4 citations · 5 across the 5 of their papers we have counts for
5 papers
Formal Quality Measures for Predictors in Markov Decision Processes
Christel Baier, Sascha Klüppelholz, Jakob Piribauer +1
In adaptive systems, predictors are used to anticipate changes in the systems state or behavior that may require system adaption, e.g., changing its configuration or adjusting reso…
Foundations of probability-raising causality in Markov decision processes
Christel Baier, Jakob Piribauer, Robin Ziemek
This work introduces a novel cause-effect relation in Markov decision processes using the probability-raising principle. Initially, sets of states as causes and effects are conside…
On probability-raising causality in Markov decision processes
Christel Baier, Florian Funke, Jakob Piribauer +1
The purpose of this paper is to introduce a notion of causality in Markov decision processes based on the probability-raising principle and to analyze its algorithmic properties. T…
From Verification to Causality-based Explications
Christel Baier, Clemens Dubslaff, Florian Funke +4
In view of the growing complexity of modern software architectures, formal models are increasingly used to understand why a system works the way it does, opposed to simply verifyin…
Probabilistic causes in Markov chains
Christel Baier, Florian Funke, Simon Jantsch +2
The paper studies a probabilistic notion of causes in Markov chains that relies on the counterfactuality principle and the probability-raising property. This notion is motivated by…