9 citations · 13 across the 6 of their papers we have counts for
7 papers
Confidence Sequences for Online Statistical Model Checking of Markov Decision Processes
Konstantin Kueffner, Tobias Meggendorfer, Maximilian Weininger +1
Markov decision processes (MDPs) are a classic model of decision making under uncertainty, exhibiting both non-deterministic choice as well as probabilistic uncertainty. Traditiona…
Statistical Model Checking Beyond Means: Quantiles, CVaR, and the DKW Inequality (extended version)
Carlos E. Budde, Arnd Hartmanns, Tobias Meggendorfer +2
Statistical model checking (SMC) randomly samples probabilistic models to approximate quantities of interest with statistical error guarantees. It is traditionally used to estimate…
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 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…
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…
More for Less: Safe Policy Improvement With Stronger Performance Guarantees
Patrick Wienhöft, Marnix Suilen, Thiago D. Simão +3
In an offline reinforcement learning setting, the safe policy improvement (SPI) problem aims to improve the performance of a behavior policy according to which sample data has been…
Strategy Synthesis in Markov Decision Processes Under Limited Sampling Access
Christel Baier, Clemens Dubslaff, Patrick Wienhöft +1
A central task in control theory, artificial intelligence, and formal methods is to synthesize reward-maximizing strategies for agents that operate in partially unknown environment…