Abstract
The lifted dynamic junction tree algorithm (LDJT) efficiently answers 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 LDJT to (i) solve the smoothing inference problem to answer hindsight queries by introducing an efficient backward pass and (ii) discuss different options to instantiate a first-order cluster representation during a backward pass. Further, our relational forward backward algorithm makes hindsight queries to the very beginning feasible. LDJT answers multiple temporal queries faster than the static lifted junction tree algorithm on an unrolled model, which performs smoothing during message passing.
Original language | English |
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Number of pages | 8 |
Publication status | Published - 02.07.2018 |
Event | 27th International Joint Conference on Artificial Intelligence - Stockholm, Sweden Duration: 13.07.2018 → 19.07.2018 Conference number: 140653 |
Conference
Conference | 27th International Joint Conference on Artificial Intelligence |
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Abbreviated title | IJCAI 2018 |
Country/Territory | Sweden |
City | Stockholm |
Period | 13.07.18 → 19.07.18 |
DFG Research Classification Scheme
- 4.43-01 Theoretical Computer Science