Probabilistic Knowledge Base Validation.

Abstract

Our work develops a new methodology and tool for the validation of probabilistic knowledge bases throughout their lifecycle. The methodology minimizes user interaction by automatically modifying incorrect knowledge; only the occurrence of incomplete knowledge involves interaction. These gains are realized by combining and modifying techniques borrowed from rule-based and artificial neural network validation strategies. The presented methodology is demonstrated through BVAL, which is designed for a new knowledge representation, the Bayesian Knowledge Base. This knowledge representation accommodates incomplete knowledge while remaining firmly grounded in probability theory.

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Document Details

Document Type
Technical Report
Publication Date
Dec 01, 1995
Accession Number
ADA306138

Entities

People

  • Howard T. Gleason

Organizations

  • Air Force Institute of Technology

Tags

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Air Force
  • Algorithms
  • Artificial Intelligence
  • Bayesian Networks
  • Computers
  • Diseases And Disorders
  • Expert Systems
  • Heart Diseases
  • Inference Engines
  • Knowledge Based Systems
  • Neural Networks
  • Pain
  • Probability
  • Random Variables
  • Reinforcement Learning

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence

Technology Areas

  • AI & ML