Guided incremental construction of belief networks

Charles A. Sutton, Brendan Burns, Clayton T Morrison, Paul R Cohen

Research output: Contribution to journalArticle

Abstract

Because uncertain reasoning is often intractable, it is hard to reason with a large amount of knowledge. One solution to this problem is to specify a set of possible models, some simple and some complex, and choose which to use based on the problem. We present an architecture for interpreting temporal data, called AIID, that incrementally constructs belief networks based on data that arrives asynchronously. It synthesizes the opportunistic control of the blackboard architecture with recent work on constructing belief networks from fragments. We have implemented this architecture in the domain of military analysis.

Original languageEnglish (US)
Pages (from-to)533-543
Number of pages11
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2810
StatePublished - 2003
Externally publishedYes

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Belief Networks
Bayesian networks
Uncertain Reasoning
Military
Fragment
Choose
Architecture
Model

ASJC Scopus subject areas

  • Computer Science(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Theoretical Computer Science

Cite this

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