Date of Award
12-1996
Document Type
Thesis
Degree Name
Master of Science
Department
Department of Electrical and Computer Engineering
First Advisor
Eugene Santos, PhD
Abstract
This work develops tools and techniques to identify particular Bayesian Knowledge Base (BKB) incompletenesses, and to modify the existing knowledge-base (KB) structure to correct these problems. The methodology performs manually or automatically, informing the user of either problems causing the incompleteness, or of details resulting from the automatic knowledge-base correction. The proposed methodology is designed for integration with BVAL, to augment BVAL's validation techniques.
AFIT Designator
AFIT-GCS-ENG-96D-17
DTIC Accession Number
ADA320697
Recommended Citation
Lyle, Louise J., "A Test-Case Based Approach to Bayesian Knowledge Base Incompleteness Detection and Correction" (1996). Theses and Dissertations. 5874.
https://scholar.afit.edu/etd/5874