Probabilistic Causes in Markov Chains

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in Buch/Sammelband/GutachtenBeigetragenBegutachtung

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.

Details

OriginalspracheEnglisch
TitelAutomated Technology for Verification and Analysis
Redakteure/-innenZhe Hou, Vijay Ganesh
Herausgeber (Verlag)Springer, Berlin [u. a.]
Seiten205–221
Seitenumfang17
ISBN (Print)978-3-030-88884-8
PublikationsstatusVeröffentlicht - 2021
Peer-Review-StatusJa

Publikationsreihe

ReiheLecture Notes in Computer Science, Volume 12971
ISSN0302-9743

Konferenz

Titel19th International Symposium on Automated Technology for Verification and Analysis
KurztitelATVA 2021
Veranstaltungsnummer19
Dauer18 - 22 Oktober 2021
Webseite
BekanntheitsgradInternationale Veranstaltung
Ortonline
StadtGold Coast
LandAustralien

Externe IDs

Scopus 85118163265
ORCID /0000-0002-5321-9343/work/142236772
ORCID /0000-0002-8490-1433/work/142246190
ORCID /0000-0003-4829-0476/work/165453937

Schlagworte

Bibliotheksschlagworte