@article{LI2020104678,
    Abstract = {In this paper, we propose a novel algorithm to learn a B{\"u}chi automaton from a teacher who knows an {$\omega$}-regular language. The learned B{\"u}chi automaton can be a nondeterministic B{\"u}chi automaton or a limit deterministic B{\"u}chi automaton. The learning algorithm is based on learning a formalism called family of DFAs (FDFAs) recently proposed by Angluin and Fisman. 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 that required by the table-based algorithm proposed by Angluin and Fisman. We implement the proposed learning algorithms in the learning library ROLL (Regular Omega Language Learning), which also consists of other complete {$\omega$}-regular learning algorithms available in the literature. Experimental results show that our tree-based learning algorithms have the best performance among others regarding the number of solved learning tasks.},
    Author = {Li, Yong and Chen, Yu-Fang and Zhang, Lijun and Liu, Depeng},
    File = {A novel learning algorithm for Büchi automata based on family of DFAs and classification trees - 1-s2.0-S0890540120301711-main.pdf},
    ISSN = {0890-5401},
    Journal = {Information and Computation},
    Keywords = {B{\"u}chi automata, Learning algorithm, Automata learning, Observation table, Family of DFAs, Classification tree,},
    Pages = {104678},
    Title = {A novel learning algorithm for B{\"u}chi automata based on family of DFAs and classification trees},
    URL = {https://www.sciencedirect.com/science/article/pii/S0890540120301711},
    Year = {2020},
    bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0890540120301711},
    bdsk-url-2 = {https://doi.org/10.1016/j.ic.2020.104678},
    date-added = {2021-08-02 09:11:39 +0200},
    date-modified = {2021-08-02 09:11:39 +0200},
    doi = {10.1016/j.ic.2020.104678}
}

@article{LI2020104678, Abstract = {In this paper, we propose a novel algorithm to learn a B{\"u}chi automaton from a teacher who knows an {$\omega$}-regular language. The learned B{\"u}chi automaton can be a nondeterministic B{\"u}chi automaton or a limit deterministic B{\"u}chi automaton. The learning algorithm is based on learning a formalism called family of DFAs (FDFAs) recently proposed by Angluin and Fisman. 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 that required by the table-based algorithm proposed by Angluin and Fisman. We implement the proposed learning algorithms in the learning library ROLL (Regular Omega Language Learning), which also consists of other complete {$\omega$}-regular learning algorithms available in the literature. Experimental results show that our tree-based learning algorithms have the best performance among others regarding the number of solved learning tasks.}, Author = {Li, Yong and Chen, Yu-Fang and Zhang, Lijun and Liu, Depeng}, File = {A novel learning algorithm for Büchi automata based on family of DFAs and classification trees - 1-s2.0-S0890540120301711-main.pdf}, ISSN = {0890-5401}, Journal = {Information and Computation}, Keywords = {B{\"u}chi automata, Learning algorithm, Automata learning, Observation table, Family of DFAs, Classification tree,}, Pages = {104678}, Title = {A novel learning algorithm for B{\"u}chi automata based on family of DFAs and classification trees}, URL = {https://www.sciencedirect.com/science/article/pii/S0890540120301711}, Year = {2020}, bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0890540120301711}, bdsk-url-2 = {https://doi.org/10.1016/j.ic.2020.104678}, date-added = {2021-08-02 09:11:39 +0200}, date-modified = {2021-08-02 09:11:39 +0200}, doi = {10.1016/j.ic.2020.104678} }