Abstract
Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for tractable inference. Universes may not always be known, though, or may only described by assumptions such as “small universes are more likely”. Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.
| Original language | English |
|---|---|
| Title of host publication | AI 2019: AI 2019: Advances in Artificial Intelligence |
| Editors | Jixue Liu, James Bailey |
| Number of pages | 13 |
| Volume | 11919 LNAI |
| Publisher | Springer, Cham |
| Publication date | 25.11.2019 |
| Pages | 91-103 |
| ISBN (Print) | 978-3-030-35287-5 |
| ISBN (Electronic) | 978-3-030-35288-2 |
| DOIs | |
| Publication status | Published - 25.11.2019 |
| Event | 32nd Australasian Joint Conference on Artificial Intelligence - Adelaide, Australia Duration: 02.12.2019 → 05.12.2019 Conference number: 234489 |
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
