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A survey of soft modeling approaches for collaborative networks

A large number of aspects in collaborative networks are difficult to capture with traditional modeling approaches due to the inherent imprecision and incompleteness of information. Soft modeling approaches are specifically developed to handle such cases and thus have a high potential to the establishment of more effective and close to reality models. Computational intelligence methods are complemented with other approaches such as qualitative reasoning, complexity theories, chaos theory, etc.

Keywords

Fuzzy Logic Soft Computing Causal Modeling Chaos Theory Collaborative Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Akkok, N. (1998). The causal modeling technique. Unpublished Thesis for the degree of Cand. Scient. in informatics, Institute of Informatics, University of Oslo.Google Scholar
  2. Allen, J. F. (1983). Maintaining Knowledge about Temporal Intervals, Communications ofGoogle Scholar
  3. Berthold, M. R. (2003). Fuzzy Logic. In D. J. Hand (Ed.), Intelligent Data Analysis: An Introdution (Second ed.): Springer.Google Scholar
  4. BISC. (2006). Berkeley Initiative in Soft Computing. Retrieved May 2nd, 2006, from the World Wide Web: http://www-bisc.cs.berkeley.edu/bisc/bisc.memo.html#what_is_sc
  5. Bonissone, P. P. (1997). Soft computing: the convergence of emerging reasoning technologies. Soft Computing - A Fusion of Foundations, Methodologies and Applications, 1(1), 6-18.CrossRefMathSciNetGoogle Scholar
  6. Bonissone, P. P., Chen, Y.-T., Goebel, K., & Khedkar, P. S. (1999). Hybrid soft computing systems: industrial and commercial applications. Proceedings of the IEEE, 87(9), 1641-1667.CrossRefGoogle Scholar
  7. Bredeweg, B., & Salles, P. (2005). The Ants'Garden: Complex Interactions between Populations and the Scalability of Qualitative Models. AI Communications, Vol. 18(4), 305-317.MATHMathSciNetGoogle Scholar
  8. Bustard, D. W., He, Z., & Wilkie, F. G. (1999). Soft Systems and Use-Case Modelling: Mutually Supportive or Mutually Exclusive? Thirty-Second Annual Hawaii International Conference on System Sciences, Vol. 3, pp. 3055.Google Scholar
  9. Checkland, P. (1981). Systems Thinking, Systems Practice: John Wiley & Sons, Chichester.Google Scholar
  10. Checkland, P. (2000). Soft systems methodology: a thirty year retrospective. Systems Research and Behavioral Science, 17(S1), S11-S58.CrossRefGoogle Scholar
  11. Dubois, Didier and Prade, Henri, "Possibility Theory, Probability Theory and Multiple-valued Logics: A Clarification", Annals of Mathematics and Artificial Intelligence 32:35-66, 2001.CrossRefMathSciNetGoogle Scholar
  12. Eva, M. (2004). Soft systems methodology. ACCA Global. Retrieved September, 2006, from the World Wide Web: http://www.accaglobal.com/publications/studentaccountant/1073535
  13. Finegan, A. (1994). Soft Systems Methodology: An Alternative Approach to Knowledge Elicitation in Complex and Poorly Defined Systems. Complexity International. Vol. 1. Retrieved, 2006, from the World Wide Web: http://journal-ci.csse.monash.edu.au/ci/vol01/finega01/html/
  14. French, S. (2003). Soft Modelling and Problem Formulation. Manchester Business School, The University of Manchester. Retrieved, 2006, from the World Wide Web: http://www.sal.hut.fi/TED/slides/Soft_modelling.pdf
  15. Greenland, S., & Brumback, B. (2002). An overview of relations among causal modeling methods. In international journal of epidemiology., ISBN: 31-1030-1037.Google Scholar
  16. Harel, D. (1987). Statecharts: A Visual Formalism for Complex Systems. In Science of Computer Programming, Vol. 8, 231-274.MATHCrossRefMathSciNetGoogle Scholar
  17. Hassanien, A. E., "Rough Set Approach for Attribute Reduction and Rule Generation: A Case of Patients With Suspected Breast Cancer", Journal of the American Society for information Science and Technology, 55(11):954-962, 2004CrossRefGoogle Scholar
