A Novel Learning Algorithm for Büchi Automata Based on Family of DFAs and Classification Trees

  • Yong Li
  • Yu-Fang Chen
  • Lijun ZhangEmail author
  • Depeng Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10205)


In this paper, we propose a novel algorithm to learn a Büchi automaton from a teacher who knows an \(\omega \)-regular language. The algorithm is based on learning a formalism named family of DFAs (FDFAs) recently proposed by Angluin and Fisman [10]. The main catch is that we use a classification tree structure instead of the standard observation table structure. The worst case storage space required by our algorithm is quadratically better than the table-based algorithm proposed in [10]. We implement the first publicly available library ROLL (Regular Omega Language Learning), which consists of all \(\omega \)-regular learning algorithms available in the literature and the new algorithms proposed in this paper. Experimental results show that our tree-based algorithms have the best performance among others regarding the number of solved learning tasks.



This work has been supported by the National Basic Research (973) Program of China under Grant No. 2014CB340701, the CAS Fellowship for Visiting Scientists from Taiwan under Grant No. 2015TW2GA0001, the National Natural Science Foundation of China (Grants 61532019, 61472473), the CAS/SAFEA International Partnership Program for Creative Research Teams, the Sino-German CDZ project CAP (GZ 1023), and the MOST project No. 103-2221-E-001-019-MY3.


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Copyright information

© Springer-Verlag GmbH Germany 2017

Authors and Affiliations

  • Yong Li
    • 1
    • 2
  • Yu-Fang Chen
    • 3
  • Lijun Zhang
    • 1
    • 2
    Email author
  • Depeng Liu
    • 1
    • 2
  1. 1.State Key Laboratory of Computer ScienceInstitute of Software, CASBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina
  3. 3.Institute of Information ScienceAcademia SinicaTaipeiTaiwan

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