Guided incremental construction of belief networks

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

Research output: Chapter in Book/Report/Conference proceedingChapter

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)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsMichael R. Berthold, Hans-Joachim Lenz, Elizabeth Bradley, Rudolf Kruse, Christian Borgelt
PublisherSpringer-Verlag
Pages533-543
Number of pages11
ISBN (Print)3540408134, 9783540408130
DOIs
StatePublished - 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2810
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Sutton, C. A., Burns, B., Morrison, C., & Cohen, P. R. (2003). Guided incremental construction of belief networks. In M. R. Berthold, H-J. Lenz, E. Bradley, R. Kruse, & C. Borgelt (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 533-543). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2810). Springer-Verlag. https://doi.org/10.1007/978-3-540-45231-7_49