Exploring Unknown Universes in Probabilistic Relational Models

Abstract

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.

Lifting DecPOMDPs for Nanoscale Systems -- A Work in Progress

“…The models currently do not have further structure, which makes this problem challenging. Given a factorised representation with local distributions, inference in unknown universes (Braun and Möller 2019) is a starting point.…”