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Markov Decision Processes with Multiple Long-run Average Objectives

Tomáš Brázdil ; Václav Brožek ; Krishnendu Chatterjee ; Vojtěch Forejt ; Antonín Kučera.

We study Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) functions. We consider two different objectives, namely, expectation and satisfaction objectives. Given an MDP with k limit-average functions, in the expectation objective the goal is to maximize the expected&nbsp;[&hellip;]

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Permissive Controller Synthesis for Probabilistic Systems

Klaus Drager ; Vojtech Forejt ; Marta Kwiatkowska ; David Parker ; Mateusz Ujma.

We propose novel controller synthesis techniques for probabilistic systems modelled using stochastic two-player games: one player acts as a controller, the second represents its environment, and probability is used to capture uncertainty arising due to, for example, unreliable sensors or faulty&nbsp;[&hellip;]

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Game Characterization of Probabilistic Bisimilarity, and Applications to Pushdown Automata

Vojtěch Forejt ; Petr Jančar ; Stefan Kiefer ; James Worrell.

We study the bisimilarity problem for probabilistic pushdown automata (pPDA) and subclasses thereof. Our definition of pPDA allows both probabilistic and non-deterministic branching, generalising the classical notion of pushdown automata (without epsilon-transitions). We first show a general&nbsp;[&hellip;]

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