A Probabilistic Learning Approach for Counterexample Guided Abstraction Refinement View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2006

AUTHORS

Fei He , Xiaoyu Song , Ming Gu , Jiaguang Sun

ABSTRACT

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. More... »

PAGES

39-50

Book

TITLE

Automated Technology for Verification and Analysis

ISBN

978-3-540-47237-7
978-3-540-47238-4

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/11901914_6

DOI

http://dx.doi.org/10.1007/11901914_6

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1051993117


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