3 papers
cs.LO2025
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…
cs.LO2025
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…
cs.LO2024
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…