A Framework for Autonomous Generation of Strategies in Satisfiability Modulo Theories

Abstract : The Strategy Challenge in Satisfiability Modulo Theories (SMT) claims to build theoretical and practical tools allowing users to exert strategic control over core heuristic aspects of high-performance SMT solvers. In this work, we focus in Z3 Theorem Prover: one of the most efficient SMT solver according to the SMT Competition, SMT-COMP. In SMT solvers, the definition of a strategy relies on a set of tools that can be scheduled and configured in order to guide the search for a (un)satisfiability proof of a given instance. In this thesis, we address the Strategy Challenge in SMT defining a framework for the autonomous generation of strategies in Z3, i.e. a practical system to automatically generate SMT strategies without the use of expert knowledge. This framework is applied through an incremental evolutionary approach starting from basic algorithms to more complex genetic constructions. This framework formalise strategies modification as rewriting rules, where algorithms acts as enginess to apply them. This intermediate layer, will allow apply any algorithm or operator with no need to being structurally modified, in order to introduce new information in strategies. Validation is done through experiments on classic benchmarks of the SMT-COMP.
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Submitted on : Wednesday, May 22, 2019 - 1:27:03 PM
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  • HAL Id : tel-02136805, version 1


Nicolas Galvez Ramirez. A Framework for Autonomous Generation of Strategies in Satisfiability Modulo Theories. Computation and Language [cs.CL]. Université d'Angers; Universidad técnica Federico Santa María (Valparaiso, Chili), 2018. English. ⟨NNT : 2018ANGE0026⟩. ⟨tel-02136805⟩



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