Advances in Symbolic Probabilistic Model Checking with PRISM

Klein, Joachim; Baier, Christel; Chrszon, Philipp; Daum, Marcus; Dubslaff, Clemens; Klüppelholz, Sascha; Märcker, Steffen; Muller, David E.

doi:10.1007/978-3-662-49674-9_20

Cited by 19 publications

(23 citation statements)

References 31 publications

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“…The first one is a reformulation of the value iteration-based variant [14] of the algorithm introduced in [2]. We incorporate a simple fix for the problem that the error accumulation over the sequence of value iterations had not been accounted for and refer to the result as algorithm modvi.…”

Section: Reward-bounded Analysis Techniquesmentioning

confidence: 99%

“…, LP i−1 with r being the maximal reward that occurs in the MDP. Since LP does not scale to large MDP [7], the technique has been reconsidered using value iteration instead [14]. Using the transformations and assumptions introduced above, we can formulate it as in Algorithm 1.…”

Section: Sequential Value Iterationsmentioning

confidence: 99%

“…Initially, V contains the probabilities to reach a goal state with zero reward. In contrast to [14], when given an overall error bound , we use bound b+1 for the individual value iteration calls. At the cost of higher runtime, this counteracts the accumulation of error over multiple calls to yield an -close final result:…”

Section: Sequential Value Iterationsmentioning

confidence: 99%

“…While it solves the memory problem in principle, LP is known not to scale to large MDP in practice. Consequently, LP has been replaced by value iteration to achieve scalability in the most recent implementation [ 14]. Value iteration is an approximative numeric technique to compute reachability probabilities up to a predefined error bound .…”

Section: Introductionmentioning

confidence: 99%

“…When used in sequence, this error accumulates, and the final result for bound b may differ from the actual probability by more than . This has not been taken into account in [14].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques

Hahn

Hartmanns

2016

Dependable Software Engineering: Theories, Tools, and Applications

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In the design of probabilistic timed systems, requirements concerning behaviour that occurs within a given time or energy budget are of central importance. We observe that model-checking such requirements for probabilistic timed automata can be reduced to checking reward-bounded properties on Markov decision processes. This is traditionally implemented by unfolding the model according to the bound, or by solving a sequence of linear programs. Neither scales well to large models. Using value iteration in place of linear programming achieves scalability but accumulates approximation error. In this paper, we correct the value iteration-based scheme, present two new approaches based on scheduler enumeration and state elimination, and compare the practical performance and scalability of all techniques on a number of case studies from the literature. We show that state elimination can significantly reduce runtime for large models or high bounds.

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Section: Reward-bounded Analysis Techniquesmentioning

confidence: 99%

Section: Sequential Value Iterationsmentioning

confidence: 99%

Section: Sequential Value Iterationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…When used in sequence, this error accumulates, and the final result for bound b may differ from the actual probability by more than . This has not been taken into account in [14].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques

Hahn

Hartmanns

2016

Dependable Software Engineering: Theories, Tools, and Applications

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Probabilistic Verification for Reliable Network-on-Chip System Design

Lewis

Hartmanns

Basu

et al. 2019

Lecture Notes in Computer Science

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The design of modern network-on-chip (NoC) systems faces reliability challenges due to process and environmental variations. Peak power supply noise (PSN) in the power delivery network of a NoC device plays a critical role in determining reliable operations: PSN typically leads to voltage droop, which can cause timing errors in the NoC router pipelines. Existing simulation-based approaches cannot provide rigorous, worst-case reliability guarantees on the probabilistic behaviors of PSN. To address this problem, this paper takes a significant step in formally analyzing PSN in modern NoCs. Specifically, we present a probabilistic model checking approach for the rigorous characterization of PSN for a generic central router of a large mesh-NoC system, under the Round Robin scheduling mechanism with a uniform random network traffic load. Defining features for PSN are extracted at the behavioral level to facilitate property formulation. Several abstract models have been derived for the central router's concrete model based on the observations of its arbiter's conflict resolution behavior. Probabilistic modeling and verification are performed using the Modest Toolset. Results show significant scalability of our abstract models, and reveal key PSN characteristics that are indicative of NoC design and optimization.

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Bayesian Inference by Symbolic Model Checking

Salmani

Katoen

2020

Quantitative Evaluation of Systems

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This paper applies probabilistic model checking techniques for discrete Markov chains to inference in Bayesian networks. We present a simple translation from Bayesian networks into tree-like Markov chains such that inference can be reduced to computing reachability probabilities. Using a prototypical implementation on top of the Storm model checker, we show that symbolic data structures such as multi-terminal BDDs (MTBDDs) are very effective to perform inference on large Bayesian network benchmarks. We compare our result to inference using probabilistic sentential decision diagrams and vtrees, a scalable symbolic technique in AI inference tools.

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Advances in Symbolic Probabilistic Model Checking with PRISM

Cited by 19 publications

References 31 publications

A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques

A Comparison of Time- and Reward-Bounded Probabilistic Model Checking Techniques

Probabilistic Verification for Reliable Network-on-Chip System Design

Bayesian Inference by Symbolic Model Checking

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