Constrained abductive reasoning with fuzzy parameters in Bayesian networks

Han-Lin Li, Han Ying Kao*

*Corresponding author for this work

Research output: Contribution to journalArticle

20 Scopus citations

Abstract

This work proposes a novel approach for solving abductive reasoning problems in Bayesian networks involving fuzzy parameters and extra constraints. The proposed method formulates abduction problems using nonlinear programming. To maximize the sum of the fuzzy membership functions subjected to various constraints, such as boundary, dependency and disjunctive conditions, unknown node belief propagation is completed. The model developed here can be built on any exact propagation methods, including clustering, joint tree decomposition, etc.

Original languageEnglish
Pages (from-to)87-105
Number of pages19
JournalComputers and Operations Research
Volume32
Issue number1
DOIs
StatePublished - 1 Jan 2005

Keywords

  • Abductive reasoning
  • Bayesian networks
  • Constraints
  • Fuzzy parameters
  • Optimization

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