Solving Simple Stochastic Tail Games

Abstract : Stochastic games are a natural model for open reactive processes: one player represents the controller and his opponent represents a hostile environnment. Teh evolution of the system depends on the decisions of the players, supplemented by random transitions. There are two main algorithmic problems on such games: computing the values (quantitative analysis) and deciding whether a player can win with probability 1 (qualitative analysis). In this paper we reduce the quantitative analysis to the qualitative analysis: we provide an algorithm for computing values which uses qualitative analysis as a sub-procedure. The correctness proof of this algorithm reveals several nice properties of perfect-information stochastic tail games, in particular the existence of optimal strategies. We apply these results to games whose winning conditions are boolean combinations of mean-payoff and Buchi conditions.
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Contributor : Hugo Gimbert <>
Submitted on : Friday, September 4, 2009 - 10:13:00 AM
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  • HAL Id : hal-00413430, version 1



Hugo Gimbert, Florian Horn. Solving Simple Stochastic Tail Games. SODA'10 (Symposium on Discrete Algorithms), Jan 2010, Austin, United States. pp.1000. ⟨hal-00413430⟩



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