Abstractions of discrete-time stochastic systems

Abstractions lead to simpler (usually) discrete representations of dynamical systems. The obtained discrete model is then used to assert properties for the original dynamics using formal methods techniques. In this research direction, my goal is to develop data-driven approaches to construct abstractions of dynamical systems leveraging available data. The main representative papers are:

1. T. Badings, A. Abate, N. Jensen, D. Parker, H. Poonawala, M. Stoelinga, “Sampling-based Robust Control of Autonomous Sysmtems with Non-Gaussian Noise”, AAAI, 2022. Award-winning paper
2. T. Badings, L. Romao, A. Abate, N. Jensen, “Probabilities Are Not Enough: Formal Controller Synthesis for Stochastic Dynamical Models with Epistemic Uncertainty”, AAAI, 2023.
3. T. Badings, L. Romao, A. Abate, D. Parker, H. Poonawala, M. Stoelinga, N. Jensen, “Robust Control for Dynamical Systems with Non-Gaussian Noise via Formal Abstractions”. Journal of Artificial Intelligence Research (JAIR).
4. A. Banse, L. Romao, A. Abate, R. Jungers. “Data-driven memory-dependent abstractions of dynamical systems”. L4DC 2023.
5. A. Banse, L. Romao, A. Abate, R. Jungers. “Data-driven abstractions via adaptive refinements and a Kantorovich metric”. Technical report.
6. M. Engelaar, L. Romao, Y. Gao, M. Lazar, A. Abate, S. Haesaert. “Model Reduction of Linear Stochastic Systems with Preservation of sc-LTL Specifications”. Technical report.