Probabilistic causes in Markov Chains

Publikation: Beitrag in FachzeitschriftForschungsartikelBeigetragenBegutachtung

Abstract

By combining two of the central paradigms of causality, namely counterfactual reasoning and probability-raising, we introduce a probabilistic notion of cause in Markov chains. Such a cause consists of finite executions of the probabilistic system after which the probability of an ω-regular effect exceeds a given threshold. The cause, as a set of executions, then has to cover all behaviors exhibiting the effect. With these properties, such causes can be used for monitoring purposes where the aim is to detect faulty behavior before it actually occurs. In order to choose which cause should be computed, we introduce multiple types of costs to capture the consumption of resources by the system or monitor from different perspectives, and study the complexity of computing cost-minimal causes.

Details

OriginalspracheEnglisch
Seiten (von - bis)347-367
Seitenumfang21
FachzeitschriftInnovations in Systems and Software Engineering
Jahrgang18
Ausgabenummer3
PublikationsstatusVeröffentlicht - 25 Apr. 2022
Peer-Review-StatusJa

Externe IDs

Scopus 85128827096
unpaywall 10.1007/s11334-022-00452-8
dblp journals/isse/ZiemekPFJB22
WOSLite 000787142300001
Mendeley dc884727-a7c0-39bc-991f-344fa54713df

Schlagworte

Forschungsprofillinien der TU Dresden

    DFG-Fachsystematik nach Fachkollegium

    Fächergruppen, Lehr- und Forschungsbereiche, Fachgebiete nach Destatis

    ASJC Scopus Sachgebiete

    Schlagwörter

    • Causality, Expected costs, Markov chain, Model checking