J
Jean-François Raskin
Researcher at Université libre de Bruxelles
Publications - 306
Citations - 8087
Jean-François Raskin is an academic researcher from Université libre de Bruxelles. The author has contributed to research in topics: Decidability & Markov decision process. The author has an hindex of 47, co-authored 293 publications receiving 7429 citations. Previous affiliations of Jean-François Raskin include Free University of Brussels & Université de Namur.
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Proceedings ArticleDOI
Model checking lots of systems: efficient verification of temporal properties in software product lines
TL;DR: This paper first extends transition systems with features in order to describe the combined behaviour of an entire system family, and defines and implements a model checking technique that allows to verify such transition systems against temporal properties.
BookDOI
Tools and Algorithms for the Construction and Analysis of Systems: 22nd International Conference, TACAS 2016 held as part of the european joint conferences on theory and practice of software, ETAPS 2016 Eindhoven, The Netherlands, April 2-8, 2016 proceedings
TL;DR: This paper presents a meta-analyses of parallel SAT simplification on GPU architectures and its applications in reinforcement learning, artificial intelligence, and bioinformatics.
Journal ArticleDOI
Featured Transition Systems: Foundations for Verifying Variability-Intensive Systems and Their Application to LTL Model Checking
Andreas Classen,Maxime Cordy,Pierre-Yves Schobbens,Patrick Heymans,Axel Legay,Jean-François Raskin +5 more
TL;DR: This paper proposes an efficient automata-based approach to linear time logic (LTL) model checking of variability-intensive systems, and provides an in-depth treatment of the FTS model checking algorithm.
Journal ArticleDOI
Algorithms for Omega-Regular Games with Imperfect Information
TL;DR: An algorithm for computing the set of states from which a player can win with probability 1 with a randomized observation-based strategy for a Buechi objective is given and it is shown that these algorithms are optimal by proving matching lower bounds.
Proceedings ArticleDOI
Generalized Mean-payoff and Energy Games
TL;DR: It is shown that the problem of deciding the existence of a winning strategy for the protagonist is NP-complete, and the previously best known upper bound was EXPSPACE and no lower bound was known, so an optimal coNP-complete bound is given.