no code implementations • 30 Apr 2024 • Rayan Mazouz, John Skovbekk, Frederik Baymler Mathiesen, Eric Frew, Luca Laurenti, Morteza Lahijanian
This paper introduces a method of identifying a maximal set of safe strategies from data for stochastic systems with unknown dynamics using barrier certificates.
no code implementations • 8 Jan 2024 • Frederik Baymler Mathiesen, Morteza Lahijanian, Luca Laurenti
In this paper, we present IntervalMDP. jl, a Julia package for probabilistic analysis of interval Markov Decision Processes (IMDPs).
no code implementations • 17 Nov 2023 • Xinyu Wang, Luzia Knoedler, Frederik Baymler Mathiesen, Javier Alonso-Mora
In this work, we leverage bound propagation techniques and the Branch-and-Bound scheme to efficiently verify that a neural network satisfies the conditions to be a CBF over the continuous state space.
no code implementations • 10 Apr 2023 • Frederik Baymler Mathiesen, Licio Romao, Simeon C. Calvert, Alessandro Abate, Luca Laurenti
In particular, we show that the stochastic program to synthesize a SBF can be relaxed into a chance-constrained optimisation problem on which scenario approach theory applies.
1 code implementation • 3 Jun 2022 • Frederik Baymler Mathiesen, Simeon Calvert, Luca Laurenti
In this paper, we parameterize a barrier function as a neural network and show that techniques for robust training of neural networks can be successfully employed to find neural barrier functions.
