Knowledge acquisition for expert systems using statistical methodsA common problem in the design of expert systems is the definition of rules from data obtained in system operation or simulation. A statistical method for generating rule bases from numerical data, motivated by an example based on aircraft navigation with multiple sensors is presented. 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. The systematic development of a Navigation Sensor Management (NSM) Expert System from Kalman Filter covariance data is described. The development method invokes two statistical techniques: Analysis-of-Variance (ANOVA) and the ID3 algorithm. The ANOVA technique indicates whether variations of problem parameters give statistically different covariance results, and the ID3 algorithm identifies the relationships between the problem parameters using probabilistic knowledge extracted from a simulation example set.
Document ID
19910015815
Acquisition Source
Legacy CDMS
Document Type
Conference Paper
Authors
Belkin, Brenda L. (Princeton Univ. NJ, United States)
Stengel, Robert F. (Princeton Univ. NJ, United States)
Date Acquired
August 14, 2013
Publication Date
April 1, 1991
Publication Information
Publication: AGARD, Knowledge Based System Applications for Guidance and Control