DOI: 10.3990/1.9789036505925
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Efficient modelling, generation and analysis of Markov Automata

Abstract: Quantitative model checking is concerned with the verification of both quantitative and qualitative properties over models incorporating quantitative information. Increases in expressivity of the models involved allow more types of systems to be analysed, but also raise the difficulty of their efficient analysis.Three years ago, the Markov automaton (MA) was introduced as a generalisation of probabilistic automata and interactive Markov chains, supporting nondeterminism, discrete probabilistic choice as well a… Show more

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Cited by 10 publications
(13 citation statements)
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“…The Markov Automata Process Algebra (MAPA) language allows MAs to be generated in an efficient and effective manner [31]. It is based on µCRL [16], allowing the standard process-algebraic constructs such as nondeterministic choice and action prefix to be used in a data-rich context: processes are equipped with a set of variables over user-definable data types, and actions can be parameterised based on the values of these variables.…”
Section: Mapa With Rewardsmentioning
confidence: 99%
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“…The Markov Automata Process Algebra (MAPA) language allows MAs to be generated in an efficient and effective manner [31]. It is based on µCRL [16], allowing the standard process-algebraic constructs such as nondeterministic choice and action prefix to be used in a data-rich context: processes are equipped with a set of variables over user-definable data types, and actions can be parameterised based on the values of these variables.…”
Section: Mapa With Rewardsmentioning
confidence: 99%
“…Action-based rewards are just added as decorations to the actions in the process-algebraic specification: we use a[r] • x:D f : p to denote an action a having reward r, continuing as process p (where the variable x gets a value from its domain D based on a probabilistic expression f ). We refer to [31] for a detailed exposition of the syntax and semantics of MAPA; this is trivially generalised to incorporate the action-based rewards.…”
Section: Mapa With Rewardsmentioning
confidence: 99%
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“…A probability space (Ω, F, P) is defined in a standard way [88], where Ω is the sample space, F is a set of events, and P is the unique measure on F [86]. The sample space Ω is a set of all possible outcomes of the probabilistic experiment.…”
Section: Probability Spacementioning
confidence: 99%