Partial order reduction for probabilistic systems

Baier, Christel; Größer, Marcus; Ciesinski, Frank

doi:10.1109/qest.2004.1348037

“…In the context of partial order reduction for MDPs, it has been noticed in [4,14] that the criteria that are known to be sound for non-probabilistic systems are not sufficient to preserve extremal probabilities for ω-regular path events. In this setting, the problem is that the commutativity of independent probabilistic actions is a local property that does not carry over to a global setting.…”

Section: Partially-observable Markov Decision Processesmentioning

confidence: 99%

On Model Checking Techniques for Randomized Distributed Systems

Baier

¹

2010

Lecture Notes in Computer Science

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“…Confluence reduction for process algebras was first introduced for non-probabilistic systems [7], and later for probabilistic automata [25]. Also, several types of partial order reduction (POR) have been defined, both for nonprobabilistic [26,21,16] and probabilistic systems [11,4,3]. These techniques are based on ideas similar to confluence, and have been compared to confluence recently, both in a theoretical [17] and in a practical manner [18].…”

Section: Introductionmentioning

confidence: 99%

Confluence reduction for Markov automata

Timmer

¹

,

Katoen

²

,

Pol

³

et al. 2016

Theoretical Computer Science

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Abstract. Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude.

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“…Confluence reduction for process algebras was first introduced for non-probabilistic systems [7], and later for probabilistic automata [25]. Also, several types of partial order reduction (POR) have been defined, both for nonprobabilistic [26,21,16] and probabilistic systems [11,4,3]. These techniques are based on ideas similar to confluence, and have been compared to confluence recently, both in a theoretical [17] and in a practical manner [18].…”

Section: Introductionmentioning

confidence: 99%

Confluence Reduction for Markov Automata

Timmer

¹

,

Pol

²

,

Stoelinga

³

2013

Lecture Notes in Computer Science

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Abstract. Markov automata are a novel formalism for specifying systems exhibiting nondeterminism, probabilistic choices and Markovian rates. Recently, the process algebra MAPA was introduced to efficiently model such systems. As always, the state space explosion threatens the analysability of the models generated by such specifications. We therefore introduce confluence reduction for Markov automata, a powerful reduction technique to keep these models small. We define the notion of confluence directly on Markov automata, and discuss how to syntactically detect confluence on the MAPA language as well. That way, Markov automata generated by MAPA specifications can be reduced on-the-fly while preserving divergence-sensitive branching bisimulation. Three case studies demonstrate the significance of our approach, with reductions in analysis time up to an order of magnitude.

show abstract

Partial order reduction for probabilistic systems

Cited by 41 publications

References 21 publications

On Model Checking Techniques for Randomized Distributed Systems

On Model Checking Techniques for Randomized Distributed Systems

Confluence reduction for Markov automata

Confluence Reduction for Markov Automata

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