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Quantitative analysis of stochastic systems : priority games and populations of Markov chains

Abstract : This thesis examines some quantitative questions in the framework of two different stochastic models. It is divided into two parts: the first part examines a new class of stochastic games with priority payoff. This class of games contains as proper subclasses the parity games extensively studied in computer science, and limsup and liminf games studied in game theory. The second part of the thesis examines some natural but involved questions about distributions, studied in the simple framework of finite state Markov chain.In the first part, we examine two-player zero-sum games focusing on a particular payoff function that we call the priority payoff. This payoff function generalizes the payoff used in parity games. We consider both turn-based stochastic priority games and concurrent priority games. Our approach to priority games is based on the concept of the nearest fixed point of monotone nonexpansive mappings and extends the mu-calculus approach to priority games.The second part of the thesis deals with population questions. Roughly speaking, we examine how a probability distribution over states evolves in time. More specifically, we are interested in questions like the following one: from an initial distribution, can the population reach at some moment a distribution with a probability mass exceeding a given threshold in state Goal? It turns out that this type of questions is much more difficult to handle than the questions concerning individual trajectories: it is not known for the simple model of Markov chains whether population questions are decidable. We study restrictions of Markov chains ensuring decidability of population questions.
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Bruno Karelović. Quantitative analysis of stochastic systems : priority games and populations of Markov chains. Computer Science and Game Theory [cs.GT]. Université Sorbonne Paris Cité, 2017. English. ⟨NNT : 2017USPCC165⟩. ⟨tel-02080440⟩

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