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roll-library

Regular Omega Language Learning Library

ROLL is a library of learning algorithms for ω-regular languages. It consists of all the learning algorithms for the complete class of ω-regular languages available in the literature, namely

  • the learning algorithms for FDFAs
    • the algorithm in [5] learns three canonical FDFAs using observation tables, which is based on results in [3],
    • the algorithm in [6] learns three canonical FDFAs using classification trees;
  • and the learning algorithms for Büchi automata
    • the algorithm in [4] learns a Büchi automaton by combining L* algorithm [1] and results in [2],
    • the algorithm in [6] learns the Büchi automata via learning three canonical FDFAs.
    • the algorithm in [10] learns the limit-deterministic Büchi automata via learning three canonical FDFAs.

Since 2023, it also added the support for the limit FDFAs proposed in [11] and the following:

  • the learning algorithms for limit FDFAs using observation tables or classification trees;
  • the learning algorithms for Büchi automata via learning limit FDFAs;
  • and the learning algorithms for transition-based deterministic Büchi automata for an unknown DBA language based on learning limit FDFAs [12];

The ROLL library is implemented in JAVA. Its DFA operations are delegated to the dk.brics.automaton package. We use RABIT tool to check the equivalence of two Büchi automata.

References

[1] Dana Angluin. "Learning regular sets from queries and counterexamples." Information and computation 75.2 (1987): 87-106.

[2] Hugues Calbrix, Maurice Nivat, and Andreas Podelski. "Ultimately periodic words of rational ω-languages." In MFPS. Springer Berlin Heidelberg, 1993: 554-566.

[3] Oded Maler and Ludwig Staiger. "On syntactic congruences for ω-languages." In STACS. Springer Berlin Heidelberg, 1993: 586-594.

[4] Azadeh Farzan, Yu-Fang Chen, Edmund M. Clarke, Yih-Kuen Tsay, Bow-Yaw Wang. "Extending automated compositional verification to the full class of omega-regular languages." In TACAS. Springer Berlin Heidelberg, 2008: 2-17.

[5] Dana Angluin, and Dana Fisman. "Learning regular omega languages." In ALT. Springer International Publishing, 2014: 125-139.

[6] Yong Li, Yu-Fang Chen, Lijun Zhang, and Depeng Liu. "A Novel Learning Algorithm for Büchi Automata based on Family of DFAs and Classification Trees." In TACAS. Springer Berlin Heidelberg, 2017: 208-226. paper

[7] Yong Li, Andrea Turrini, Lijun Zhang and Sven Schewe. "Learning to Complement Büchi Automata." In VMCAI. Springer, Cham, 2018:313-335.

[8] Radu Grosu, Scott A. Smolka. "Monte carlo model checking." In TACAS. Springer-Verlag Berlin, Heidelberg, 2005:271-286.

[9] Yong Li, Andrea Turrini, Xuechao Sun and Lijun Zhang. "Proving Non-inclusion of Büchi Automata Based on Monte Carlo Sampling." In ATVA. Springer, 2020: 467-483. paper

[10] Yong Li, Yu-Fang Chen, Lijun Zhang, and Depeng Liu. "A Novel Learning Algorithm for Büchi Automata based on Family of DFAs and Classification Trees." In I&C. (Added an algorithm to transform an FDFA to a limit-deterministic Büchi automaton) paper

[11] Yong Li, Sven Schewe, and Qiyi Tang. "A Novel Family of Finite Automata for Recognizing and Learning ω-Regular Languages." In ATVA 2023. paper

[12] Yong Li, Sven Schewe, and Qiyi Tang. "Angluin-Style Learning of Deterministic Büchi and Co-Büchi Automata." In IJCAI 2024. paper

For more information, please visit our website http://iscasmc.ios.ac.cn/roll/.