Abductive inference in Bayesian belief networks is intended as the process
of generating the K most probable configurations given an observed evidence
. These configurations axe called explanations and in most of the approache
s found in the literature, all the explanations have the same number of lit
erals. In this paper we propose some criteria to simplify the explanations
in such a way that the resulting configurations are still accounting for th
e observed facts. Computational methods to perform the simplification task
are also presented. Finally the algorithms are experimentally tested using
a set of experiments which involves three different Bayesian belief network
s.