The role of relevance in explanation I: Irrelevance as statistical independence

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34 Scopus citations

Abstract

We evaluate current explanation schemes. These are either insufficiently general, or suffer from other serious drawbacks. A domain-independent explanation theory, based on ignoring irrelevant variables in a probabilistic setting, is proposed. Independence-based maximum aposteriori probability (IB-MAP) explanations, an instance of irrelevance-based explanation, has several interesting properties, which provide for simple algorithms for computing such explanations. A best-first algorithm that generates IB-MAP explanations is presented, and evaluated empirically. The algorithm shows reasonable performance for up to medium-size problems on a set of randomly generated belief networks. An alternate algorithm, based on linear systems of inequalities, is discussed.

Original languageEnglish
Pages (from-to)281-324
Number of pages44
JournalInternational Journal of Approximate Reasoning
Volume8
Issue number4
DOIs
StatePublished - 1 Jan 1993

Keywords

  • Bayesian belief networks
  • abduction
  • explanation under uncertainty
  • probabilistic reasoning
  • relevance

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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