Abstract
We look at probabilistic first-order formalisms where the domain objects are known. In these formalisms, the standard approach for inference is lifted variable elimination. To benefit from the advantages of the junction tree algorithm for inference in the first-order setting, we transfer the idea of lifting to the junction tree algorithm.
Our lifted junction tree algorithm aims at reducing computations by introducing first-order junction trees that compactly represent symmetries. First experiments show that we speed up the computation time compared to the propositional version. When querying for multiple marginals, the lifted junction tree algorithm performs better than using lifted VE to infer each marginal individually.
Our lifted junction tree algorithm aims at reducing computations by introducing first-order junction trees that compactly represent symmetries. First experiments show that we speed up the computation time compared to the propositional version. When querying for multiple marginals, the lifted junction tree algorithm performs better than using lifted VE to infer each marginal individually.
| Originalsprache | Englisch |
|---|---|
| Titel | KI 2016: Advances in Artificial Intelligence |
| Redakteure/-innen | Gerhard Friedrich, Malte Helmert, Franz Wotawa |
| Seitenumfang | 13 |
| Band | 9904 |
| Erscheinungsort | Cham |
| Herausgeber (Verlag) | Springer International Publishing |
| Erscheinungsdatum | 08.09.2016 |
| Seiten | 30-42 |
| ISBN (Print) | 978-3-319-46072-7 |
| ISBN (elektronisch) | 978-3-319-46073-4 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 08.09.2016 |
| Veranstaltung | 39th German Conference on Artificial Intelligence - Klagenfurt, Österreich Dauer: 26.09.2016 → 30.09.2016 Konferenznummer: 181639 |
DFG-Fachsystematik
- 4.43-01 Theoretische Informatik
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
