2 citations · 3 across the 11 of their papers we have counts for
12 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 Češka +2
Partially observable Markov decision processes (POMDPs) model specific environments in sequential decision-making under uncertainty. Critically, optimal policies for POMDPs may not…
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…
Small Decision Trees for MDPs with Deductive Synthesis
Roman Andriushchenko, Milan Češka, 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…
Policies Grow on Trees: Model Checking Families of MDPs
Roman Andriushchenko, Milan Češka, Sebastian Junges +1
Markov decision processes (MDPs) provide a fundamental model for sequential decision making under process uncertainty. A classical synthesis task is to compute for a given MDP a wi…
Tools at the Frontiers of Quantitative Verification
Roman Andriushchenko, Alexander Bork, Carlos E. Budde +20
The analysis of formal models that include quantitative aspects such as timing or probabilistic choices is performed by quantitative verification tools. Broad and mature tool suppo…
Deductive Controller Synthesis for Probabilistic Hyperproperties
Roman Andriushchenko, Ezio Bartocci, Milan Ceska +2
Probabilistic hyperproperties specify quantitative relations between the probabilities of reaching different target sets of states from different initial sets of states. This class…
Search and Explore: Symbiotic Policy Synthesis in POMDPs
Roman Andriushchenko, Alexander Bork, Milan Češka +3
This paper marries two state-of-the-art controller synthesis methods for partially observable Markov decision processes (POMDPs), a prominent model in sequential decision making un…
Inductive Synthesis of Finite-State Controllers for POMDPs
Roman Andriushchenko, Milan Ceska, Sebastian Junges +1
We present a novel learning framework to obtain finite-state controllers (FSCs) for partially observable Markov decision processes and illustrate its applicability for indefinite-h…
Inductive Synthesis for Probabilistic Programs Reaches New Horizons
Roman Andriushchenko, Milan Ceska, Sebastian Junges +1
This paper presents a novel method for the automated synthesis of probabilistic programs. The starting point is a program sketch representing a finite family of finite-state Markov…