8 papers
UMB: A Unified Markov Binary Format for Probabilistic Model Checking (extended version)
Roman Andriushchenko, Arnd Hartmanns, Joshua Jeppson +5
This paper presents the unified Markov binary (UMB) format, an efficient, extensible, and well-supported explicit-state file format for representing a wide range of probabilistic s…
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…
Missingness-MDPs: Bridging the Theory of Missing Data and POMDPs
Joshua Wendland, Markel Zubia, Roman Andriushchenko +6
We introduce missingness-MDPs (miss-MDPs), a novel subclass of partially observable Markov decision processes (POMDPs) that incorporates the theory of missing data. A miss-MDP is a…
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…
Small Decision Trees for MDPs with Deductive Synthesis
Roman Andriushchenko, Milan ÄeÅ¡ka, Sebastian Junges +1
Markov decision processes (MDPs) describe sequential decision-making processes; MDP policies return for every state in that process an advised action. Classical algorithms can effi…
Decentralized Planning Using Probabilistic Hyperproperties
Francesco Pontiggia, Filip Macák, Roman Andriushchenko +2
Multi-agent planning under stochastic dynamics is usually formalised using decentralized (partially observable) Markov decision processes ( MDPs) and reachability or expected rewar…