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  • 标题:Probabilistic abstraction for model checking: An approach based on property testing
  • 本地全文:下载
  • 作者:Sophie Laplante ; Richard Lassaigne ; Frederic Magniez
  • 期刊名称:Electronic Colloquium on Computational Complexity
  • 印刷版ISSN:1433-8092
  • 出版年度:2001
  • 卷号:2001
  • 出版社:Universität Trier, Lehrstuhl für Theoretische Computer-Forschung
  • 摘要:In model checking, program correctness on all inputs is verified by considering the transition system underlying a given program. In practice, the system can be intractably large. In property testing, a property of a single input is verified by looking at a small subset of that input. We join the strengths of both approaches by introducing to model checking the notion of probabilistic abstraction. We put forth the notion of \eps -reducibility which is implicit in many property testers. Our probabilistic abstraction associates a set of small random transition systems to a program. Under some conditions, these transition systems are sufficient to guarantee that a program approximately decides on all its inputs a property like bipartiteness, k -colorability, or any first order graph properties of type . We give a concrete example of an abstraction for a program which decides bipartiteness. Finally, we show that abstraction is necessary by proving an exponential lower bound on OBDDs for approximate bipartiteness.
  • 关键词:abstraction , Communication complexity , model checking , Property Testing
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