Interactive Markov chains (IMC) are compositional behavioral models extending both labeled transition systems and continuous-time Markov chains. IMC pair modeling convenience- owed to compositionality properties- with effective verification algorithms and tools- owed to Markov properties. Thus far however, IMC verification did not consider compositionality properties, but considered closed systems. This paper discusses the evaluation of IMC in an open and thus compositional interpretation. For this we embed the IMC into a game that is played with the environment. We devise algorithms that enable us to derive bounds on reachability probabilities that are assured to hold in any composition context
Markov chains are a versatile and widely used means to model an extensive variety of stochastic phen...
In the field of model checking, abstraction refinement has proved to be an extremely successful meth...
International audienceWe present a formal proof system for compositional verifica- tion of probabili...
Interactive Markov chains (IMC) are compositional behavioral models extending both labeled transitio...
Abstract. Interactive Markov chains (IMC) are compositional behavioural models extending labelled tr...
Interactive Markov chains (IMCs) constitute a powerful sto- chastic model that extends both continuo...
Abstract. Interactive Markov chains (IMCs) constitute a powerful sto-chastic model that extends both...
This paper reviews the model of interactive Markov chains (IMCs, for short), an extension of labelle...
Hermanns has introduced interactive Markov chains (IMCs) which arise as an orthogonal extension of l...
We propose to exploit three-valued abstraction to stochastic systems in a compositional way. This co...
Abstract This paper presents new algorithms and accompanying tool support for analyzing interactive ...
AbstractThe usage of process algebras for the performance modeling and evaluation of concurrent syst...
AbstractInterval Markov Chains (IMC), or Markov Chains with probability intervals in the transition ...
We consider Markov decision processes (MDPs) which are a standard model for probabilistic systems. W...
Interval Markov Chains (IMC), or Markov Chains with probability intervals in the transition matrix, ...
Markov chains are a versatile and widely used means to model an extensive variety of stochastic phen...
In the field of model checking, abstraction refinement has proved to be an extremely successful meth...
International audienceWe present a formal proof system for compositional verifica- tion of probabili...
Interactive Markov chains (IMC) are compositional behavioral models extending both labeled transitio...
Abstract. Interactive Markov chains (IMC) are compositional behavioural models extending labelled tr...
Interactive Markov chains (IMCs) constitute a powerful sto- chastic model that extends both continuo...
Abstract. Interactive Markov chains (IMCs) constitute a powerful sto-chastic model that extends both...
This paper reviews the model of interactive Markov chains (IMCs, for short), an extension of labelle...
Hermanns has introduced interactive Markov chains (IMCs) which arise as an orthogonal extension of l...
We propose to exploit three-valued abstraction to stochastic systems in a compositional way. This co...
Abstract This paper presents new algorithms and accompanying tool support for analyzing interactive ...
AbstractThe usage of process algebras for the performance modeling and evaluation of concurrent syst...
AbstractInterval Markov Chains (IMC), or Markov Chains with probability intervals in the transition ...
We consider Markov decision processes (MDPs) which are a standard model for probabilistic systems. W...
Interval Markov Chains (IMC), or Markov Chains with probability intervals in the transition matrix, ...
Markov chains are a versatile and widely used means to model an extensive variety of stochastic phen...
In the field of model checking, abstraction refinement has proved to be an extremely successful meth...
International audienceWe present a formal proof system for compositional verifica- tion of probabili...