Budde, C., D’Argenio, P., Hartmanns, A., & Sedwards, S. (2018). A Statistical Model Checker for Nondeterminism and Rare Events Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-89963-3_20
References
Filter by:
2018
Colwell, I. (2018). Runtime Restriction of the Operational Design Domain: A Safety Concept for Automated Vehicles Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13398 (Original work published 2018)
Juodisius, P., Sarkar, A., Mukkamala, R. R., Antkiewicz, M., Czarnecki, K., & Wąsowski, A. (2018). Clafer: Lightweight Modeling of Structure and Behaviour The Art, Science, and Engineering of Programming Journal, 3. https://doi.org/10.22152/programming-journal.org/2019/3/2 (Original work published 2018)
Liang, J. H. (2018). Machine Learning for SAT Solvers Waterloo, ON, Canada. Retrieved from http://hdl.handle.net/10012/14207 (Original work published 2018)
Czarnecki, K., & Salay, R. (2018). Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving Presented at the Towards a Framework to Manage Perceptual Uncertainty for Safe Automated Driving conference. Väster\r as, Sweden: Springer. (Original work published 2018)
Zhang, Z., Ernst, G., Hasuo, I., & Sedwards, S. (2018). Time-Staging Enhancement of Hybrid System Falsification Porto, Portugal: IEEE. Retrieved from https://ieeexplore.ieee.org/abstract/document/8429475
Zulkoski, E. (2018). Understanding and Enhancing CDCL-based SAT Solvers Waterloo. Retrieved from https://uwspace.uwaterloo.ca/handle/10012/13525 (Original work published 2018)
Given-Wilson, T., Legay, A., Sedwards, S., & Zendra, O. (2018). Group abstraction for assisted navigation of social activities in intelligent environments Springer Journal of Reliable Intelligent Environments, 4, 107\textendash120. Retrieved from https://link.springer.com/article/10.1007/s40860-018-0058-1
Angus, M., ElBalkini, M., Khan, S., Harakeh, A., Andrienko, O., Reading, C., … Waslander, S. (2018). Unlimited Road-scene Synthetic Annotation (URSA) Dataset Presented at the Unlimited Road-Scene Synthetic Annotation (URSA) Dataset conference. Maui, Hawaii, USA: IEEE. Retrieved from https://arxiv.org/abs/1807.06056 (Original work published 2018)
Zhang, Z., Ernst, G., Sedwards, S., Arcani, P., & Hasuo, I. (2018). Two-Layered Falsification of Hybrid Systems Guided by Monte Carlo Tree Search. IEEE TCAD Torino, Italy: IEEE. Retrieved from https://ieeexplore.ieee.org/document/8418450