale, P. and Petrie, K. 2010. Learning when to use lazy learning in constraint solving. In Proceedings of the Nineteenth European Conference on Artificial Intelligence (ECAI10). IOS Press, Amsterdam, Netherlands, 873–878.Google Scholar
Gomes, C. and Selman, B. 2001. Algorithm portfolios. Artificial Intelligence 126, 1–2, 43–62.CrossRefGoogle Scholar
Guerri, A. and Milano, M. 2004. Learning techniques for automatic algorithm portfolio selection. In Proceedings of the 16th European Conference on AI, Valencia, Spain. Morgan and Claypool, Florida, 475–479.Google Scholar
Guo, H. and Hsu, W. 2004. A learning-based algorithm selection meta-reasoner for the real-time MPE problem. In Proceedings of the Seventeenth Australian Joint Conference on Artificial Intelligence. Springer, New York, NY, 307–318.Google Scholar
Hamadi, Y. and Schoenauer, M., Eds. 2012. Proceedings of the Sixth International Conference Learning and Intelligent Optimization (LION'12). Lecture Notes in Computer Science, vol. 7219. Springer-Verlag, Berlin, Germany.Google Scholar
Helmert, M., Röger, G. and Karpas, E. 2011. Fast downward stone soup: A baseline for building planner portfolios. In ICAPS 2011 Workshop on Planning and Learning. AAAI Press, Palo Alto, CA, 28–35.Google Scholar
Hoos, H., Kaufmann, B., Schaub, T. and Schneider, M. 2013. Robust benchmark set selection for boolean constraint solvers. In Proceedings of the Seventh International Conference on Learning and Intelligent Optimization (LION'13), Pardalos, P. and Nicosia, G., Eds., Lecture Notes in Computer Science, vol. 8248. Springer-Verlag, Berlin, Germany, 138–152.CrossRefGoogle Scholar
Huberman, B., Lukose, R. and Hogg, T. 1997. An economic approach to hard computational problems. Science 275, 51–54.CrossRefGoogle ScholarPubMed
Hutter, F., Hoos, H. and Leyton-Brown, K. 2011. Sequential model-based optimization for general algorithm configuration. In Proceedings of the Fifth International Conference on Learning and Intelligent Optimization (LION'11), Lecture Notes in Computer Science, vol. 6683. Springer-Verlag, Berlin, Germany, 507–523.CrossRefGoogle Scholar
Hutter, F., Hoos, H., Leyton-Brown, K. and Stützle, T. 2009. ParamILS: An automatic algorithm configuration framework. Journal of Artificial Intelligence Research 36, 267–306.CrossRefGoogle Scholar
Hutter, F., Hoos, H. and Stützle, T. 2007. Automatic algorithm configuration based on local search. In Proceedings of the Twenty-second National Conference on Artificial Intelligence (AAAI'07). AAAI Press., Palo Alto, CA, 1152–1157.Google Scholar
Kadioglu, S., Malitsky, Y., Sabharwal, A., Samulowitz, H. and Sellmann, M. 2011. Algorithm selection and scheduling. In Proceedings of the Seventeenth International Conference on Principles and Practice of Constraint Programming (CP'11), Lee, J., Ed., Lecture Notes in Computer Science, vol. 6876. Springer-Verlag, Berlin, Germany, 454–469.Google Scholar
Kadioglu, S., Malitsky, Y., Sellmann, M. and Tierney, K. 2010. ISAC – instance-specific algorithm configuration. In Proceedings of the Nineteenth European Conference on Artificial Intelligence (ECAI'10). IOS Press, Amsterdam, Netherlands, 751–756.Google Scholar
López-Ibáñez, M., Dubois-Lacoste, J., Stützle, T. and Birattari, M. 2011. The Irace Package, Iterated Race for Automatic Algorithm Configuration. Technical report, IRIDIA, Université Libre de Bruxelles, Belgium.Google Scholar
Malitsky, Y., Mehta, D. and OSullivan, B. 2013. Evolving instance specific algorithm configuration. In Proceedings of the Sixth Annual Symposium on Combinatorial Search (SOCS'13), Helmert, H. and Röger, G., Eds. Proceedings of the National Conference on Artificial Intelligence (AAAI). AAAI, Palo Alto, CA, 132–140.Google Scholar
