The Probabilistic Model Checker Storm

02/17/2020

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We present the probabilistic model checker Storm. Storm supports the analysis of discrete- and continuous-time variants of both Markov chains and Markov decision processes. Storm has three major distinguishing features. It supports multiple input languages for Markov models, including the JANI and PRISM modeling languages, dynamic fault trees, generalized stochastic Petri nets, and the probabilistic guarded command language. It has a modular set-up in which solvers and symbolic engines can easily be exchanged. Its Python API allows for rapid prototyping by encapsulating Storm's fast and scalable algorithms. This paper reports on the main features of Storm and explains how to effectively use them. A description is provided of the main distinguishing functionalities of Storm. Finally, an empirical evaluation of different configurations of Storm on the QComp 2019 benchmark set is presented.

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The Probabilistic Model Checker Storm

Reduction Methods on Probabilistic Control-flow Programs for Reliability Analysis

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Taming denumerable Markov decision processes with decisiveness

Probabilistic Hyperproperties of Markov Decision Processes

Markov Chains on Orbits of Permutation Groups

Continuous-Time Markov Decisions based on Partial Exploration

The Probabilistic Model Checker Storm

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Reduction Methods on Probabilistic Control-flow Programs for Reliability Analysis

Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs

Taming denumerable Markov decision processes with decisiveness

Probabilistic Hyperproperties of Markov Decision Processes

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