Dynamic Domain Sizes in Temporal Probabilistic Relational Models

Abstract

Probabilistic dynamic relational models (PDRMs) allow for an expressive, yet sparse and efficient representation of uncertain temporal (dynamic) and relational information with a fixed (static) set of domain objects (entities). While for different points in time, information about objects may differ, the set of objects under consideration is the same for all time points in standard PDRMs. Motivated by examples from a logistics application, in this paper we extend the theory of PDRMs with dynamically changing sets of domain objects. The paper introduces the semantics of so-called PD²RMs and analyses model management as well as query answering problems and algorithms.