Knowledge-Based System

Knowledge-Based System

DOI: 10.4018/978-1-60566-864-2.ch002
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Abstract

Knowledge-Based System (KBS), a branch research area of AI, has been widely used in interpretation, prediction, diagnosis, debugging, design, planning, monitoring, repair, instruction, and control (Stefik et al., 1982) since it emerged in 1960s. KBS has been recognized as a promising paradigm for the next generation manufacturing systems and there is no doubt that the use of KBS in manufacturing will continue to expand, both in areas of application as well as in depth of knowledge. As a result, factories will benefit a lot, such as improved productivity, more stable and increased yields and increased asset utilization, all leading to improved factory performance. Now KBS are finding an increasing number of applications in almost each stage of intelligent manufacturing, including design, process planning and scheduling, production control, diagnosis and etc. Followed by a case study, the overview over all these applications will be discussed in this chapter after the key technologies of KBS are presented, including knowledge representation, knowledge use, knowledge acquisition and evaluation of KBS.
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Introduction

Background

Knowledge-Based System (KBS), a branch research area of AI, has been widely used in interpretation, prediction, diagnosis, debugging, design, planning, monitoring, repair, instruction, and control (Stefik et al., 1982) since it emerged in 1960s. KBS has been recognized as a promising paradigm for the next generation manufacturing systems and there is no doubt that the use of KBS in manufacturing will continue to expand, both in areas of application as well as in depth of knowledge. As a result, factories will benefit a lot, such as improved productivity, more stable and increased yields and increased asset utilization, all leading to improved factory performance. Now KBS are finding an increasing number of applications in almost each stage of intelligent manufacturing, including design, process planning and scheduling, production control, diagnosis and etc. Followed by a case study, the overview over all these applications will be discussed in this chapter after the key technologies of KBS are presented, including knowledge representation, knowledge use, knowledge acquisition and evaluation of KBS.

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