Integrating integer programming and probabilistic deduction graphs for probabilistic reasoning

Han-Lin Li*, Chao Chih Yang

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Optimal solutions of several variants of the probabilistic reasoning problem were found by a new technique that integrates integer programming and probabilistic deduction graphs (PDG). PDGs are extended from deduction graphs of the and-type via normal deduction graphs. The foregoing variants to be solved can involve multiple hypotheses and multiple evidences where the former is given and the latter is unknown and being found or vice versa. The relationship among these hypotheses and evidences with possible intermediaries is represented by a causal graph. The proposed method can handle a large causal graph of any type and find an optimal solution by invoking a linear integer programming package. In addition, formulating the reasoning problem to fit integer programming takes a polynomial time.

Original languageEnglish
Pages (from-to)195-214
Number of pages20
JournalJournal of Systems Integration
Volume1
Issue number2
DOIs
StatePublished - 1 Aug 1991

Keywords

  • abduction
  • causal graph
  • deduction
  • deduction graph
  • expert system
  • integer programming
  • medical diagnosis
  • probabilistic reasoning
  • quantitative logic

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