Uncertain reasoning in AI concerns various forms of "probabilistic" as opposed
to logical inference. Our work in the area has focused on theory refinement
for knowledge bases using
uncertainty such as certainty-factor rule bases and Bayesian networks. Rather
than building such knowledge-bases completely manually or learning them
form scratch, our work focuses on using data to revise imperfect
expert-supplied initial KB's.