Abstract

Intelligent agents must function in an uncertain world, containing multiple objects and relations that change over time. Unfortunately, no representation is currently available that can handle all these issues, while allowing for principled and efficient inference. This paper addresses this need by introducing dynamic probabilistic relational models (DPRMs). DPRMs are an extension of dynamic Bayesian networks (DBNs) where each time slice (and its dependences on previous slices) is represented by a probabilistic relational model (PRM). Particle filtering, the standard method for inference in DBNs, has severe limitations when applied to DPRMs, but we are able to greatly improve its performance through a form of relational Rao-Blackwellisation. Further gains in efficiency are obtained through the use of abstraction trees, a novel data structure. We successfully apply DPRMs to execution monitoring and fault diagnosis of an assembly plan, in which a complex product is gradually constructed from subparts.

Citations