Paper Info
Title
A- And V-Uncertainty: An Exploration About Uncertainty Modeling From A Engineering Perspective
Abstract
This paper presents a concrete experience of Knowledge Engineering, which, starting from a specific problem which occurred during the development of ASTRA, a knowledge-based system for preventive diagnosis of power transformers, turned out to provide significant insights concerning modeling of uncertain knowledge. In particular, it was observed that there are (at least) two conceptually distinct types of uncertainty about validity(V-uncertainty, for short), which are different in nature and play different roles in uncertain reasoning. The concepts of A- and V-uncertainty are applicable in any context where uncertainty affecting domian knowledge can be ascribed to two kinds of sources: on the one hand, the existence of exceptions, on the other hand, deep-rooted doubts about the foundations themselves of the relevant domain knowledge. The introduction of these concepts allows one to define articulated uncertainty models, supporting the representation of the reasoning mechanisms used by experts in domains where both such uncertainty sources are present. This general claim was confirmed by the experience developed with ASTRA, where the explicit representation and management of A- and V-uncertainty enable the correct treatment of some critical diagnostic cases.
Year
DOI
Venue
2007
10.1142/S0218213007003278
INTERNATIONAL JOURNAL ON ARTIFICIAL INTELLIGENCE TOOLS
Keywords
Field
DocType
uncertainty representation, knowledge acquisition, uncertain reasoning
Body of knowledge,Domain knowledge,Computer science,Uncertainty analysis,Artificial intelligence,Knowledge engineering,ASTRA,Uncertainty representation,Uncertainty modeling,Machine learning,Management science,Knowledge acquisition
Journal
Volume
Issue
ISSN
16
2
0218-2130
Citations 
PageRank 
References 
1
0.36
17
Authors
3
Name
Order
Citations
PageRank
Pietro Baroni172250.00
Giovanni Guida245067.68
Massimiliano Giacomin378554.15