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Rapid development of knowledge-based systems via integrated knowledge acquisition

Published online by Cambridge University Press:  12 February 2004

HAO XING ,
SAMUEL H. HUANG  and
J. SHI
HAO XING
Affiliation:
Department of Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, Ohio 43606, USA
SAMUEL H. HUANG
Affiliation:
Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221, USA
J. SHI
Affiliation:
Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221, USA
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Abstract

This paper presents a novel approach, which is based on integrated (automatic/interactive) knowledge acquisition, to rapidly develop knowledge-based systems. Linguistic rules compatible with heuristic expert knowledge are used to construct the knowledge base. A fuzzy inference mechanism is used to query the knowledge base for problem solving. Compared with the traditional interview-based knowledge acquisition, our approach is more flexible and requires a shorter development cycle. The traditional approach requires several rounds of interviews (both structured and unstructured). However, our method involves an optional initial interview, followed by data collection, automatic rule generation, and an optional final interview/rule verification process. The effectiveness of our approach is demonstrated through a benchmark case study and a real-life manufacturing application.

Keywords

Drop Hammer FormingData DiscretizationFuzzy InferenceKnowledge AcquisitionKnowledge-Based SystemRule Simplification
Type
Research Article
Information
AI EDAM , Volume 17 , Issue 3 , August 2003 , pp. 221 - 234
Copyright
© 2003 Cambridge University Press

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