MACHINE LEARNING APPROACHES TO KNOWLEDGE SYNTHESIS AND INTEGRATION TASKS FOR ADVANCED ENGINEERING AUTOMATION

被引：33

作者：

LU, SCY ^{[1
]}

机构：

[1] UNIV ILLINOIS,DEPT MECH & IND ENGN,KNOWLEDGE BASED ENGN SYST RES LAB,URBANA,IL 61801

来源：

COMPUTERS IN INDUSTRY | 1990年 / 15卷 / 1-2期

关键词：

D O I：

10.1016/0166-3615(90)90088-7

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

This paper introduces machine learning approaches from AI research as knowledge processing tools to synthesize and integrate engineering knowledge for advanced automation. An inductive/deductive decision-making framework, which serves as the foundation of our research, is explained. Machine learning is employed as an automatic model building tool, and as a transformation tool among different knowledge representations. The result from the synthesis and integration process is more comprehensive, proactive, and comprehensible domain models (knowledge) which have increased utilities in engineering practice. Several research projects are explained to illustrate implementation challenges and practical impacts of knowledge synthesis and integration. © 1990.

引用

页码：105 / 120

页数：16