1 citations · 2 across the 8 of their papers we have counts for
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Fast Computation of Conditional Probabilities in MDPs and Markov Chain Families
Milan Češka, 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…
Constrained and Robust Policy Synthesis with Satisfiability-Modulo-Probabilistic-Model-Checking
Linus Heck, Filip Macák, Milan Češka +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…
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…