51 citations · 233 across the 33 of their papers we have counts for
6 papers · 1 filter
Property-driven Causal Abstractions for Markov Decision Processes
Jule Schmidt, Maximilian Weininger, Clemens Dubslaff +2
Markov Decision Processes (MDPs) are widely used as decision-making models, commonly specified over factored state spaces through state variables and their valuations. The exponent…
dtControl2+: Trading Optimality for Explainability in MDPs via Decision Trees
Tereza Kinská, Jan Křetínský, Tobias Meggendorfer +2
Over the past decade, decision trees have been used to represent controllers (a.k.a. policies) in an explainable way, with dtControl2 as a current state-of-the-art tool. However, f…
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…
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…
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…
Perception-Based Beliefs for POMDPs with Visual Observations
Miriam Schäfers, Merlijn Krale, Thiago D. Simão +2
Partially observable Markov decision processes (POMDPs) are a principled planning model for sequential decision-making under uncertainty. Yet, real-world problems with high-dimensi…