@inproceedings{10.1007/978-3-662-54577-5_12,
    Abstract = {In this paper, we propose a novel algorithm to learn a B{\"u}chi automaton from a teacher who knows an {\$}{\$}{\backslash}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 {\$}{\$}{\backslash}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.},
    Address = {Berlin, Heidelberg},
    Author = {Li, Yong and Chen, Yu-Fang and Zhang, Lijun and Liu, Depeng},
    BookTitle = {Tools and Algorithms for the Construction and Analysis of Systems},
    Editor = {Legay, Axel and Margaria, Tiziana},
    File = {10.1007\%2F978-3-662-54577-5\_12 (0) - a - a - m.pdf},
    ISBN = {978-3-662-54577-5},
    Keywords = {citesme!},
    Pages = {208--226},
    Publisher = {Springer Berlin Heidelberg},
    Title = {A Novel Learning Algorithm for B{\"u}chi Automata Based on Family of DFAs and Classification Trees},
    Year = {2017},
    date-added = {2018-03-29 08:27:12 +0000},
    date-modified = {2018-03-29 08:27:18 +0000},
    doi = {10.1007/978-3-662-54577-5_12}
}

@inproceedings{10.1007/978-3-662-54577-5_12, Abstract = {In this paper, we propose a novel algorithm to learn a B{\"u}chi automaton from a teacher who knows an {\$}{\$}{\backslash}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 {\$}{\$}{\backslash}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.}, Address = {Berlin, Heidelberg}, Author = {Li, Yong and Chen, Yu-Fang and Zhang, Lijun and Liu, Depeng}, BookTitle = {Tools and Algorithms for the Construction and Analysis of Systems}, Editor = {Legay, Axel and Margaria, Tiziana}, File = {10.1007\%2F978-3-662-54577-5_12 (0) - a - a - m.pdf}, ISBN = {978-3-662-54577-5}, Keywords = {citesme!}, Pages = {208--226}, Publisher = {Springer Berlin Heidelberg}, Title = {A Novel Learning Algorithm for B{\"u}chi Automata Based on Family of DFAs and Classification Trees}, Year = {2017}, date-added = {2018-03-29 08:27:12 +0000}, date-modified = {2018-03-29 08:27:18 +0000}, doi = {10.1007/978-3-662-54577-5_12} }