By Ryszard Kowalczyk, Shyi-Ming Chen, Ngoc Thanh Nguyen
This e-book constitutes the complaints of the 1st overseas convention on Computational Collective Intelligence, ICCCI 2009, held in Wroclaw, Poland, in October 2009. The seventy one papers provided during this quantity including three keynote speeches have been conscientiously reviewed and chosen from 212 submissions. The papers are prepared in topical sections on collective selection making, multiagent platforms, social networks, semantic net, ontology administration, dynamics of real-world social networks, nature-inspired collective intelligence, internet structures research, collective intelligence for financial information research.
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Additional resources for Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wroc?aw, Poland, October 2009, Proceedings
Kluwer, Dordrecht (1991) 39. : Rough set approach to multi-attribute decision analysis. European Journal of Operational Research 72, 443–459 (1994) 40. : Loss functions for preference levels: regression with discrete ordered labels. In: Proc. of the IJCAI-2005 Multidisciplinary Workshop on Advances in Preference Handling (2005) 41. : Multicriteria Methodology for Decision Aiding. Kluwer Academic Publishers, Dordrecht (1996) 42. : Aide Multicrit`ere ` a la D´ecision: M´ethodes et Cas. Economica, Paris (1993) 43.
2 A Simple Model of Collective Intelligence in the Service of Human Development The hexagrams of Figure 2 represent the six poles of collective intelligence and should be read in the following manner. Starting from the top, each line of a hexagram symbolizes a “semantic primitive”: E, U, A, S, B, T. Full lines mean that the corresponding primitives are "ON" and broken lines that the corresponding primitives are "OFF". The diagram highlight the symmetry between two dialectics. Vertically, the virtual/actual binary dialectic juxtaposes and joins the two complementary triples: know-want-can/documents-persons-body.
Preference representation by means of conjoint measurement & decision rule model. , Vincke, P. ) Aiding Decisions with Multiple Criteria–Essays in Honor of Bernard Roy, pp. 263–313. Kluwer, Dordrecht (2002) 21. : Axiomatic characterization of a general utility function and its particular cases in terms of conjoint measurement and rough-set decision rules. European Journal of Operational Research 158, 271–292 (2004) 22. : Dominance-Based Rough Set Approach to Knowledge Discovery (I) - General Perspective (II) - Extensions and Applications.