Rewrite-Based Statistical Model Checking of WMTL

Int J Softw Tools Technol Transfer

Legay

et al. 2015

Self Cite

426

297

“…For specifying properties over NSTAs, we use a weighted extension of the temporal logic MITL [2] expressing properties over runs [13], defined by the grammar:…”

Section: Query Languagementioning

confidence: 99%

Uppaal SMC tutorial

David

Int J Softw Tools Technol Transfer

Legay

et al. 2015

Self Cite

426

297

“…If F = G, the likelihood ratio of realisable paths is uniformly equal to 1, (4) reduces to (1) and (6) reduces to (3). To ensure that the variance of (5) is less than the variance of (2) it is necessary to make σ 2 is < σ 2 crude , for which it is sufficient to make dF/dG < 1, ∀ω ∈ Ω. Lemma 1.…”

Section: Variance Reductionmentioning

confidence: 99%

“…A series of works [7,5,3,4] has developed methods for analysing Stochastic Timed Automata using Statistical Model Checking (SMC) [18]. SMC includes a collection of Monte Carlo techniques that use simulation to avoid "state space explosion" and other intractabilities encountered by model checking.…”

Section: Introductionmentioning

confidence: 99%

Importance Sampling for Stochastic Timed Automata

Jégourel

Dependable Software Engineering: Theories, Tools, and Applications

Legay

et al. 2016

Self Cite

Abstract. We present an importance sampling framework that combines symbolic analysis and simulation to estimate the probability of rare reachability properties in stochastic timed automata. By means of symbolic exploration, our framework first identifies states that cannot reach the goal. A state-wise change of measure is then applied on-thefly during simulations, ensuring that dead ends are never reached. The change of measure is guaranteed by construction to reduce the variance of the estimator with respect to crude Monte Carlo, while experimental results demonstrate that we can achieve substantial computational gains.

“…Our Uppaal SMC toolset implements a wide range of SMC algorithms for answering qualitative and quantitative questions on networks of SHAs. The tool supports not only classical reachability and safety properties, but also general weighted MTL properties [6,4]. One can exploit the quantitative engine of SMC to do parameter sweep in order to optimize some quantity.…”

Section: Statistical Model Checkingmentioning

confidence: 99%

Optimizing Control Strategy Using Statistical Model Checking

David

Lecture Notes in Computer Science

et al. 2013

Self Cite

This paper proposes a new efficient approach to optimize energy consumption for energy aware buildings. Our approach relies on stochastic hybrid automata for representing energy aware systems. The model is parameterized by several cost values that need to be optimized in order to minimize energy consumption. Our approach exploits a stochastic semantic together with simulation in order to estimate the best value for such parameters. Contrary to existing techniques that would estimate energy consumption for each value of the parameters, our approach relies on a new statistical engine that exploits ANOVA, a technique that can reduce the number of runs needed by the comparison algorithm to perform the estimates. Our approach has been implemented and our experiments show that we clearly outperform the naive approach.