Probabilistic Causes in Markov Chains

Christel BaierChristel Baier, Florian FunkeFlorian Funke, Simon JantschSimon Jantsch, Jakob PiribauerJakob Piribauer, Robin ZiemekRobin Ziemek

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Christel Baier, Florian Funke, Simon Jantsch, Jakob Piribauer, Robin Ziemek
Probabilistic Causes in Markov Chains
In Hou, Zhe and Ganesh, Vijay, eds., Automated Technology for Verification and Analysis, Lecture Notes in Computer Science, 205--221, 2021. Springer International Publishing

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KurzfassungAbstract
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 consump-tion of resources from different perspectives, and study the complexity of computing cost-minimal causes.
Forschungsgruppe:Research Group: Algebraische und logische Grundlagen der InformatikAlgebraic and Logical Foundations of Computer Science

@inproceedings{BFJPZ2021,
  author    = {Christel Baier and Florian Funke and Simon Jantsch and Jakob
               Piribauer and Robin Ziemek},
  title     = {Probabilistic Causes in Markov Chains},
  editor    = {Hou and Zhe and Ganesh and Vijay},
  booktitle = {Automated Technology for Verification and Analysis},
  series    = {Lecture Notes in Computer Science},
  publisher = {Springer International Publishing},
  year      = {2021},
  pages     = {205--221},
  doi       = {10.1007/978-3-030-88885-5_14}
}