1 citations · 1 across the 2 of their papers we have counts for
10 papers
Fast Computation of Conditional Probabilities in MDPs and Markov Chain Families
Milan ÄeÅ¡ka, Sebastian Junges, Luko van der Maas +2
Computing optimal conditional reachability probabilities in Markov decision processes (MDPs) is tractable by a reduction to reachability probabilities. Yet, this reduction yields c…
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…
Learning Verified Monitors for Hidden Markov Models
Luko van der Maas, Sebastian Junges
Runtime monitors assess whether a system is in an unsafe state based on a stream of observations. We study the problem where the system is subject to probabilistic uncertainty and…
Robust Finite-Memory Policy Gradients for Hidden-Model POMDPs
Maris F. L. Galesloot, Roman Andriushchenko, Milan ÄeÅ¡ka +2
Partially observable Markov decision processes (POMDPs) model specific environments in sequential decision-making under uncertainty. Critically, optimal policies for POMDPs may not…
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…
Policy Verification in Stochastic Dynamical Systems Using Logarithmic Neural Certificates
Thom Badings, Wietze Koops, Sebastian Junges +1
We consider the verification of neural network policies for discrete-time stochastic systems with respect to reach-avoid specifications. We use a learner-verifier procedure that le…