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Learning Büchi Automata and Its Applications

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Engineering Trustworthy Software Systems (SETSS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 11430))

Abstract

In this work, we review an algorithm that learns 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. We introduce the learning algorithm by learning the simple \(\omega \)-regular language \((ab)^{\omega }\): besides giving the readers an overview of the algorithm, it guides them on how the algorithm works step by step. Further, we demonstrate how the learning algorithm can be exploited in classical automata operations such as complementation checking and in the context of termination analysis.

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Acknowledgement

This work has been supported by the National Natural Science Foundation of China (Grant Nos. 61532019, 61761136011), and by the CAP project GZ1023.

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Authors and Affiliations

  1. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, China

    Yong Li, Andrea Turrini & Lijun Zhang

  2. University of Chinese Academy of Sciences, Beijing, China

    Yong Li & Lijun Zhang

  3. Institute of Intelligent Software, Guangzhou, China

    Andrea Turrini & Lijun Zhang

  4. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Yu-Fang Chen

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  1. Yong Li
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  2. Andrea Turrini
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  3. Yu-Fang Chen
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  4. Lijun Zhang
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Correspondence to Andrea Turrini .

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Editors and Affiliations

  1. London South Bank University, London, UK

    Jonathan P. Bowen

  2. Southwest University, Chongqing, China

    Zhiming Liu

  3. Southwest University, Chongqing, China

    Zili Zhang

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Li, Y., Turrini, A., Chen, YF., Zhang, L. (2019). Learning Büchi Automata and Its Applications. In: Bowen, J., Liu, Z., Zhang, Z. (eds) Engineering Trustworthy Software Systems. SETSS 2018. Lecture Notes in Computer Science(), vol 11430. Springer, Cham. https://doi.org/10.1007/978-3-030-17601-3_2

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