Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree
  Algorithm
  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.
