Uncertainty in Artificial Intelligence
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Evidence Combination and Reasoning and Its Application to Real-World Problem-Solving
L. Chang, Rangasami Kashyap
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
Pages: 370-377
PS Link:
PDF Link: /papers/90/p370-chang.pdf
AUTHOR = "L. Chang and Rangasami Kashyap",
TITLE = "Evidence Combination and Reasoning and Its Application to Real-World Problem-Solving",
BOOKTITLE = "Proceedings of the Sixth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-90)",
ADDRESS = "Corvallis, Oregon",
YEAR = "1990",
PAGES = "370--377"

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