A Probabilistic Learning Approach for Counterexample Guided Abstraction Refinement

  • Fei He
  • Xiaoyu Song
  • Ming Gu
  • Jiaguang Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4218)


The paper presents a novel probabilistic learning approach to state separation problem which occurs in the counterexample guided abstraction refinement. The method is based on the sample learning technique, evolutionary algorithm and effective probabilistic heuristics. Compared with the previous work by the sampling decision tree learning solver, the proposed method outperforms 2 to 4 orders of magnitude faster and the size of the separation set is 76% smaller on average.


Evolutionary Algorithm Failure State State Pair Uniform Crossover Bound Model Check 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fei He
    • 1
    • 2
  • Xiaoyu Song
    • 3
  • Ming Gu
    • 2
  • Jiaguang Sun
    • 2
  1. 1.Dept. Computer Science & TechnologyTsinghua UniversityBeijingChina
  2. 2.School of SoftwareTsinghua UniversityBeijingChina
  3. 3.Dept. ECEPortland State UniversityOregonUSA

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