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Probabilistic Causes in Markov Chains

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Automated Technology for Verification and Analysis (ATVA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 12971))

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 consump-tion of resources from different perspectives, and study the complexity of computing cost-minimal causes.

This work was funded by DFG grant 389792660 as part of TRR 248, the Cluster of Excellence EXC 2050/1 (CeTI, project ID 390696704, as part of Germany’s Excellence Strategy), DFG-projects BA-1679/11-1 and BA-1679/12-1, and the Research Training Group QuantLA (GRK 1763).

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Authors and Affiliations

  1. Technische Universität Dresden, Dresden, Germany

    Christel Baier, Florian Funke, Simon Jantsch, Jakob Piribauer & Robin Ziemek

Authors
  1. Christel Baier
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  2. Florian Funke
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  3. Simon Jantsch
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  4. Jakob Piribauer
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  5. Robin Ziemek
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Correspondence to Robin Ziemek .

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Editors and Affiliations

  1. Griffith University, Brisbane, QLD, Australia

    Zhe Hou

  2. University of Waterloo, Waterloo, ON, Canada

    Vijay Ganesh

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Baier, C., Funke, F., Jantsch, S., Piribauer, J., Ziemek, R. (2021). Probabilistic Causes in Markov Chains. In: Hou, Z., Ganesh, V. (eds) Automated Technology for Verification and Analysis. ATVA 2021. Lecture Notes in Computer Science(), vol 12971. Springer, Cham. https://doi.org/10.1007/978-3-030-88885-5_14

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  • DOI: https://doi.org/10.1007/978-3-030-88885-5_14

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