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

0957-4174/$ see front matter 2011 Elsevier Ltd. A doi:10.1016/j.eswa.2011.06.032 ⇑ Corresponding author. Tel.: +82 62 530 3436; fax E-mail addresses: [email protected] (W. edu (Y.S. Kim), [email protected] (K.-Y. Kim), hjy 1 Tel.: +60 6 252 4113; fax: +60 6 231 8840. 2 Tel.: +1 313 577 5791; fax: +1 313 577 8833. 3 Tel.: +1 313 5774396; fax: +1 313 577 8833. 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. 2011 Elsevier Ltd. All rights reserved.