  Dempster/Shafer (D/S) theory has been advocated as a way of representing
incompleteness of evidence in a system's knowledge base. Methods now exist for
propagating beliefs through chains of inference. This paper discusses how rules
with attached beliefs, a common representation for knowledge in automated
reasoning systems, can be transformed into the joint belief functions required
by propagation algorithms. A rule is taken as defining a conditional belief
function on the consequent given the antecedents. It is demonstrated by example
that different joint belief functions may be consistent with a given set of
rules. Moreover, different representations of the same rules may yield
different beliefs on the consequent hypotheses.
