Probabilistic Model Checking Taken by Storm

arXiv (Cornell University)

Abstract

This tutorial paper presents a hands-on perspective on probabilistic model checking with the Storm model checker. Storm is a decade-old model checker that excels in performance and a rich Python-based ecosystem, which makes it easy to integrate in various workflows. This tutorial focuses on Markov decision processes (MDP), which are popular in a variety of fields. It demonstrates the basic workflow, from Python-based modeling, model checking with a variety of properties, to the extraction of policies. Further, it showcases the support for recent topics that focus on different types of uncertainty, such as interval MDP and POMDP, and the ability to quickly implement simple algorithms on top of existing data structures.

Authors 5

1. Matthias Volk

   Eindhoven University of Technology

   Affiliation as printed

   Eindhoven University of Technology , Eindhoven , The Netherlands

2. Linus Heck

   Radboud University Nijmegen

   Affiliation as printed

   Radboud University , Nijmegen , The Netherlands

3. Sebastian Junges

   Radboud University Nijmegen

   Affiliation as printed

   Radboud University , Nijmegen , The Netherlands

4. Joost-Pieter Katoen Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany

5. Tim Quatmann Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University , Aachen , Germany