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Energy and mean-payoff parity Markov decision processes
, 2011
"... Abstract. We consider Markov Decision Processes (MDPs) with mean-payoff parity and energy parity objectives. In system design, the parity objective is used to encode ω-regular specifications, while the mean-payoff and energy objectives can be used to model quantitative resource constraints. The ener ..."
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Cited by 6 (2 self)
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Abstract. We consider Markov Decision Processes (MDPs) with mean-payoff parity and energy parity objectives. In system design, the parity objective is used to encode ω-regular specifications, while the mean-payoff and energy objectives can be used to model quantitative resource constraints
Faster and Dynamic Algorithms For Maximal End-Component Decomposition And Related Graph Problems In Probabilistic Verification
"... We present faster and dynamic algorithms for the following problems arising in probabilistic verification: Computation of the maximal end-component (mec) decomposition of Markov decision processes (MDPs), and of the almost sure winning set for reachability and parity objectives in MDPs. We achieve t ..."
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Cited by 20 (10 self)
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We present faster and dynamic algorithms for the following problems arising in probabilistic verification: Computation of the maximal end-component (mec) decomposition of Markov decision processes (MDPs), and of the almost sure winning set for reachability and parity objectives in MDPs. We achieve
R.: QUASY: Quantitative Synthesis Tool
- TACAS 2011. LNCS
, 2011
"... Abstract. We present the tool QUASY, a quantitative synthesis tool. QUASY takes qualitative and quantitative specifications and automatically constructs a system that satisfies the qualitative specification and optimizes the quantitative specification, if such a system exists. The user can choose be ..."
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Cited by 6 (2 self)
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to instances of 2-player games and Markov Decision Processes (MDPs) with quantitative winning objectives. QUASY can also be seen as a game solver for quantitative games. Most notable, it can solve lexicographic mean-payoff games with2players, MDPs with mean-payoff objectives, and ergodic MDPs with meanpayoff
Measuring and synthesizing systems in probabilistic environments
- CoRR
"... Abstract. Often one has a preference order among the different systems that satisfy a given specification. Under a probabilistic assumption about the possible inputs, such a preference order is naturally expressed by a weighted automaton, which assigns to each word a value, such that a system is pre ..."
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Cited by 22 (11 self)
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, which can be done in polynomial time. For general omega-regular specifications, the solution rests on a new, polynomial-time algorithm for computing optimal strategies in MDPs with mean-payoff parity objectives. We present some experimental results showing optimal systems that were automatically
Multiple-environment markov decision processes.
, 2014
"... Abstract We introduce Multi-Environment Markov Decision Processes (MEMDPs) which are MDPs with a set of probabilistic transition functions. The goal in an MEMDP is to synthesize a single controller strategy with guaranteed performances against all environments even though the environment is unknown ..."
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Cited by 1 (1 self)
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of the second population. Facing two potentially indistinguishable environments can be easily modeled with a partially observable MDPs. Unfortunately, this model is particularly intractable [3] (e. g. quantitative reachability, safety, and parity objectives, and even qualitative parity objectives * Supported
Recursive Stochastic Games with Positive Rewards
"... Abstract. We study the complexity of a class of Markov decision processes and, more generally, stochastic games, called 1-exit Recursive Markov Decision Processes (1-RMDPs) and Simple Stochastic Games (1-RSSGs) with strictly positive rewards. These are a class of finitely presented countable-state z ..."
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Cited by 10 (4 self)
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-state zero-sum stochastic games, with total expected reward objective. They subsume standard finite-state MDPs and Condon’s simple stochastic games and correspond to optimization and game versions of several classic stochastic models, with rewards. Such stochastic models arise naturally as models