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

Expert Systems with Applications

Volumn 38, Issue 12, 2011, Pages 15316-15331

  Cheah, Wooi Ping ^a   Kim, Yun Seon ^b   Kim, Kyoung Yun ^b   Yang, Hyung Jeong ^c  

^a MULTIMEDIA UNIVERSITY   (Malaysia)

^b Wayne State University   (United States)

^c Chonnam National University   (South Korea)

Author keywords

Bayesian belief network; Causal reasoning; Fuzzy cognitive map; Knowledge acquisition; Product design knowledge

Indexed keywords

BAYESIAN NETWORKS; COGNITIVE SYSTEMS; DECISION SUPPORT SYSTEMS; FUZZY RULES; KNOWLEDGE ACQUISITION; KNOWLEDGE BASED SYSTEMS; LARGE SCALE SYSTEMS;

CAUSAL REASONING; CONDITIONAL PROBABILITY TABLES; DESIGN DECISION SUPPORTS; DESIGN KNOWLEDGE; DOMAIN EXPERTS; FUZZY COGNITIVE MAP; PRODUCT DESIGN KNOWLEDGE;

PRODUCT DESIGN;

EID: 80052026365     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2011.06.032     Document Type: Article

References (37)

1. J. Aguilar A survey about fuzzy cognitive maps papers International Journal of Computational Cognition 3 2 2005 27 33
2. P.A. Alexander, D.L. Schallert, and V.C. Hare Coming to terms: How researchers in learning and literacy talk about knowledge Review of Educational Research 61 1991 315 343
3. J.R. Anderson Skill acquisition: Compilation of weak-method problem solutions Psychological Review 94 1987 192 210
4. I. Arana, H. Ahriz, and P. Fothergill Redesign knowledge analysis, representation and reuse Industrial knowledge management, a micro-level approach 2000 Springer-Verlag London pp. 139-146
5. R. Axelrod Structure of decision: The cognitive maps of political elites 1976 Princeton NJ
6. Blessing, L. T. (1996). Design process capture and support. In 2nd WDK workshop on product structuring, Delft (pp. 109-121).
7. Carvalho, J. P., Tomé, J. A. B. (1999). Rule-based fuzzy cognitive maps and fuzzy cognitive maps: a comparative study. In Proceedings of the international conference of the North American fuzzy information processing society (pp. 115-119).
8. W.P. Cheah, K.Y. Kim, H.J. Yang, M.S. Kim, and J.S. Kim Constructing manufacturing-environmental model in Bayesian belief networks for assembly design decision support through fuzzy cognitive map International Journal of Intelligent Information and Database Systems 3 1 2009 3 27
9. J. Conklin, and K.B. Yakemovic A process-oriented approach to design rationale Human-Computer Interaction 6,4 3 1991 357 393
10. R. Elio, and P.B. Scharf Modelling novice-to-expert shifts in problem solving strategy and knowledge organization Cognitive Science 14 1990 579 639
11. Georgopoulos, V. C., & Stylios, C. D. (2005). Augmented fuzzy cognitive maps supplemented with case base reasoning for advanced medical decision support, soft computing for information processing and analysis enhancing the power of the information technology. In M. Nikravesh, L. A. Zadeh, J. Kacprzyk (Eds.), Studies, fuzziness and soft computing (pp. 389-398).
12. A. Gopnik, and C. Glymour Causal maps and Bayes nets: A cognitive and computational account of theory-formation P. Carruthers, S. Stich, M. Siegal, The cognitive basis of science 2002 Cambridge University Press pp. 117-132
13. A. Gopnik, C. Glymour, D.M. Sobel, L.E. Schulz, T. Kushnir, and D. Danks A theory of causal learning in children: Causal maps and Bayes nets Psychological Review 111 1 2004 3 32
14. Greeno, J. G. (1980). Trends in the theory of knowledge for problem solving. In D. T. Tuma & F. Reif (Eds.), Problem solving and education (pp. 9-25). Illsdale, NJ: Erlbaum.
15. Horvitz, E., Breese, J., Heckerman, D., Hovel, D., & Rommelse, K. (1998). The Lumiere project: Bayesian user modeling for inferring the goals and needs of software users. In Proceedings of the conference of the uncertainty in artificial intelligence (pp. 256-265).
16. Hugin Expert: Accessed 12.01.2009.
17. I. Kang, S. Lee, and J. Choi Using fuzzy cognitive map for the relationship management in airline service Expert Systems with Applications 26 4 2004 545 555
18. M.S. Khan Group decision support using fuzzy cognitive maps for causal reasoning Group Decision and Negotiation 13 5 2004 463 480
19. K.B. Korb, and A.E. Nicholson Bayesian artificial intelligence 2004 Chapman and Hall/CRC
20. B. Kosko Fuzzy cognitive maps International Journal of Man-Machine Studies 24 1 1986 65 75
21. Kosko, B. (1988). Hidden patterns in combined and adaptive knowledge networks. In Proceedings of the IEEE international conference on neural networks (pp. 377-393).
22. B. Kosko Neural networks and fuzzy systems: A dynamical systems approach to machine intelligence 1992 Prentice-Hall Englewood Cliffs, NJ
23. Kosko, B. (1995). Combining fuzzy systems. In Proceedings of the IEEE international conference on fuzzy systems (pp. 1855-1863).
24. B. Kosko Fuzzy engineering 1997 Prentice-Hall Englewood Cliffs, NJ
25. Kunz, W., & Rittel, H. W. J. (1970). Issues as elements of information systems. Working paper 131, Center for Planning and Development Research, Berkeley.
26. P. Langley, H.A. Simon, G.L. Bradshaw, and J.M. Zytkow Scientific discovery. Computational explorations of the creative processes 1987 The MIT Press Cambridge, MA
27. K.C. Lee, and H.S. Kim A fuzzy cognitive map-based bi-directional inference mechanism: An application to stock investment analysis Intelligent Systems in Accounting Finance and Management 6 1 1997 41 57
28. Z.Q. Liu Causation, Bayesian networks and cognitive maps ACTA Automatica Sinica 27 4 2001 552 566
29. Z.Q. Liu, and R. Satur Contextual fuzzy cognitive map for decision support in geographic information systems IEEE Transactions on Fuzzy Systems 7 5 1999 495 507
30. W. Long Medical diagnosis using a probabilistic causal network Applied Artificial Intelligence 3 2-3 1989 367 383
31. R.E. Mayer Educational psychology: A cognitive approach 1987 Little Brown, Boston
32. S. Nadkarni, and P. Shenoy A Bayesian network approach to making inferences in causal maps European Journal of Operational Research 128 3 2001 479 498
33. S. Nadkarni, and P. Shenoy A causal mapping approach to constructing Bayesian networks Decision Support Systems 2 2004 259 281
34. Norsys: http://www.norsys.com/netica.html (Accessed 12.01.2009).
35. H.A. Simon Structure of Ill structured problems Artificial Intelligence 4 1973 181 201
36. Skaanning, C., Jensen, F., & Kjaerulff, U. (2000). Printer Troubleshooting using Bayesian networks. In R. Loganantharaj & G. Palm (Eds.), Intelligent problem solving, methodologies and approaches, LNCS (Vol. 1821, pp. 367-379).
37. A. Tang, A. Nicholson, Y. Jin, and J. Han Using Bayesian belief networks for change impact analysis in architecture design Journal of Systems and Software 80 1 2007 127 148