Larson, K. ., Peled, D. ., & Sedwards, S. . (2017). Memory-Efficient Tactics for Randomized LTL Model Checking. Heidelberg, Germany: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-319-72308-2_10
Reference author: Sean Sedwards
First name
Sean
Last name
Sedwards
Kido, K. ., Sedwards, S. ., & Hasuo, I. . (2017). Switching Delays and the Skorokhod Distance in Incrementally Stable Switched Systems. Seoul, South Korea: Springer. Retrieved from https://link.springer.com/chapter/10.1007/978-3-030-17910-6_9
Ilievski, M. ., Sedwards, S. ., Gaurav, A. ., Balakrishnan, A. ., Sarkar, A. ., Lee, J. ., Bouchard, F. ed\ eric, De Iaco, R. ., & Czarnecki, K. . (2019). Design Space of Behaviour Planning for Autonomous Driving. Waterloo. Retrieved from https://arxiv.org/abs/1908.07931 (Original work published 2019)
Ernst, G. ., Sedwards, S. ., Zhang, Z. ., & Hasuo, I. . (2019). Fast Falsification of Hybrid Systems using Probabilistically Adaptive Input. Glasgow, Scotland: Springer.
Lee, J. ., Sedwards, S. ., & Czarnecki, K. . (2023). Uniformly Constrained Reinforcement Learning. Accepted for Publication in Journal of Autonomous Agents and Multi-Agent Systems (JAAMAS): Special Issue on Multi-Objective Decision Making (MODeM).
Larter, S. ., Queiroz, R. ., Sedwards, S. ., Sarkar, A. ., & Czarnecki, K. . (2022). A Hierarchical Pedestrian Behavior Model to Generate Realistic Human Behavior in Traffic Simulation. Aachen, Germany: IEEE. https://doi.org/10.1109/IV51971.2022.9827035 (Original work published 2022)
Bouchard, F. ed\ eric, Sedwards, S. ., & Czarnecki, K. . (2022). A Rule-Based Behaviour Planner for Autonomous Driving. Berlin (virtual): Springer.
Ernst, G. ., Sedwards, S. ., Zhang, Z. ., & Hasuo, I. . (2021). Falsification of Hybrid Systems Using Adaptive Probabilistic Search. ACM Transactions on Modeling and Computer Simulation (TOMACS), 31, 1-22. https://doi.org/10.1145/3459605
Abdelzad, V. ., Lee, J. ., Sedwards, S. ., Soltani, S. ., & Czarnecki, K. . (2021). Non-divergent Imitation for Verification of Complex Learned Controllers. Shenzhen, China (virtual): IEEE. https://doi.org/10.1109/IJCNN52387.2021.9533410
Lee, J. ., Sedwards, S. ., & Czarnecki, K. . (2021). Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. Recursive Constraints to Prevent Instability in Constrained Reinforcement Learning. Presented at the. Online at http://modem2021.cs.nuigalway.ie/. Retrieved from https://arxiv.org/abs/2201.07958