Exploring evidential reasoning in multicriteria decision making under uncertainty

Authors:
Yuliang Fan;Peter Deer
Affiliations:
School of Information and Communication Technology, Griffith University, Gold Coast Campus, PMB 50 Gold Coast Mail Centre, Queensland 9726, Australia.;School of Information and Communication Technology, Griffith University, Gold Coast Campus, PMB 50 Gold Coast Mail Centre, Queensland 9726, Australia
Venue:
International Journal of Intelligent Systems Technologies and Applications
Year:
2008

Citing 12
Cited 0

On the Dempster-Shafer framework and new combination rules

Information Sciences: an International Journal
Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
The dynamic of belief in the transferable belief model and specialization-generalization matrices

UAI '92 Proceedings of the eighth conference on Uncertainty in Artificial Intelligence
The transferable belief model

Artificial Intelligence
Principles of uncertainty: what are they? Why do we need them?

Fuzzy Sets and Systems - Special issue on nuclear engineering
Generating belief functions from qualitative preferences: an approach to eliciting expert judgments and deriving probability functions

Data & Knowledge Engineering
Eliciting and analyzing expert judgment: a practical guide

Eliciting and analyzing expert judgment: a practical guide
Fast Algorithms for Dempster-Shafer Theory

IPMU '90 Proceedings of the 3rd International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems: Uncertainty in Knowledge Bases
Computational aspects of the Mobius transformation

UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
Decision making in the TBM: the necessity of the pignistic transformation

International Journal of Approximate Reasoning
Representing partial ignorance

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty

IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans

Quantified Score

Hi-index	0.00

Visualization

Abstract

In a typical Multicriteria Decision Making (MCDM) problem, we need to combine the subjective opinions of a number of experts. Existing Evidential Reasoning (ER) approaches apply the Dempster-Shafer theory (DS theory) of evidence to all levels of subattributes, with the possibility of different Frame of Discernment (FOD) and compute the expected utility values directly from the combined experts' belief distributions. We introduce a two-level Transferable Belief Model (TBM) to the reasoning process and a general interval-based DS belief structure to effect reasoning under the same FOD. Within this framework, we can combine beliefs of possible subattributes and make rational decisions based on real probability distributions.