Relational Dynamic Bayesian Networks

09/09/2011
by   P. Domingos, et al.
0

Stochastic processes that involve the creation of objects and relations over time are widespread, but relatively poorly studied. For example, accurate fault diagnosis in factory assembly processes requires inferring the probabilities of erroneous assembly operations, but doing this efficiently and accurately is difficult. Modeled as dynamic Bayesian networks, these processes have discrete variables with very large domains and extremely high dimensionality. In this paper, we introduce relational dynamic Bayesian networks (RDBNs), which are an extension of dynamic Bayesian networks (DBNs) to first-order logic. RDBNs are a generalization of dynamic probabilistic relational models (DPRMs), which we had proposed in our previous work to model dynamic uncertain domains. We first extend the Rao-Blackwellised particle filtering described in our earlier work to RDBNs. Next, we lift the assumptions associated with Rao-Blackwellization in RDBNs and propose two new forms of particle filtering. The first one uses abstraction hierarchies over the predicates to smooth the particle filters estimates. The second employs kernel density estimation with a kernel function specifically designed for relational domains. Experiments show these two methods greatly outperform standard particle filtering on the task of assembly plan execution monitoring.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
01/16/2013

Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks

Particle filters (PFs) are powerful sampling-based inference/learning al...
research
12/12/2012

Factored Particles for Scalable Monitoring

Exact monitoring in dynamic Bayesian networks is intractable, so approxi...
research
03/29/2016

Towards Practical Bayesian Parameter and State Estimation

Joint state and parameter estimation is a core problem for dynamic Bayes...
research
07/10/2019

Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks (Extended Abstract)

Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
research
03/13/2013

Expressing Relational and Temporal Knowledge in Visual Probabilistic Networks

Bayesian networks have been used extensively in diagnostic tasks such as...
research
01/30/2014

Exploiting Causality for Selective Belief Filtering in Dynamic Bayesian Networks

Dynamic Bayesian networks (DBNs) are a general model for stochastic proc...
research
10/10/2019

PROFET: Construction and Inference of DBNs Based on Mathematical Models

This paper presents, evaluates, and discusses a new software tool to aut...

Please sign up or login with your details

Forgot password? Click here to reset