Article: Relational Dynamic Bayesian Networks

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Dec 2, 2005, 6:49:57 PM12/2/05
JAIR is pleased to announce the publication of the following article:

Sanghai, S., Domingos, P. and Weld, D. (2005)
"Relational Dynamic Bayesian Networks",
Volume 24, pages 759-797.

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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 filter's 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.

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Steven Minton
JAIR Managing Editor

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