Systematic causal knowledge acquisition using FCM Constructor for product design decision support

  • Authors:

  • Wooi Ping Cheah;Yun Seon Kim;Kyoung-Yun Kim;Hyung-Jeong Yang

  • Affiliations:

  • Faculty of Information Science and Technology, Multimedia University, Melaka, Malaysia;Department of Industrial and Manufacturing Engineering, Wayne State University, 4815 Fourth St., Detroit, MI 48202, USA;Department of Industrial and Manufacturing Engineering, Wayne State University, 4815 Fourth St., Detroit, MI 48202, USA;School of Electronics and Computer Engineering, Chonnam National University, Gwangjusi, South Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2011

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Abstract

Despite its usefulness, design knowledge is not often captured or documented, and is therefore lost or damaged after a product design is completed. As a way to address this issue, two major formalisms can be used for modeling, representing, and reasoning about causal design knowledge: fuzzy cognitive map (FCM) and Bayesian belief network (BBN). Although FCM has been used extensively in knowledge engineering, few methodologies exist for systematically constructing it. In this paper, we present a methodology and application-FCM Constructor-to systematically acquire design knowledge from domain experts, and to construct a corresponding BBN. To show the system's usability, we use three realistic product design cases to compare BBNs that are directly generated by domain experts, with BBNs that are generated using the FCM Constructor. We find that the BBN constructed through the FCM Constructor is similar, based on reasoning results, to the BBN constructed directly by specifying conditional probability tables of BBNs.