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Lookup NU author(s): Dr Sadegh SoudjaniORCiD
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
The formal verification and controller synthesis for general Markov decision processes (gMDPs) that evolve over uncountable state spaces are computationally hard and thus generally rely on the use of approximate abstractions. In this paper, we contribute to the state of the art of control synthesis for temporal logic properties by computing and quantifying a less conservative gridding of the continuous state space of linear stochastic dynamic systems and by giving a new approach for control synthesis and verification that is robust to the incurred approximation errors. The approximation errors are expressed as both deviations in the outputs of the gMDPs and in the probabilistic transitions.
Author(s): Haesaert S, Soudjani S, Abate A
Editor(s): Alessandro Abate, Antoine Girard, Maurice Heemels
Publication type: Conference Proceedings (inc. Abstract)
Publication status: Published
Conference Name: 6th IFAC Conference on Analysis and Design of Hybrid Systems (ADHS 2018)
Year of Conference: 2018
Pages: 73-78
Online publication date: 31/08/2018
Acceptance date: 01/03/2018
Date deposited: 04/11/2019
ISSN: 2405-8963
Publisher: International Federation of Automatic Control
URL: https://doi.org/10.1016/j.ifacol.2018.08.013
DOI: 10.1016/j.ifacol.2018.08.013
Series Title: IFAC-PapersOnline
