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International Conference on Computer Aided Verification

CAV 2012: Computer Aided Verification pp 55–70Cite as

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Learning Boolean Functions Incrementally

Learning Boolean Functions Incrementally

  • Yu-Fang Chen18 &
  • Bow-Yaw Wang18 
  • Conference paper
  • 3750 Accesses

  • 8 Citations

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7358)

Abstract

Classical learning algorithms for Boolean functions assume that unknown targets are Boolean functions over fixed variables. The assumption precludes scenarios where indefinitely many variables are needed. It also induces unnecessary queries when many variables are redundant. Based on a classical learning algorithm for Boolean functions, we develop two learning algorithms to infer Boolean functions over enlarging sets of ordered variables. We evaluate their performance in the learning-based loop invariant generation framework.

This work is partially supported by the National Science Council of Taiwan under the grant numbers 99-2218-E-001-002-MY3 and 100-2221-E-002-116-.

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Author information

Authors and Affiliations

  1. Academia Sinica, Taiwan

    Yu-Fang Chen & Bow-Yaw Wang

Authors
  1. Yu-Fang Chen
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  2. Bow-Yaw Wang
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Editor information

Editors and Affiliations

  1. Dept. of Computer Science, University of Illinois at Urbana-Champaign, 3226 Siebel Center, 201 N. Goodwin Avenue, 61801-2302, Urbana, IL, USA

    P. Madhusudan

  2. Dept. of Electrical Engineering and Computer Science, University of California, Berkeley, 253 Cory Hall # 1770, 94720-1770, Berkeley, CA, USA

    Sanjit A. Seshia

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Chen, YF., Wang, BY. (2012). Learning Boolean Functions Incrementally. In: Madhusudan, P., Seshia, S.A. (eds) Computer Aided Verification. CAV 2012. Lecture Notes in Computer Science, vol 7358. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31424-7_10

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  • DOI: https://doi.org/10.1007/978-3-642-31424-7_10

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