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This note describes our experiments aiming to empirically test the ability of machine learning models to act as decision oracles for NP problems. Focusing on satisfiability testing problems, we have generated random 3-SAT instances and found out that the correct branch prediction accuracy reached levels in excess of 99%. The branching in a simple backtracking-based SAT solver has been reduced in more than 90% of the tested cases, and the average number of branching steps has reduced to between 1/5 and 1/3 of the one without the machine learning model. The percentage of SAT instances where the machine learned heuristic-enhanced algorithm solved SAT in a single pass reached levels of 80-90%, depending on the set of features used.
Słowa kluczowe
Wydawca
Czasopismo
Rocznik
Tom
Strony
441--450
Opis fizyczny
Bibliogr. 14 poz., rys., tab., wykr.
Twórcy
autor
- Fraunhofer FOKUS, Kaiserin Augusta Allee 31, 10589 Berlin, Germany
autor
- University of Bucharest, Academiei 14, Bucharest, Romania
Bibliografia
- [1] Biau, G.: Analysis of a random forests model, The Journal of Machine Learning Research, 98888, 2012, 1063–1095.
- [2] Breiman, L.: Random forests, Machine learning, 45(1), 2001, 5–32.
- [3] Devlin, D., OSullivan, B.: Satisfiability as a classification problem, Proc. of the 19th Irish Conf. on Artificial Intelligence and Cognitive Science, 2008.
- [4] Ganesh, V., Singh, R., Near, J. P., Rinard, M.: AvatarSAT: An Auto-tuning Boolean SAT Solver, 2009.
- [5] Haim, S., Walsh, T.: Restart strategy selection using machine learning techniques, in: Theory and Applications of Satisfiability Testing-SAT 2009, Springer, 2009, 312–325.
- [6] Hutter, F., Hamadi, Y., Hoos, H. H., Leyton-Brown, K.: Performance prediction and automated tuning of randomized and parametric algorithms, in: Principles and Practice of Constraint Programming-CP 2006, Springer, 2006, 213–228.
- [7] Kotthoff, L.: Algorithm Selection for Combinatorial Search Problems: A Survey, arXiv preprint arXiv:1210.7959, 2012.
- [8] Lagoudakis, M. G., Littman, M. L.: Learning to select branching rules in the DPLL procedure for satisfiability, Electronic Notes in Discrete Mathematics, 9, 2001, 16–16.
- [9] Nudelman, E., Leyton-Brown, K., Devkar, A., Shoham, Y., Hoos, H.: Satzilla: An algorithm portfolio for SAT, Solver description, SAT competition, 2004, 2004.
- [10] Nudelman, E., Leyton-Brown, K., Hoos, H. H., Devkar, A., Shoham, Y.: Understanding random SAT: Beyond the clauses-to-variables ratio, in: Principles and Practice of Constraint Programming–CP 2004, Springer, 2004, 438–452.
- [11] Xu, L., Hutter, F., Hoos, H. H., Leyton-Brown, K.: SATzilla-07: The design and analysis of an algorithm portfolio for SAT, in: Principles and Practice of Constraint Programming–CP 2007, Springer, 2007, 712–727.
- [12] Xu, L., Hutter, F., Hoos, H. H., Leyton-Brown, K.: SATzilla: portfolio-based algorithm selection for SAT, Journal of Artificial Intelligence Research, 32(1), 2008, 565–606.
- [13] Xu, L., Hutter, F., Hoos, H. H., Leyton-Brown, K.: SATzilla2009: an automatic algorithm portfolio for SAT, SAT, 4, 2009, 53–55.
- [14] Xu, L., Hutter, F., Shen, J., Hoos, H. H., Leyton-Brown, K.: SATzilla2012: Improved algorithm selection based on cost-sensitive classification models, Proceedings of SAT Challenge 2012; Solver and, 2012, 57.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e3231e09-97e0-48a2-aa8e-2356fe3c411b