Probabilistic Causes in Markov Chains
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Beitragende
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
Originalsprache | Englisch |
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Titel | Automated Technology for Verification and Analysis |
Redakteure/-innen | Zhe Hou, Vijay Ganesh |
Herausgeber (Verlag) | Springer, Berlin [u. a.] |
Seiten | 205–221 |
Seitenumfang | 17 |
ISBN (Print) | 978-3-030-88884-8 |
Publikationsstatus | Veröffentlicht - 2021 |
Peer-Review-Status | Ja |
Publikationsreihe
Reihe | Lecture Notes in Computer Science, Volume 12971 |
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ISSN | 0302-9743 |
Konferenz
Titel | 19th International Symposium on Automated Technology for Verification and Analysis |
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Kurztitel | ATVA 2021 |
Veranstaltungsnummer | 19 |
Dauer | 18 - 22 Oktober 2021 |
Webseite | |
Bekanntheitsgrad | Internationale Veranstaltung |
Ort | online |
Stadt | Gold Coast |
Land | Australien |
Externe IDs
Scopus | 85118163265 |
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ORCID | /0000-0002-5321-9343/work/142236772 |
ORCID | /0000-0002-8490-1433/work/142246190 |
ORCID | /0000-0003-4829-0476/work/165453937 |