On probability-raising causality in Markov decision processes
From International Center for Computational Logic
On probability-raising causality in Markov decision processes
Christel BaierChristel Baier, Florian FunkeFlorian Funke, Jakob PiribauerJakob Piribauer, Robin ZiemekRobin Ziemek
Christel Baier, Florian Funke, Jakob Piribauer, Robin Ziemek
On probability-raising causality in Markov decision processes
In Bouyer, Patricia and Schröder, Lutz, eds., Foundations of Software Science and Computation Structures, 40--60, 2022. Springer International Publishing
On probability-raising causality in Markov decision processes
In Bouyer, Patricia and Schröder, Lutz, eds., Foundations of Software Science and Computation Structures, 40--60, 2022. Springer International Publishing
- KurzfassungAbstract
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. The latter includes algorithms for checking causeeffect relationships and the existence of probability-raising causes for given effect scenarios. Inspired by concepts of statistical analysis, we study quality measures (recall, coverage ratio and f-score) for causes and develop algorithms for their computation. Finally, the computational complexity for finding optimal causes with respect to these measures is analyzed. - Forschungsgruppe:Research Group: Algebraische und logische Grundlagen der InformatikAlgebraic and Logical Foundations of Computer Science
@inproceedings{BFPZ2022,
author = {Christel Baier and Florian Funke and Jakob Piribauer and Robin
Ziemek},
title = {On probability-raising causality in Markov decision processes},
editor = {Bouyer and Patricia and Schr{\"{o}}der and Lutz},
booktitle = {Foundations of Software Science and Computation Structures},
publisher = {Springer International Publishing},
year = {2022},
pages = {40--60},
doi = {10.1007/978-3-030-99253-8_3}
}
