Radboud Repository

      View Item 
      •   Radboud Repository
      • Collections Radboud University
      • Datasets
      • View Item
      •   Radboud Repository
      • Collections Radboud University
      • Datasets
      • View Item
      JavaScript is disabled for your browser. Some features of this site may not work without it.
      BrowseAll of RepositoryCollectionsDepartmentsDate IssuedAuthorsTitlesDocument typeThis CollectionDepartmentsDate IssuedAuthorsTitlesDocument type
      StatisticsView Item Statistics

      Source code and data relevant for the paper 'Model Learning as a Satisfiability Modulo Theories Problem'

      Find Full text
      Creators
      Smetsers, R.H.A.M.
      Fiterau-Brostean, P.
      Vaandrager, F.W.
      Date of Archiving
      2017
      Archive
      DANS EASY
      DOI
      https://doi.org/10.17026/dans-xn2-yewe
      Publication type
      Dataset
      Access level
      open access
      Please use this identifier to cite or link to this item: https://hdl.handle.net/2066/183259   https://hdl.handle.net/2066/183259
      Display more detailsDisplay less details
      Organization
      Software Science
      Audience(s)
      Computer science
      Languages used
      English
      Key words
      automata learning, grammatical inference, formal methods
      Abstract
      This datasets belongs to the following paper: Model Learning as a Satisfiability Modulo Theories Problem. URL = http://www.sws.cs.ru.nl/publications/papers/fvaan/SMT/main.pdf The dataset contains the source code of the tool implementing the SMT-based approach described in the paper, the setups/scripts used to run experiments and experimental logs. Paper Abstract: We explore an approach to model learning that is based on using satisfiability modulo theories (SMT) solvers. To that end, we explain how DFAs, Mealy machines and register automata, and observations of their behavior can be encoded as logic formulas. An SMT solver is then tasked with finding an assignment for such a formula, from which we can extract an automaton of minimal size. We provide an implementation of this approach which we use to conduct experiments on a series of benchmarks. These experiments address both the scalability of the approach and its performance relative to existing active learning tools.
      This item appears in the following Collection(s)
      • Datasets [1237]
      • Faculty of Science [31885]
       
      •  Upload Full Text
      •  Terms of Use
      •  Notice and Takedown
      Bookmark and Share
      Admin login