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Learning real-time automata

Abstract

Real-time automata (RTAs) are a subclass of timed automata with only one clock which resets at each transition. In this paper, we present an active learning algorithm for deterministic real-time automata (DRTAs) in both continuous-time semantics and discrete-time semantics. For a target language recognized by a DRTA \(\mathcal{A}\), we convert the problem of learning DRTA \(\mathcal{A}\) to the problem of learning a canonical real-time automaton \(\mathbb{A}\) with the same recognized language, i.e., \(\mathcal{L}(\mathbb{A})=\mathcal{L}(\mathcal{A})\). The algorithm is inspired by existing learning algorithms for symbolic automata.

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Acknowledgements

Jie AN and Miaomiao ZHANG have been supported partly by National Natural Science Foundation of China (Grant Nos. 61972284, 61472279). Jie AN, Lingtai WANG, Bohua ZHAN and Naijun ZHAN have been supported partly by National Natural Science Foundation of China (Grant Nos. 61625206, 61732001, 61872341). Bohua ZHAN has been partly supported by CAS Pioneer Hundred Talents Program (Grant No. Y9RC585036). The authors would like to thank the anonymous reviewers for their insightful comments and suggestions raised in the reviewing process.

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Correspondence to Bohua Zhan, Naijun Zhan or Miaomiao Zhang.

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An, J., Wang, L., Zhan, B. et al. Learning real-time automata. Sci. China Inf. Sci. 64, 192103 (2021). https://doi.org/10.1007/s11432-019-2767-4

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  • DOI: https://doi.org/10.1007/s11432-019-2767-4

Keywords

  • automaton learning
  • active learning
  • real-time automata