51 citations · 233 across the 33 of their papers we have counts for
7 papers · 1 filter
Solving Robust Markov Decision Processes: Generic, Reliable, Efficient
Tobias Meggendorfer, Maximilian Weininger, Patrick Wienhöft
Markov decision processes (MDP) are a well-established model for sequential decision-making in the presence of probabilities. In robust MDP (RMDP), every action is associated with…
Sound Value Iteration for Simple Stochastic Games
Muqsit Azeem, Jan Kretinsky, Maximilian Weininger
Algorithmic analysis of Markov decision processes (MDP) and stochastic games (SG) in practice relies on value-iteration (VI) algorithms. Since the basic version of VI does not prov…
Sound Statistical Model Checking for Probabilities and Expected Rewards (extended version)
Carlos E. Budde, Arnd Hartmanns, Tobias Meggendorfer +2
Statistical model checking estimates probabilities and expectations of interest in probabilistic system models by using random simulations. Its results come with statistical guaran…
1-2-3-Go! Policy Synthesis for Parameterized Markov Decision Processes via Decision-Tree Learning and Generalization
Muqsit Azeem, Debraj Chakraborty, Sudeep Kanav +4
Despite the advances in probabilistic model checking, the scalability of the verification methods remains limited. In particular, the state space often becomes extremely large when…
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…
Playing Games with your PET: Extending the Partial Exploration Tool to Stochastic Games
Tobias Meggendorfer, Maximilian Weininger
We present version 2.0 of the Partial Exploration Tool (PET), a tool for verification of probabilistic systems. We extend the previous version by adding support for stochastic game…
What Are the Odds? Improving the foundations of Statistical Model Checking
Tobias Meggendorfer, Maximilian Weininger, Patrick Wienhöft
Markov decision processes (MDPs) are a fundamental model for decision making under uncertainty. They exhibit non-deterministic choice as well as probabilistic uncertainty. Traditio…