Abstract
The lifted dynamic junction tree algorithm (LDJT) efficiently answers exact filtering and prediction queries for probabilistic relational temporal models by building and then reusing a first-order cluster representation of a knowledge base for multiple queries and time steps. We extend the underling model of LDJT to provide means to calculate a lifted temporal solution to the maximum expected utility problem. © CEUR-WS. All rights reserved.
Originalsprache | Englisch |
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Titel | 1st Joint Workshop on AI in Health |
Seitenumfang | 4 |
Band | 2142 |
Herausgeber (Verlag) | CEUR-WS.org |
Erscheinungsdatum | 01.07.2018 |
Seiten | 93-96 |
Publikationsstatus | Veröffentlicht - 01.07.2018 |
Veranstaltung | 1st Joint Workshop on AI in Health - Stockholm, Schweden Dauer: 13.07.2018 → 14.07.2018 Konferenznummer: 138105 |
DFG-Fachsystematik
- 4.43-01 Theoretische Informatik