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are efficiently identifiable in the limit. In: Dediu, A., Ionescu, A., Mart´ ın-Vide, C.
(eds.) LATA 2009. LNCS, vol. 5457, pp. 740–751. Springer, Heidelberg (2009).
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40. Verwer, S., de Weerdt, M., Witteveen, C.: The efficiency of identifying timed automata and
the power of clocks. Information and Computation 209(3), 606–625 (2011)
41. Verwer, S., de Weerdt, M., Witteveen, C.: Efficiently identifying deterministic real-time automata from labeled data. Machine Learning 86(3), 295–333 (2012)
42. Verwer, S., Weerdt, M.D., Witteveen, C.: An algorithm for learning real-time automata. In:
Proceedings of the 18th Annual Machine Learning Conference of Belgium and the Netherlands, Benelearn 2007. pp. 57–64 (2007)
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license
and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.
J. An et al.
35. Stigge, M., Ekberg, P., Guan, N., Yi, W.: The digraph real-time task model. In: Proceedings
of 17th IEEE Real-Time and Embedded Technology and Applications Symposium, RTAS
2011. pp. 71–80. IEEE Computer Society (2011)
36. Tappler, M., Aichernig, B.K., Bacci, G., Eichlseder, M., Larsen, K.G.: L
∗ -based
learning of Markov decision processes. In: ter Beek, M.H., McIver, A., Oliveira,
J.N. (eds.) FM 2019. LNCS, vol. 11800, pp. 651–669. Springer, Heidelberg (2019).
https://doi.org/10.1007/978-3-030-30942-8 38
37. Tappler, M., Aichernig, B.K., Larsen, K.G., Lorber, F.: Time to learn - learning timed automata from tests. In: Andr´ e, ´
E., Stoelinga, M. (eds.) FORMATS 2019. LNCS, vol. 11750,
pp. 216–235. Springer, Heidelberg (2019). https://doi.org/10.1007/978-3-030-29662-9 13
38. Vaandrager, F.W.: Model learning. Communications of the ACM 60(2), 86–95 (2017)
39. Verwer, S., de Weerdt, M., Witteveen, C.: One-clock deterministic timed automata
are efficiently identifiable in the limit. In: Dediu, A., Ionescu, A., Mart´ ın-Vide, C.
(eds.) LATA 2009. LNCS, vol. 5457, pp. 740–751. Springer, Heidelberg (2009).
https://doi.org/10.1007/978-3-642-00982-2 63
40. Verwer, S., de Weerdt, M., Witteveen, C.: The efficiency of identifying timed automata and
the power of clocks. Information and Computation 209(3), 606–625 (2011)
41. Verwer, S., de Weerdt, M., Witteveen, C.: Efficiently identifying deterministic real-time automata from labeled data. Machine Learning 86(3), 295–333 (2012)
42. Verwer, S., Weerdt, M.D., Witteveen, C.: An algorithm for learning real-time automata. In:
Proceedings of the 18th Annual Machine Learning Conference of Belgium and the Netherlands, Benelearn 2007. pp. 57–64 (2007)
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing,
adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license
and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter’s Creative
Commons license, unless indicated otherwise in a credit line to the material. If material is not
included in the chapter’s Creative Commons license and your intended use is not permitted by
statutory regulation or exceeds the permitted use, you will need to obtain permission directly from
the copyright holder.