1. Aarts, F., Kuppens, H., Tretmans, J., Vaandrager, F.W., Verwer, S.: Improving active Mealy machine learning for protocol conformance testing. Mach. Learn. 96(1–2), 189–224 (2014). https://doi.org/10.1007/s10994-013-5405-0
2. Aichernig, B.K., Burghard, C., Korosec, R.: Learning-based testing of an industrial measurement device. In: Badger, J.M., Rozier, K.Y. (eds.) NASA Formal Methods - 11th International Symposium, NFM 2019, Houston, TX, USA, 7–9 May 2019, Proceedings. LNCS, vol. 11460, pp. 1–18. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-20652-9_1
3. Aichernig, B.K., Muskardin, E., Pferscher, A.: Learning-based fuzzing of IoT message brokers. In: 14th IEEE Conference on Software Testing, Verification and Validation, ICST 2021, Porto de Galinhas, Brazil, 12–16 April 2021, pp. 47–58. IEEE (2021). https://doi.org/10.1109/ICST49551.2021.00017
4. Aichernig, B.K., Tappler, M.: Probabilistic black-box reachability checking (extended version). Formal Methods Syst. Des. 54(3), 416–448 (2019). https://doi.org/10.1007/s10703-019-00333-0
5. Alshiekh, M., Bloem, R., Ehlers, R., Könighofer, B., Niekum, S., Topcu, U.: Safe reinforcement learning via shielding. In: McIlraith, S.A., Weinberger, K.Q. (eds.) Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-2018), the 30th innovative Applications of Artificial Intelligence (IAAI-2018), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-2018), New Orleans, Louisiana, USA, 2–7 February 2018, pp. 2669–2678. AAAI Press (2018). https://www.aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/17211