Exploring Unknown Universes in Probabilistic Relational Models
This addresses a limitation in probabilistic relational models for researchers and practitioners in AI and machine learning, though it appears incremental as it builds on existing lifted inference methods.
The paper tackles the problem of performing inference in probabilistic relational models when the universe of individuals is unknown, which prevents the use of tractable lifted inference algorithms. It proposes a semantics for such models that is decoupled from a specific constraint language to enable lifted inference.
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.