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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Stackelberg-Pareto Synthesis (Extended Version)
TL;DR: In this paper , the Stackelberg Pareto synthesis problem is studied in the context of two-player games, where one player announces a strategy and the other player responds rationally with a strategy that is an optimal response.
Posted Content
Lifted Model Checking for Relational MDPs.
TL;DR: PCTL-REBEL as discussed by the authors is a lifted model checking approach for verifying pCTL properties on relational MDPs, which exploits symmetries and reasons at an abstract relational level.
Journal ArticleDOI
Special issue: Selected papers of the 11th International Symposium on Games, Automata, Logics, and Formal Verification (GandALF 2020)
TL;DR: In this article , a game-theoretic semantics (GTS) for the modal mu-calculus is introduced, which replaces parity games with alternative evaluation games where only finite paths arise; infinite paths are not needed even when the considered transition system is infinite.
Journal ArticleDOI
Subgame-perfect Equilibria in Mean-payoff Games (journal version)
TL;DR: In this paper , the authors provide an effective characterization of all the subgame-perfect equilibria in infinite-time games played on finite graphs with mean-payoff objectives.
Proceedings ArticleDOI
Multiple-environment Markov decision processes
Jean-François Raskin,Ocan Sankur +1 more
TL;DR: In this article, the authors introduce multi-environment Markov decision processes (MEMDPs), which are MDPs with a set of probabilistic transition functions, where the goal is to synthesize a single controller strategy with guaranteed performances against all environments even though the environment is unknown a priori.