Bayesian Knowledge-Bases.

Abstract

Abstract Managing uncertainty in complex domains requires a flexible and semantically sound knowledge representation. This is especially important during the initial knowledge engineering and subsequent maintenance of the knowledge base. We present a new model of knowledge representation called Bayesian Knowledge Bases. It unifies an if then style rules with probability theory. We can prove that such a merger remains fully probabilistic and yet maintains full flexibility and intuitiveness.

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Document Details

Document Type
Technical Report
Publication Date
Aug 12, 1996
Accession Number
ADA324260

Entities

People

  • Eugene Santos

Organizations

  • Air Force Institute of Technology

Tags

Communities of Interest

  • C4I

DTIC Thesaurus Topics

  • Abstracts
  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computations
  • Expert Systems
  • Genetic Algorithms
  • Language
  • Mathematical Models
  • Notation
  • Numbers
  • Probability
  • Random Variables
  • Resilience
  • Semantics
  • Uncertainty

Fields of Study

  • Computer science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Artificial Intelligence

Technology Areas

  • AI & ML