Preventing Groundings and Handling Evidence in the Lifted Junction Tree Algorithm

AI 2018: Advances in Artificial Intelligence

2018

Self Cite

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

Section: Arxiv:180700744v1 [Csai] 2 Jul 2018mentioning

confidence: 99%

Section: Lifted Dynamic Junction Tree Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Preventing Unnecessary Groundings in the Lifted Dynamic Junction Tree Algorithm

Gehrke

AI 2018: Advances in Artificial Intelligence

2018

Self Cite

“…A known universe means that logvars in parfactors or Markov logic networks have a domain and possibly a constraint restricting domains to certain constants for specific parfactors or formulas. Lifted inference algorithms such as (i) lifted variable elimination (LVE) (Poole 2003;Taghipour et al 2013), (ii) the lifted junction tree algorithm (Braun and Möller 2017), (iii) first-order knowledge compilation (Van den Broeck et al 2011), (iv) probabilistic theorem proving (Gogate and Domingos 2011), or (v) lifted belief propagation (Ahmadi et al 2013), use domains or constraints to determine the number of individuals represented to be able to perform efficient inference.…”

Section: Introductionmentioning

confidence: 99%

Exploring Unknown Universes in Probabilistic Relational Models

AI 2019: Advances in Artificial Intelligence

2019

Self Cite

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 tractable inference. Universes may not always be known, though, or may only described by assumptions such as "small universes are more likely". Without a universe, inference is no longer possible for lifted algorithms, losing their advantage of tractable inference. The aim of this paper is to define a semantics for models with unknown universes decoupled from a specific constraint language to enable lifted and thereby, tractable inference.

“…More specifically, IT security involves network dependencies (relational) for many components (objects), streams of attacks over time (temporal), and uncertainties due to, for example, missing or incomplete information, caused by faulty sensor data. By performing model counting, probabilistic databases (PDBs) can answer queries for relational temporal models with uncertainties (Dignös, Böhlen, and Gamper 2012;Dylla, Miliaraki, and Theobald 2013). However, each query embeds a process behaviour, resulting in huge queries with possibly redundant information.…”

Section: Introductionmentioning

confidence: 99%

Answering Multiple Conjunctive Queries with the Lifted Dynamic Junction Tree Algorithm

Gehrke

AI 2018: Advances in Artificial Intelligence

2018

Self Cite

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