Statistical Model Checking Using Perfect Simulation

Rabih, Diana El; Pekergin, Nihal

doi:10.1007/978-3-642-04761-9_11

Cited by 42 publications

(33 citation statements)

References 16 publications

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“…Several authors [8], [9], [10], [11] have studied Statistical Model Checking, which handles the PMC problem statistically in fully probabilistic systems. Several implementations [12], [13] have already shown the applicability of SMC.…”

Section: Related Workmentioning

confidence: 99%

Statistical Model Checking for Markov Decision Processes

Henriques

Martins

Zuliani

et al. 2012

2012 Ninth International Conference on Quantitative Evaluation of Systems

100

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Abstract-Statistical Model Checking (SMC) is a computationally very efficient verification technique based on selective system sampling. One well identified shortcoming of SMC is that, unlike probabilistic model checking, it cannot be applied to systems featuring nondeterminism, such as Markov Decision Processes (MDP). We address this limitation by developing an algorithm that resolves nondeterminism probabilistically, and then uses multiple rounds of sampling and Reinforcement Learning to provably improve resolutions of nondeterminism with respect to satisfying a Bounded Linear Temporal Logic (BLTL) property. Our algorithm thus reduces an MDP to a fully probabilistic Markov chain on which SMC may be applied to give an approximate solution to the problem of checking the probabilistic BLTL property. We integrate our algorithm in a parallelised modification of the PRISM simulation framework. Extensive validation with both new and PRISM benchmarks demonstrates that the approach scales very well in scenarios where symbolic algorithms fail to do so.

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Section: Related Workmentioning

confidence: 99%

Statistical Model Checking for Markov Decision Processes

Henriques

Martins

Zuliani

et al. 2012

2012 Ninth International Conference on Quantitative Evaluation of Systems

100

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“…Therefore, this approach is suitable only for medium-size PBNs and is implemented for the comprehensiveness of the tool. Unfortunately, since PBNs with perturbations are non-monotone systems, the very efficient monotone version of perfect simulation [13] in which only a small subset of the state space needs to be considered is of no use in this context.…”

Section: Architecture and Usagementioning

confidence: 99%

“…Existing statistical model checkers are either restricted for bounded properties or cannot directly deal with PBNs. The Skart method and the perfect simulation algorithm have been recently used for statistical model checking of steady state and unbounded until properties [13,16]. To the best of our knowledge, ASSA-PBN is the first tool to introduce those two methods into the context of PBNs.…”

Section: Comparison Evaluation and Future Developmentsmentioning

confidence: 99%

ASSA-PBN: An Approximate Steady-State Analyser of Probabilistic Boolean Networks

Mizera

Pang

Yuan

2015

Automated Technology for Verification and Analysis

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Abstract. We present ASSA-PBN, a tool for approximate steady-state analysis of large probabilistic Boolean networks (PBNs). ASSA-PBN contains a constructor, a simulator, and an analyser which can approximately compute the steadystate probabilities of PBNs. For large PBNs, such approximate analysis is the only viable way to study their long-run behaviours. Experiments show that ASSA-PBN can handle large PBNs with a few thousands of nodes.

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“…-modelling of PBNs in high-level ASSA-PBN format and converting a model from Matlab-PBN-toolbox format to ASSA-PBN format; -random generation of PBNs; -efficient simulation of a PBN; -computation of steady-state probabilities of a PBN with either numerical methods or statistical methods (the two-state Markov chain approach, the Skart method, and the perfect-simulation method) [5,6]; -parallel computation of steady-state probabilities of a PBN with either the twostate Markov chain approach or the Skart method; -parameter estimation of a PBN; -long-run influence and sensitivity analysis of a PBN; -a command-line tool and a GUI.…”

Section: Introductionmentioning

confidence: 99%

ASSA-PBN 2.0: A Software Tool for Probabilistic Boolean Networks

Mizera

Pang

Yuan

2016

Computational Methods in Systems Biology

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Abstract. We present a major new release of ASSA-PBN, a software tool for modelling, simulation, and analysis of probabilistic Boolean networks (PBNs). PBNs are a widely used computational framework for modelling biological systems. The steady-state dynamics of a PBN is of special interest and obtaining it poses a significant challenge due to the state space explosion problem which often arises in the case of large biological systems. In its previous version, ASSA-PBN applied efficient statistical methods to approximately compute steady-state probabilities of large PBNs. In this newly released version, ASSA-PBN not only speeds up the computation of steady-state probabilities with three different realisations of parallel computing, but also implements parameter estimation and techniques for in-depth analysis of PBNs, i.e., influence and sensitivity analysis of PBNs. In addition, a graphical user interface (GUI) is provided for the convenience of users.

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Statistical Model Checking Using Perfect Simulation

Cited by 42 publications

References 16 publications

Statistical Model Checking for Markov Decision Processes

Statistical Model Checking for Markov Decision Processes

ASSA-PBN: An Approximate Steady-State Analyser of Probabilistic Boolean Networks

ASSA-PBN 2.0: A Software Tool for Probabilistic Boolean Networks

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