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- W67698512 abstract "The theory of graph games is the foundation for modeling and synthesizing reactive processes. In the synthesis of stochastic processes, we use (2frac{1}{2})-player games where some transitions of the game graph are controlled by two adversarial players, the System and the Environment, and the other transitions are determined probabilistically. We consider (2frac{1}{2})-player games where the objective of the System is the conjunction of a qualitative objective (specified as a parity condition) and a quantitative objective (specified as a mean-payoff condition). We establish that the problem of deciding whether the System can ensure that the probability to satisfy the mean-payoff parity objective is at least a given threshold is in NP ∩ coNP, matching the best known bound in the special case of 2-player games (where all transitions are deterministic). We present an algorithm running in time O(d·n 2d · MeanGame) to compute the set of almost-sure winning states from which the objective can be ensured with probability 1, where n is the number of states of the game, d the number of priorities of the parity objective, and MeanGame is the complexity to compute the set of almost-sure winning states in (2frac{1}{2})-player mean-payoff games. Our results are useful in the synthesis of stochastic reactive systems with both functional requirement (given as a qualitative objective) and performance requirement (given as a quantitative objective)." @default.
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- W67698512 date "2014-01-01" @default.
- W67698512 modified "2023-10-11" @default.
- W67698512 title "Perfect-Information Stochastic Mean-Payoff Parity Games" @default.
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- W67698512 doi "https://doi.org/10.1007/978-3-642-54830-7_14" @default.
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