  In this paper a new mathematical procedure is presented for combining
different pieces of evidence which are represented in the interval form to
reflect our knowledge about the truth of a hypothesis. Evidences may be
correlated to each other (dependent evidences) or conflicting in supports
(conflicting evidences). First, assuming independent evidences, we propose a
methodology to construct combination rules which obey a set of essential
properties. The method is based on a geometric model. We compare results
obtained from Dempster-Shafer's rule and the proposed combination rules with
both conflicting and non-conflicting data and show that the values generated by
proposed combining rules are in tune with our intuition in both cases.
Secondly, in the case that evidences are known to be dependent, we consider
extensions of the rules derived for handling conflicting evidence. The
performance of proposed rules are shown by different examples. The results show
that the proposed rules reasonably make decision under dependent evidences

Artificial Intelligence