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Query learning of bounded-width OBDDs

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Algorithmic Learning Theory (ALT 1996)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1160))

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Abstract

In this paper, we study exact learnability of bounded-width ordered binary decision diagrams (OBDDs), when no ordering of the variables is given and learning is conducted by way of equivalence queries and membership queries. We present a learning algorithm for width-2 OBDDs, an algorithm which uses O(n3) equivalence queries alone, where n is the number of variables. We also present a learning algorithm for width-2 OBDDs which uses O(n) proper equivalence queries and O(n2) membership queries. Further, we show a negative result: that there are no polynomial-time algorithms capable of learning width-3 OBDDs from proper equivalence queries alone.

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Authors and Affiliations

  1. Theory NEC Laboratory RWCP c/o C & C Research Laboratories, NEC Corporation, 4-1-1 Miyazaki Miyamae-ku, 216, Kawasaki, Japan

    Atsuyoshi Nakamura

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  1. Atsuyoshi Nakamura
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Setsuo Arikawa Arun K. Sharma

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© 1996 Springer-Verlag Berlin Heidelberg

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Nakamura, A. (1996). Query learning of bounded-width OBDDs. In: Arikawa, S., Sharma, A.K. (eds) Algorithmic Learning Theory. ALT 1996. Lecture Notes in Computer Science, vol 1160. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61863-5_32

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  • DOI: https://doi.org/10.1007/3-540-61863-5_32

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61863-8

  • Online ISBN: 978-3-540-70719-6

  • eBook Packages: Springer Book Archive

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