Presentation 1995/11/17
Learnability of Ordered Binary Decision Diagrams
Kouichi Hirata, Ayumi Shinohara, Satoshi Matsumoto,
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Abstract(in English) Ordered binary decision diagrams (OBDDs, for short) represent Boolean functions as directed acyclic graphs. In this paper, we investigate the learnability of OBDDs. First, we show that OBDDs representing totally symmetric functions of n variables are polynomial-time learnable with at most n+1 equivalence queries alone, or just n+1 membership queries alone. Also we show that OBDDs representing the conjunction, or disjunction, of literals over n variables are polynomial-time learnable with at most TL equivalence queries alone. Hence, we can conclude that all of them are polynomial-time PAC-learnable with respect to n.
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Keyword(in English) Binary decision diagram / PAC-learning / Exact learning with queries / Algorithmic learning theory
Paper # COMP95-61
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Committee COMP
Conference Date 1995/11/17(1days)
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Registration To Theoretical Foundations of Computing (COMP)
Language ENG
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Learnability of Ordered Binary Decision Diagrams
Sub Title (in English)
Keyword(1) Binary decision diagram
Keyword(2) PAC-learning
Keyword(3) Exact learning with queries
Keyword(4) Algorithmic learning theory
1st Author's Name Kouichi Hirata
1st Author's Affiliation Department of Artificial Intelligence, Kyushu Institute of Technology()
2nd Author's Name Ayumi Shinohara
2nd Author's Affiliation Research Institute of Fundamental Information Science, Kyushu University
3rd Author's Name Satoshi Matsumoto
3rd Author's Affiliation Research Institute of Fundamental Information Science, Kyushu University
Date 1995/11/17
Paper # COMP95-61
Volume (vol) vol.95
Number (no) 374
Page pp.pp.-
#Pages 8
Date of Issue