Temporal Properties of Conditional Independence in Dynamic Bayesian Networks

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Beitragende

Abstract

Dynamic Bayesian networks (DBNs) are compact graphical representations used to model probabilistic systems where interdependent random variables and their distributions evolve over time. In this paper, we study the verification of the evolution of conditional-independence (CI) propositions against temporal logic specifications. To this end, we consider two specification formalisms over CI propositions: linear temporal logic (LTL), and non-deterministic Büchi automata (NBAs). This problem has two variants. Stochastic CI properties take the given concrete probability distributions into account, while structural CI properties are viewed purely in terms of the graphical structure of the DBN. We show that deciding if a stochastic CI proposition eventually holds is at least as hard as the Skolem problem for linear recurrence sequences, a long-standing open problem in number theory. On the other hand, we show that verifying the evolution of structural CI propositions against LTL and NBA specifications is in PSPACE, and is NP-and coNP-hard. We also identify natural restrictions on the graphical structure of DBNs that make the verification of structural CI properties tractable.

Details

OriginalspracheEnglisch
TitelProceedings of the 40th Annual AAAI Conference on Artificial Intelligence
Redakteure/-innenSven Koenig, Chad Jenkins, Matthew E. Taylor
Herausgeber (Verlag)Association for the Advancement of Artificial Intelligence
Seiten36601-36609
Seitenumfang9
ISBN (elektronisch)978-1-57735-906-7
PublikationsstatusVeröffentlicht - März 2026
Peer-Review-StatusJa

Publikationsreihe

ReiheProceedings of the AAAI Conference on Artificial Intelligence
Nummer43
Band40
ISSN2159-5399

Konferenz

Titel40th AAAI Conference on Artificial Intelligence
KurztitelAAAI 2026
Veranstaltungsnummer40
Dauer20 - 27 Januar 2026
Webseite
OrtSingapore EXPO
StadtSingapore
LandSingapur

Externe IDs

ORCID /0000-0002-5321-9343/work/228232892
ORCID /0000-0003-4829-0476/work/228235095

Schlagworte

ASJC Scopus Sachgebiete