Abstract
SMT solvers include many heuristic components in order to ease the theorem proving process for different logics and problems. Handling these heuristics is a non-trivial task requiring specific knowledge of many theories that even a SMT solver developer may be unaware of. This is the first barrier to break in order to allow end-users to control heuristics aspects of any SMT solver and to successfully build a strategy for their own purposes. We present a first attempt for generating an automatic selection of heuristics in order to improve SMT solver efficiency and to allow end-users to take better advantage of solvers when unknown problems are faced. Evidence of improvement is shown and the basis for future works with evolutionary and/or learning-based algorithms are raised.
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Notes
- 1.
Experiments ran before 2015 SMT-LIB benchmarks were released.
References
Barrett, C., Fontaine, P., Tinelli, C.: The SMT-LIB Standard: Version 2.5. Technical report, Department of Computer Science, The University of Iowa (2015). www.SMT-LIB.org
Burke, E.K., Gendreau, M., Hyde, M., Kendall, G., Ochoa, G., Ozcan, E., Qu, R.: Hyper-heuristics. J. Oper. Res. Soc. 64(12), 1695–1724 (2013)
Gálvez Ramírez, N., Hamadi, Y., Monfroy, E., Saubion, F.: Towards Automated Strategies in Satisfiability Modulo Theory: Appendix. http://www.inf.utfsm.cl/~ngalvez/strategies.pdf
Hamadi, Y., Monfroy, E., Saubion, F.: What is autonomous search? In: van Hentenryck, P., Milano, M. (eds.) Hybrid Optimization. Springer Optimization and Its Applications, vol. 45, pp. 357–391. Springer, New York (2011)
Harman, M.: Software engineering meets evolutionary computation. Computer 44(10), 31–39 (2011)
Harman, M.: The role of Artificial Intelligence in Software Engineering. In: 2012 First International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering (RAISE), pp. 1–6 (2012)
Hutter, F., Hoos, H.H., Leyton-Brown, K., Stützle, T.: ParamILS: an automatic algorithm configuration framework. J. Artif. Int. Res. 36(1), 267–306 (2009)
de Moura, L., Bjørner, N.S.: Z3: an efficient SMT solver. In: Ramakrishnan, C.R., Rehof, J. (eds.) TACAS 2008. LNCS, vol. 4963, pp. 337–340. Springer, Heidelberg (2008)
de Moura, L., Passmore, G.O.: The strategy challenge in SMT solving. In: Bonacina, M.P., Stickel, M.E. (eds.) Automated Reasoning and Mathematics. LNCS, vol. 7788, pp. 15–44. Springer, Heidelberg (2013)
Nannen, V., Eiben, A.: Efficient relevance estimation and value calibration of evolutionary algorithm parameters. In: IEEE Congress on Evolutionary Computation, CEC, pp. 103–110 (2007)
Petke, J., Langdon, W.B., Harman, M.: Applying genetic improvement to MiniSAT. In: Ruhe, G., Zhang, Y. (eds.) SSBSE 2013. LNCS, vol. 8084, pp. 257–262. Springer, Heidelberg (2013)
Riff, M.C., Montero, E.: A new algorithm for reducing metaheuristic design effort. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 3283–3290, June 2013
SMT-LIB Community: SMT-LIB Logics, Accessed 10 September 2015. http://smtlib.cs.uiowa.edu/logics.shtml
Acknowledgment
We want to thanks Christopher Wintersteiger from Microsoft Research for provide us critical information about Z3 theorem prover. Nicolás Gálvez Ramírez is granted by Chilean government: CONICYT-PCHA / Doctorado Nacional / 2013-21130089.
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Gálvez Ramírez, N., Hamadi, Y., Monfroy, E., Saubion, F. (2016). Towards Automated Strategies in Satisfiability Modulo Theory. In: Heywood, M., McDermott, J., Castelli, M., Costa, E., Sim, K. (eds) Genetic Programming. EuroGP 2016. Lecture Notes in Computer Science(), vol 9594. Springer, Cham. https://doi.org/10.1007/978-3-319-30668-1_15
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