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Zadehian Paradigms for Knowledge Extraction in Intelligent Manufacturing

K. Harib

Published 2006 in Unknown venue

ABSTRACT

Manufacturing is a knowledge-intensive activity. The knowledge underlying a specific manufacturing process or system is often extracted from a small set of experimental observations. To automate the knowledge extraction process various machine learning methods have been used (Pham & Afifi, 2005; Monostori, 2003). Even though such methods are used, a great deal of human intelligence (knowledge extractor’s judgment, preference) is required for get-ting good results (Ullah & Khalifa, 2006). As a result, a machine learning method that is able to utilize human cognition as straightforwardly as possible seems more realistic for extracting knowledge in manufacturing. In fact, hu-man-assisted machine learning methods are in agreement with the modern concept of manufacturing automation—how to support humans with com-puters rather than how to replace humans by computers (Kals et al., 2004). Thus, for advanced manufacturing systems, the machine learning methods wherein humans and computers compliment each other and the course of knowleldge extraction is determine by the human cognition rather than by a fully automated algorethemic approach is desirable. Artificial intelligence community has also started to recognize the need for human-assisted machining learning methods (i.e., human comprehensible ma-chine learning methods): “Humans need to trust that intelligent systems are behaving correctly, and one way to achieve such trust is to enable people to understand the inputs, outputs, and algorithms used as well as any new knowledge acquired through learning. As the use of machine learning increases in critical operations it is being applied increasingly in domains where the learning system's inputs and outputs must be understood, or even modified, by human operators….” (Dan Oblinger, AAAI Technical Report, WS-05-04, 2005.)

PUBLICATION RECORD

  • Publication year

    2006

  • Venue

    Unknown venue

  • Publication date

    2006-07-01

  • Fields of study

    Computer Science, Engineering

  • Identifiers
    DOI 10.5772/5052
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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