Abductive reasoning by constructing probabilistic deduction graphs for solving the diagnosis problem

Han-Lin Li*, Chao Chih Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

An algorithm is proposed for finding optimal solutions of the diagnosis problem by using deduction graphs (DG) to accomplish abductions of multiple causes and multiple symptoms. The relationship among causes, symptoms, and possible intermediaries is represented by a causal network. The algorithm accomplishes the abduction by constructing a deduction graph DG(C,S) from the cause set C to the symptom set S representing the subnetwork such that the product of the prior probability, P(C), of C and the conditional probability, P(S/C), of DG(C,S) is maximized. An optimal solution is achieved by solving a 0/1 linear integer programming problem. Based on some assumptions, the algorithm can deal with a causal network involving various mutually independent deduction graphs.

Original languageEnglish
Pages (from-to)121-131
Number of pages11
JournalDecision Support Systems
Volume7
Issue number2
DOIs
StatePublished - 1 Jan 1991

Keywords

  • Abduction
  • Causal network
  • Deduction
  • Deduction graph
  • Diagnosis
  • Expert system
  • Integer programming
  • Mutually independent or exclusive
  • Optimization
  • Probabilistic reasoning

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