Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

6-2019

Abstract

In real-world systems, rare events often characterize critical situations like the probability that a system fails within some time bound and they are used to model some potentially harmful scenarios in dependability of safety-critical systems. Probabilistic Model Checking has been used to verify dependability properties in various types of systems but is limited by the state space explosion problem. An alternative is the recourse to Statistical Model Checking (SMC) that relies on Monte Carlo simulations and provides estimates within predefined error and confidence bounds. However, rare properties require a large number of simulations before occurring at least once. To tackle the problem, Importance Sampling, a rare event simulation technique, has been proposed in SMC for different types of probabilistic systems. Importance Sampling requires the full knowledge of probabilistic measure of the system, e.g. Markov chains. In practice, however, we often have models with some uncertainty, e.g., Interval Markov Chains. In this work, we propose a method to apply importance sampling to Interval Markov Chains. We show promising results in applying our method to multiple case studies

Keywords

Rare Events, Importance Sampling, Markov Chains, Interval Markov Chains, Dependability, Statistical ModelChecking

Discipline

Software Engineering

Research Areas

Software and Cyber-Physical Systems

Publication

Proceedings of the 48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks, Luxembourg, 2018 June 25-28

First Page

303

Last Page

313

Identifier

10.1109/DSN.2018.00040

Publisher

IEEE

City or Country

Luxembourg City, Luxembourg

Additional URL

https://doi.org/10.1109/DSN.2018.00040

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