Quantitative Knowledge Acquisition for Expert Systems

Abstract

A common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. While it is relatively easy to collect data and to log the comments of human operators engaged in experiments, generalizing such information to a set of rules has not previously been a straightforward task. This paper presents a statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors. The specific objective is to design an expert system that selects a satisfactory suite of measurements from a dissimilar, redundant set, given an arbitrary navigation geometry and possible sensor failures. This paper describes the systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data. The development method invokes two statistical techniques: Analysis of Variance (ANOVA) and the ID3 algorithm. (rrh)

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 1990
Accession Number
ADA224297

Entities

People

  • Brenda L. Belkin
  • Robert F. Stengel

Organizations

  • Princeton University

Tags

Communities of Interest

  • Air Platforms
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Accuracy
  • Acquisition
  • Aircrafts
  • Analysis Of Variance
  • Artificial Intelligence
  • Engineering
  • Expert Systems
  • Hybrid Systems
  • Inertial Navigation
  • Inertial Navigation Systems
  • Kalman Filters
  • Loran
  • Navigation
  • Radio Navigation
  • Radio Ranges (Transmitters)
  • Rule Based Systems
  • Trajectories

Fields of Study

  • Computer science

Readers

  • Artificial Intelligence
  • Inertial Navigation Systems.
  • Regression Analysis.