Rapid development of knowledge-based systems via integrated knowledge acquisition

Authors:
Hao Xing;Samuel H. Huang;J. Shi
Affiliations:
Department of Mechanical, Industrial and Manufacturing Engineering, University of Toledo, Toledo, Ohio 43606, USA;Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221, USA;Intelligent CAM Systems Laboratory, Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, Cincinnati, Ohio 45221, USA
Venue:
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Year:
2003

Citing 14
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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.