A Bayesian Knowledge Base (BKB) is a knowledge representation for
information containing uncertainty. BKBs use random variables(RVs) and
probabilities to represent knowledge. Unlike other
approaches such as Bayesian Networks[4], BKBs are
designed to handle incompleteness in our knowledge
. One of the inferencing tasks performed
on BKBs is belief revision. In belief revision, we attempt to find the
most likely state for every variable. Unfortunately, when we use
genetic algorithms(GAs)[2, 5, 3]
to perform belief revision, there are many combinations of
states that occur which are not computable. These combinations occur when the
relation between states of RVs is incomplete. If we don't evaluate the
incompletenesses, the GA has a relatively flat solution space and
performs poorly. In this paper, we provide a solution to this problem
by developing a way to evaluate the incompletenesses. By evaluating
incompletenesses, we create new slopes for the GA to climb. This
enables the GA to converge to the optimal solution faster.
Section 2 provides a description and an example of a BKB. Section 3 discusses how genetic algorithms can be used to perform belief revision, and also reveals the problems associated using genetic algorithms on BKBs. Section 4 presents a method to evaluate incompleteness, and Section 5 describes our test methodology. Finally, Section 6 presents our conclusions.