Probabilistic causes in Markov chains
Logic in Computer Science
2021-07-09 v2
Abstract
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 the use of causes for monitoring purposes where the aim is to detect faulty or undesired behaviours before they actually occur. A cause is a set of finite executions of the system after which the probability of the effect exceeds a given threshold. We introduce multiple types of costs that capture the consumption of resources from different perspectives, and study the complexity of computing cost-minimal causes.
Cite
@article{arxiv.2104.13604,
title = {Probabilistic causes in Markov chains},
author = {Christel Baier and Florian Funke and Simon Jantsch and Jakob Piribauer and Robin Ziemek},
journal= {arXiv preprint arXiv:2104.13604},
year = {2021}
}
Comments
Full version of a conference paper at ATVA'21; 26 pages, 9 figures