  18. Holland, John. "Genetic algorithms." Scientific American, July 1992, p. 66-72.Google Scholar
  19. Huibers, T.W.C., Lalmas, M., Rijsbergen, C. J. (1996). Information Retrieval and Situation Theory. ACM SIGIR Forum, Volume 30 , Issue 1.Google Scholar
  20. Kellert, S.H. (1993). In the wake of chaos: Unpredictable order in dynamical systems. Chicago: The University of Chicago Press.MATHGoogle Scholar
  21. Klir, G., & Folger, T. (1988). Fuzzy Sets, Uncertainty, and Information.Google Scholar
  22. Krogmann, U., (1997). Techniques for computational and machine intelligence – Soft Computing. In Advances in Soft-Computing Technologies and Application in Mission Systems, AGARD Lecture Series 210.Google Scholar
  23. Marczyk, A. - Genetic Algorithms and Evolutionary Computation, 2004, http://www.talkorigins.org/faqs/genalg/genalg.html
  24. Mitchell, M. - An introduction to genetic algorithms, MIT Press, 1996.Google Scholar
  25. Odell, J. J. (1996). A Primer to Method Engineering. In R. J. Welke (Ed.), Method Engineering - Principles of Method Construction and Tool Support, proceedings of the IFIP TC8 WG8.1 Working Conference on Method Engineering: Chapman & Hall.Google Scholar
  26. Olle, T. W., Hagelstein, H., Macdonald, I. G., Rolland, C., Sol, H. G., Van Assche, F. J. M., & Verrijn-Stuart, A. A. (1991). Information Systems Methodologies - A Framework for Understanding (2nd ed.): IFIP. Addison-Wesley.Google Scholar
  27. Pawlak, Z., “Rough sets,” International Jornal of Computer and Information Sciences, pp. 341-356, 1982.Google Scholar
  28. Rae, G. (2006). Chaos Theory: A Brief Introduction. Retrieved, from the World Wide Web: http://www.imho.com/grae/chaos/chaos.html
  29. Rasmy, M. H., Tharwat, A., & Ashraf, S. (2005). Enterprise Resource Planning (ERP) Implementation in the Egyptian Organizational Context. Retrieved, 2006, from the World Wide Web: http://uxisweb1.brunel.ac.uk/iseingsites/EMCIS/EMCIS2005/pdfs/21.pdf
  30. Roelen, A. L. C., Bellamy, L. J., Hale, A. R., van Paassen, M. M., & Molemaker, R. J. (2000). Feasibility of the development of a causal model for the assessment of third party risk around airports. NLR, Amsterdam: CR-2000-189PT-2.Google Scholar
  31. Rose, J. (1997). Soft systems methodology as a social science research tool. Systems Research and Behavioral Science, 14(4), 249-258.CrossRefGoogle Scholar
  32. Sanches, A. L., Pamplona, E. d. O., & Montevechi, J. A. B. (2005). Capital Budgeting Using Triangular Fuzzy Numbers. V Encuentro Internacional de Finanzas. Santiago, Chile, 19 a 21 de Janeiro. Retrieved, from the World Wide Web: http://www.iem.efei.br/edson/download/ArtAlexFuzzyChile05.pdf
  33. Shafer, Glenn (1976). A Mathematical Theory of Evidence. Princeton University Press.Google Scholar
  34. SGZZ. (2006). Soft Modelling. Retrieved September, 2006, from the World Wide Web: http://www.sgzz.ch/?Systems_Thinking_Practice:Soft_Modelling
  35. Silipo, R. (2003). Neural Networks. In D. J. Hand (Ed.), Intelligent Data Analysis: An Introdution (Second ed.): Springer.Google Scholar
  36. Sørensen, L., & Vidal, R. V. V. (2002). The Anatomy of Soft Approaches. Economic Analysis Working Papers, 1(08).Google Scholar
  37. Tin, E., Akman, V. (1994). Computational Situation Theory. SIGART Bulletin, Vol. 5, No. 4Google Scholar
  38. Wang, P. P. (2000). Soft modeling for a certain class of intelligent and complex systems. Information Sciences, 123(1-2), 149-159.MATHCrossRefGoogle Scholar
  39. Wilson, B. (1984). Systems: Concepts, Methodologies, and Applications: John Wiley & Sons, Brisbane.Google Scholar
  40. Zadeh, L. A. (1994). Soft computing and fuzzy logic. Software, IEEE, 11(6), 48-56.CrossRefGoogle Scholar
  41. Zadeh, Lotfi, "Fuzzy Sets as the Basis for a Theory of Possibility", Fuzzy Sets and Systems 1:3-28, 1978.MATHCrossRefMathSciNetGoogle Scholar

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