Converting a rule-based expert system into a belief network

M. Korver*, P.J.F. Lucas

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

45 Citations (Scopus)
14 Downloads (Pure)

Abstract

The theory of belief networks offers a relatively new approach for dealing with uncertain information in knowledge-based (expert) systems. In contrast with the heuristic techniques for reasoning with uncertainty employed in many rule-based expert systems, the theory of belief networks is mathematically sound, based on techniques from probability theory. It therefore seems attractive to convert existing rule-based expert systems into belief networks. In this article we discuss the design of a belief network reformulation of the diagnostic rule-based expert system HEPAR. For the purpose of this experiment we have studied several typical pieces of medical knowledge represented in the HEPAR system It turned out that, due to the differences in the type of knowledge represented and in the formalism used to represent uncertainty, much of the medical knowledge required for building the belief network concerned could not be extracted from IIEPAR. As a consequence, significant additional knowledge acquisition was required. However, the objects and attributes defined in the HEPAR system, as well as the conditions in production rules mentioning these objects and attributes, were useful for guiding the selection of the statistical variables for building the belief network. The mapping of objects and attributes in HEPAR to statistical variables is discussed in detail.

Original languageEnglish
Pages (from-to)219-241
Number of pages23
JournalInformatics for health and social care
Volume18
Issue number3
DOIs
Publication statusPublished - 1993
Externally publishedYes

Keywords

  • Belief networks
  • Causal graphs
  • Decision support systems
  • Medical expert systems

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