Introduction to the <i>ER</i> Rule for Evidence Combination

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Abstract

The Evidential Reasoning (ER) approach has been developed to support multiple criteria decision making (MCDM) under uncertainty. It is built upon Dempster's rule for evidence combination and uses belief functions for dealing with probabilistic uncertainty and ignorance. In this introductory paper, following a brief introduction to Dempster's rule and the ER approach, we report the discovery of a new generic ER rule for evidence combination [16]. We first introduce the concepts and equations of a new extended belief function and then examine the detailed combination equations of the new ER rule. A numerical example is provided to illustrate the new ER rule.
Original languageEnglish
Title of host publicationIntegrated Uncertainty In Knowledge Modelling And Decision Making
EditorsYC Tang, VN Huynh, J Lawry
Pages7-15
Number of pages9
Volume7027
Publication statusPublished - 2011

Publication series

NameLecture Notes in Artificial Intelligence

Keywords

  • Belief function
  • Dempster's rule
  • Evidence combination
  • Evidential reasoning
  • Multiple criteria decision making

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