From the 1 of 18 linked papers with an AI index.
1 citations · 1 across the 7 of their papers we have counts for
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Property-driven Causal Abstractions for Markov Decision Processes
Jule Schmidt, Maximilian Weininger, Clemens Dubslaff +2
The paper proposes a property-driven causal abstraction method for factored Markov Decision Processes that groups states sharing the same causal reasons for satisfying or violating…
dtControl2+: Trading Optimality for Explainability in MDPs via Decision Trees
Tereza Kinská, Jan KÅetÃnský, 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…
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…
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…
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…