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Eliciting knowledge for material design in steel making using paper models and codification scheme

Authors :
X.D. Fang
S.S. Shivathaya
Source :
Engineering Applications of Artificial Intelligence. 8:15-24
Publication Year :
1995
Publisher :
Elsevier BV, 1995.

Abstract

Knowledge elicitation (KEL) is the most important stage, but often the principal bottleneck, in the development of knowledge-based systems. Due to the difficulties faced in the knowledge-elicitation process, development of a knowledge-based system for material design in the steel-making industry is a complex task. An attempt is made in this paper to present a new approach to deal with knowledge elicitation for material design problems in the steel-making industry. This paper centres around the human aspects and is based on practical experience gained while developing a knowledge-based system for material design at BHP Steel, Australia. This approach involves codification of the customer's special requirements to identify the knowledge sources involved in the design process. This is followed by the use of paper models to improve the efficiency of the KEL process. The second stage of the structured interviews is based on the customer's special requirement codes for eliciting the missing information and for clarifying any ambiguities or inconsistencies. The paper also discusses the use of non-interviewing techniques to elicit the expert knowledge, in order to reduce the use of expensive interview time. The knowledge-representation scheme developed for the material design system aims at reducing the search time and storage space by utilising a codification scheme to classify various knowledge sources into appropriate categories.

Details

ISSN :
09521976
Volume :
8
Database :
OpenAIRE
Journal :
Engineering Applications of Artificial Intelligence
Accession number :
edsair.doi...........0869b9f10cb1d2d5e503e3ec986e1d83