Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm
This work addresses efficiency issues in probabilistic inference for temporal models, but it is incremental as it builds on an existing algorithm.
The paper tackled the problem of unnecessary groundings in the lifted dynamic junction tree algorithm (LDJT) for probabilistic relational temporal models, resulting in an extended version that answers multiple temporal queries orders of magnitude faster than the original.
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.