International audienceIn this paper, we are interested in the synthesis of schedulers in double-weighted Markov decision processes, which satisfy both a percentile constraint over a weighted reachability condition, and a quantitative constraint on the expected value of a random variable defined using a weighted reacha-bility condition. This problem is inspired by the modelization of an electric-vehicle charging problem. We study the cartography of the problem, when one parameter varies, and show how a partial cartography can be obtained via two sequences of opimization problems. We discuss completeness and feasability of the method
We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives...
We study and provide efficient algorithms for multi-objective model checking problems for Markov Dec...
Markov Decision Processes (MDPs) constitute a mathematical framework for modelling systems featuring...
International audienceIn this paper, we are interested in the synthesis of schedulers in double-weig...
International audienceIn this paper, we are interested in the synthesis of schedulers in double-weig...
Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with m...
Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with m...
We consider Markov decision processes (MDPs) with multiple limit-average (ormean-payoff) objectives....
In this paper we address the following basic feasibility problem for infinite-horizon Markov decisio...
This paper studies parametric Markov decision processes (pMDPs), an extension to Markov decision pro...
AbstractVerification of reachability properties for probabilistic systems is usually based on varian...
In this paper we address the following basic feasibility problem for infinite-horizon Markov decisio...
We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decisi...
We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decisi...
We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decisi...
We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives...
We study and provide efficient algorithms for multi-objective model checking problems for Markov Dec...
Markov Decision Processes (MDPs) constitute a mathematical framework for modelling systems featuring...
International audienceIn this paper, we are interested in the synthesis of schedulers in double-weig...
International audienceIn this paper, we are interested in the synthesis of schedulers in double-weig...
Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with m...
Markov decision processes (MDPs) with multi-dimensional weights are useful to analyze systems with m...
We consider Markov decision processes (MDPs) with multiple limit-average (ormean-payoff) objectives....
In this paper we address the following basic feasibility problem for infinite-horizon Markov decisio...
This paper studies parametric Markov decision processes (pMDPs), an extension to Markov decision pro...
AbstractVerification of reachability properties for probabilistic systems is usually based on varian...
In this paper we address the following basic feasibility problem for infinite-horizon Markov decisio...
We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decisi...
We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decisi...
We provide a memory-efficient algorithm for multi-objective model checking problems on Markov decisi...
We consider Markov decision processes (MDPs) with multiple limit-average (or mean-payoff) objectives...
We study and provide efficient algorithms for multi-objective model checking problems for Markov Dec...
Markov Decision Processes (MDPs) constitute a mathematical framework for modelling systems featuring...