Malitsky, Y., Sabharwal, A., Samulowitz, H. and Sellmann, M. 2012. Parallel SAT solver selection and scheduling. In Proceedings of the Eighteenth International Conference on Principles and Practice of Constraint Programming (CP'12), Milano, M., Ed., Lecture Notes in Computer Science, vol. 7514. Springer-Verlag, Berlin, Germany, 512–526.CrossRefGoogle Scholar
O'Mahony, E., Hebrard, E., Holland, A., Nugent, C. and O'Sullivan, B. 2008. Using case-based reasoning in an algorithm portfolio for constraint solving. In Proceedings of the Nineteenth Irish Conference on Artificial Intelligence and Cognitive Science (AICS'08), Bridge, D., Brown, K., O'Sullivan, B., and Sorensen, H., Eds.Google Scholar
Pulina, L. and Tacchella, A. 2009. A self-adaptive multi-engine solver for quantified Boolean formulas. Constraints 14, 1, 80–116.CrossRefGoogle Scholar
Rice, J. 1976. The algorithm selection problem. Advances in Computers 15, 65–118.CrossRefGoogle Scholar
Roussel, O. 2011. Description of ppfolio. Accessed 3 February 2014. http://www.cril.univ-artois.fr/~roussel/ppfolio/solver1.pdf Google Scholar
Schneider, M. and Hoos, H. 2012. Quantifying homogeneity of instance sets for algorithm configuration. Proceedings of the Sixth International Conference on Learning and Intelligent Optimization (LION'12), Lecture Notes in Computer Science, vol. 7219. Springer-Verlag, Berlin, Germany, 190–204.Google Scholar
Seipp, J., Braun, M., Garimort, J. and Helmert, M. 2012. Learning portfolios of automatically tuned planners. In Proceedings of the Twenty-Second International Conference on Automated Planning and Scheduling (ICAPS'12), McCluskey, L., Williams, B., Silva, J. R., and Bonet, B., Eds. AAAI, Palo Alto, CA, 368–372.Google Scholar
Streeter, M., Golovin, D. and Smith, S. 2007. Combining multiple heuristics online. 2007. In Proceedings of the Twenty-Second National Conference on Artificial Intelligence (AAAI'07). AAAI Press, Palo Alto, CA, 1197–1203.Google Scholar
Tamura, N., Taga, A., Kitagawa, S. and Banbara, M. 2009. Compiling finite linear CSP into SAT. Constraints 14, 2, 254–272.Google Scholar
Xu, L., Hoos, H. and Leyton-Brown, K. 2007. Hierarchical hardness models for SAT. In Proceedings of the Thirteenth International Conference on Principles and Practice of Constraint Programming (CP'07), Bessiere, C., Ed., Lecture Notes in Computer Science, vol. 4741. Springer-Verlag, Berlin, Germany, 696–711.Google Scholar
Xu, L., Hoos, H. and Leyton-Brown, K. 2010. Hydra: Automatically configuring algorithms for portfolio-based selection. In Proceedings of the Twenty-Fourth National Conference on Artificial Intelligence (AAAI'10), Fox, M. and Poole, D., Eds. AAAI Press, Palo Alto, CA, 210–216.Google Scholar
Xu, L., Hutter, F., Hoos, H. and Leyton-Brown, K. 2008. SATzilla: Portfolio-based algorithm selection for SAT. Journal of Artificial Intelligence Research 32, 565–606.Google Scholar
Xu, L., Hutter, F., Hoos, H. and Leyton-Brown, K. 2012. Evaluating component solver contributions to portfolio-based algorithm selectors. In Proceedings of the Fifteenth International Conference on Theory and Applications of Satisfiability Testing (SAT'12), Cimatti, A. and Sebastiani, R., Eds., Lecture Notes in Computer Science, vol. 7317. Springer-Verlag, Berlin, Germany, 228–241.Google Scholar
Yun, X. and Epstein, S. 2012. Learning algorithm portfolios for parallel execution. In Proceedings of the Sixth International Conference Learning and Intelligent Optimization (LION'12), Lecture Notes in Computer Science, vol. 2012. Springer-Verlag, Berlin, Germany, 323–338.Google Scholar