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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.