5 papers
Shields to Guarantee Probabilistic Safety in MDPs
Linus Heck, Filip Macák, Roman Andriushchenko +2
Shielding is a prominent model-based technique to ensure safety of autonomous agents. Classical shielding aims to ensure that nothing bad ever happens and comes with strong guarant…
Probabilistic Model Checking Taken by Storm
Matthias Volk, Linus Heck, Sebastian Junges +2
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…
Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking
Linus Heck, Filip Macák, Milan ÄeÅ¡ka +1
The ability to compute reward-optimal policies for given and known finite Markov decision processes (MDPs) underpins a variety of applications across planning, controller synthesis…
Generalized Parameter Lifting: Finer Abstractions for Parametric Markov Chains
Linus Heck, Tim Quatmann, Jip Spel +2
Parametric Markov chains (pMCs) are Markov chains (MCs) with symbolic probabilities. A pMC encodes a family of MCs, where each member is obtained by replacing parameters with const…
Improving the Noise Estimation of Latent Neural Stochastic Differential Equations
Linus Heck, Maximilian Gelbrecht, Michael T. Schaub +1
Latent neural stochastic differential equations (SDEs) have recently emerged as a promising approach for learning generative models from stochastic time series data. However, they…