A method for managing evidential reasoning in a hierarchical hypothesis space
Artificial Intelligence
On evidential reasoning in a hierarchy of hypotheses
Artificial Intelligence
On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
A comparison of two evidential reasoning schemes
Artificial Intelligence
The Combination of Evidence in the Transferable Belief Model
IEEE Transactions on Pattern Analysis and Machine Intelligence
Readings in uncertain reasoning
Readings in uncertain reasoning
Random sets and fuzzy interval analysis
Fuzzy Sets and Systems - Special issue on mathematical aspects of fuzzy sets
Combining belief functions when evidence conflicts
Decision Support Systems
Rule Based Expert Systems: The Mycin Experiments of the Stanford Heuristic Programming Project (The Addison-Wesley series in artificial intelligence)
Intelligent decision system and its application in business innovation self assessment
Decision Support Systems
Analyzing the combination of conflicting belief functions
Information Fusion
Robust combination rules for evidence theory
Information Fusion
A computational model for causal and diagnostic reasoning in inference systems
IJCAI'83 Proceedings of the Eighth international joint conference on Artificial intelligence - Volume 1
Representing uncertainty on set-valued variables using belief functions
Artificial Intelligence
An empirical test of the evidential reasoning approach's synthesis axioms
Expert Systems with Applications: An International Journal
On the evidential reasoning algorithm for multiple attribute decision analysis under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Multiple-attribute decision making under uncertainty: the evidential reasoning approach revisited
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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This paper aims to establish a unique Evidential Reasoning (ER) rule to combine multiple pieces of independent evidence conjunctively with weights and reliabilities. The novel concept of Weighted Belief Distribution (WBD) is proposed and extended to WBD with Reliability (WBDR) to characterise evidence in complement of Belief Distribution (BD) introduced in Dempster-Shafer (D-S) theory of evidence. The implementation of the orthogonal sum operation on WBDs and WBDRs leads to the establishment of the new ER rule. The most important property of the new ER rule is that it constitutes a generic conjunctive probabilistic reasoning process, or a generalised Bayesian inference process. It is shown that the original ER algorithm is a special case of the ER rule when the reliability of evidence is equal to its weight and the weights of all pieces of evidence are normalised. It is proven that Dempster@?s rule is also a special case of the ER rule when each piece of evidence is fully reliable. The ER rule completes and enhances Dempster@?s rule by identifying how to combine pieces of fully reliable evidence that are highly or completely conflicting through a new reliability perturbation analysis. The main properties of the ER rule are explored to facilitate its applications. Several existing rules are discussed and compared with the ER rule. Numerical and simulation studies are conducted to show the features of the ER rule.