Towards Automated Strategies in Satisfiability Modulo Theory

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• 关键词：SMT ; Strategy ; Z3 ; Learning ; Tuning ; Evolutionary algorithm ; Search ; based software engineering
• 刊名：Lecture Notes in Computer Science
• 出版年：2016
• 出版时间：2016
• 年：2016
• 卷：9594
• 期：1
• 页码：230-245
• 全文大小：629 KB
• 参考文献：1.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
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  3.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
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  13.SMT-LIB Community: SMT-LIB Logics, Accessed 10 September 2015. http://​smtlib.​cs.​uiowa.​edu/​logics.​shtml
  
• 作者单位：Nicolás Gálvez Ramírez (18) (19)
  Youssef Hamadi (21)
  Eric Monfroy (20)
  Frédéric Saubion (19)
  
  18. LabDII, Universidad Técnica Federico Santa María, Valparaíso, Chile
  19. LERIA, Université d’Angers, Angers, France
  21. LIX, Ecole Polytechnique, Palaiseau, France
  20. LINA, UMR CNRS 6241, TASC INRIA, Université de Nantes, Nantes, France
  
• 丛书名：Genetic Programming
• ISBN：978-3-319-30668-1
• 刊物类别：Computer Science
• 刊物主题：Artificial Intelligence and Robotics
  Computer Communication Networks
  Software Engineering
  Data Encryption
  Database Management
  Computation by Abstract Devices
  Algorithm Analysis and Problem Complexity
  
• 出版者：Springer Berlin / Heidelberg
• ISSN：1611-3349

文摘

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.