  Bayesian belief networks can be used to represent and to reason about complex
systems with uncertain, incomplete and conflicting information. Belief networks
are graphs encoding and quantifying probabilistic dependence and conditional
independence among variables. One type of reasoning of interest in diagnosis is
called abductive inference (determination of the global most probable system
description given the values of any partial subset of variables). In some
cases, abductive inference can be performed with exact algorithms using
distributed network computations but it is an NP-hard problem and complexity
increases drastically with the presence of undirected cycles, number of
discrete states per variable, and number of variables in the network. This
paper describes an approximate method based on genetic algorithms to perform
abductive inference in large, multiply connected networks for which complexity
is a concern when using most exact methods and for which systematic search
methods are not feasible. The theoretical adequacy of the method is discussed
and preliminary experimental results are presented.
