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. Unfortunately, a non-ideal elimination order can lead to groundings even though a lifted run is possible for a model. We extend LDJT (i) to identify unnecessary groundings while proceeding in time and (ii) to prevent groundings by delaying eliminations through changes in a temporal first-order cluster representation. The extended version of LDJT answers multiple temporal queries orders of magnitude faster than the original version.
@article{arxiv.1807.00744,
title = {Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm},
author = {Marcel Gehrke and Tanya Braun and Ralf Möller},
journal= {arXiv preprint arXiv:1807.00744},
year = {2018}
}
Comments
Accepted at the Eighth International Workshop on Statistical Relational AI