activity
20182021
most citedTaming Reasoning in Temporal Probabilistic Relational Models

2 citations · 3 across the 3 of their papers we have counts for

collaborators

6 papers

cs.AI2021

Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress

Tanya Braun, Stefan Fischer, Florian Lau +1

DNA-based nanonetworks have a wide range of promising use cases, especially in the field of medicine. With a large set of agents, a partially observable stochastic environment, and…

cs.AI20201 cited

Exploring Unknown Universes in Probabilistic Relational Models

Tanya Braun, Ralf Möller

Large probabilistic models are often shaped by a pool of known individuals (a universe) and relations between them. Lifted inference algorithms handle sets of known individuals for…

cs.AI20192 cited

Taming Reasoning in Temporal Probabilistic Relational Models

Marcel Gehrke, Ralf Möller, Tanya Braun

Evidence often grounds temporal probabilistic relational models over time, which makes reasoning infeasible. To counteract groundings over time and to keep reasoning polynomial by…

cs.AI2018

Answering Hindsight Queries with Lifted Dynamic Junction Trees

Marcel Gehrke, Tanya Braun, Ralf Möller

The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a f…

cs.AI2018

Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

Marcel Gehrke, Tanya Braun, Ralf Möller

The lifted dynamic junction tree algorithm (LDJT) efficiently answers filtering and prediction queries for probabilistic relational temporal models by building and then reusing a f…

cs.AI2018

Fusing First-order Knowledge Compilation and the Lifted Junction Tree Algorithm

Tanya Braun, Ralf Möller

Standard approaches for inference in probabilistic formalisms with first-order constructs include lifted variable elimination (LVE) for single queries as well as first-order knowle…