Abstract
A 2-D model for evidential reasoning is proposed, in which the belief function of evidence is represented as a belief density function which can be in a continuous or discrete form. A vector form of mutual dependency relationship of the evidence is considered and a dependency propagation theorem is proved. This robust method can resolve the conflicts resulting from either the mutual dependency among evidences or the structural dependency in an inference network due to the evidence combination order. Belief conjunction, belief combination, belief propagation procedures, and AND/OR operations of an inference network based on the proposed 2-D model are all presented, followed by some examples demonstrating the advantages of this method over the conventional methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Cheng, Y. and Kashyap, R.L., A study of associative evidential Reasoning,IEEE Trans. Pattern Anal. Machine Intell. 11(6) (June 1989).
Cohen, P.R.,Heuristic Reasoning about Uncertainty: An Artificial Intelligence Approach, Morgan Kaufmann, Los Altos, CA., 1985.
Duda, R.O., Hart, P.E. and Nilsson, N., Subjective Baysian methods for rule-based inference systems,Proc. AFIPS (1976).
Ginsberg, M.L., Nonmonotonic reasoning using Dempster's rule,Proc. National Conf. Artificial Intell. (1984), 126–129.
Hau, H.Y. and Kashyap, R.L., Belief combination and propagation in a lattice-structured inference network,IEEE Trans. Systems Man Cybernet. 20(1) (Jan./Feb. 1990).
Hummel, R.A. and Landy, M.S., A statistical viewpoint on the theory of evidence,IEEE Trans. Pattern Anal. Machine Intell. PAMI-10(2) (Mar. 1988).
Ihara, J., Extension of conditional probability and measures of belief and disbelief in hypothesis based on uncertain evidence,IEEE Trans. Pattern Anal. Machine Intell. PAMI-9(4) (Jul. 1987).
Ishizuka, M., Fu, K.S., and Yao, J.T.P., Inference procedure with uncertainty for problem reduction method,Information Sci. 28 (1983), 179–206.
Laskey, K.B. and Lehner, P.E., Assumptions, belief and probability,Artificial Intell. 41 (1989/90), 65–77.
Pearl, J., Fusion, propagation, and structuring in belief networks,Artificial Intell. 29 (1986), 241–288.
Shafer, G. and Logan, R., Implementing Dempster's rule for hierarchical evidence,Artificial Intell. 33 (1987).
Shafer, G. and Pearl, J.,Readings in Uncertain Reasoning, Morgan Kaufmann, San Mateo, CA., 1990.
Shenoy, P.P. and Shafer, G., Propagating belief functions with local computation,IEEE Expert, (Fall 1986), 43–51.
Tahani, H. and Keller, J.M., Information fusion in computer vision using the fuzzy integral,IEEE Trans. Systems Man Cybernet. 20(3) (May/June 1990).
Widman, L.E., Loparo, K.A. and Nielsen, N.R.,Artificial Intelligence, Simulation, and Modeling, Wiley, New York, 1989.
Yen, J., Generalizing the Dempster—Shafer theory to fuzzy sets,IEEE Trans. Systems Man Cybernet. 20(3) (May/June 1990).
Zadeh, L.A., A simple view of the Dempster—Shafer theory of evidence and its implication for the rule of combination,Fuzzy Set Syst. 11 (1983).
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Wang, CC., Don, HS. A robust continuous model for evidential reasoning. J Intell Robot Syst 10, 147–171 (1994). https://doi.org/10.1007/BF01258226
Received:
Accepted:
Issue Date:
DOI: https://doi.org/10.1007/BF01258226