Abstract
The lifted dynamic junction tree algorithm (LDJT) answers filtering and prediction queries efficiently 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 LDJT to answer conjunctive queries over multiple time steps by avoiding eliminations, while keeping the complexity to answer a conjunctive query low. The extended version of saves computations compared to an existing approach to answer multiple lifted conjunctive queries.
| Original language | English |
|---|---|
| Title of host publication | AI 2018: Advances in Artificial Intelligence |
| Editors | Tanja Mitrovic, Bing Xue, Xiaodong Li |
| Number of pages | 13 |
| Volume | 11320 |
| Place of Publication | Cham |
| Publisher | Springer International Publishing |
| Publication date | 10.11.2018 |
| Pages | 543-555 |
| ISBN (Print) | 978-3-030-03990-5 |
| ISBN (Electronic) | 978-3-030-03991-2 |
| DOIs | |
| Publication status | Published - 10.11.2018 |
| Event | 31st Australasian Joint Conference on Artificial Intelligence - Wellington, Niger Duration: 11.12.2018 → 14.12.2018 https://ecs.victoria.ac.nz/Events/AI2018/WebHome#gallery |
Research Areas and Centers
- Centers: Center for Artificial Intelligence Luebeck (ZKIL)
- Research Area: Intelligent Systems
DFG Research Classification Scheme
- 4.43-01 Theoretical Computer Science
