Computational Collective Intelligence: Semantic Web, Social by Ryszard Kowalczyk, Shyi-Ming Chen, Ngoc Thanh Nguyen

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 offered during this quantity including three keynote speeches have been conscientiously reviewed and chosen from 212 submissions. The papers are geared up in topical sections on collective selection making, multiagent platforms, social networks, semantic internet, ontology administration, dynamics of real-world social networks, nature-inspired collective intelligence, net platforms research, collective intelligence for financial facts 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

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Knowledge Management in Theory and Practice. : Engines of Logic: Mathematicians and the Origin of the Computer. : La Querelle des universaux, De Platon à la fin du Moyen-Age. : A Study on Community Formation in Collaborative Tagging Systems. : Augmenting Human Intellect, Technical Report. : Neural Darwinism. : The Printing Revolution in Early Modern Europe. : The semantic Web in Action. Scientific American, pp. 90–97 (December 2007) [21] Christiane, F. ): Preface by George Miller, Wordnet, An Electronic Lexical Database.

30 P. 1 IEML Primitives IEML is made of a small number of building blocks, six primitive symbols, that are combined and recombined in a regular way in successive layers of composition. The detail of this syntactic combination process will not be explained here. I will just expound the meaning of the six primitive symbols in order to prepare the reading of Figure 2, that shows the simplest possible model of collective intelligence in IEML. First, in any expression of signification we must be able to distinguish between something (whatever it may be) and nothing, because some syntactic roles must sometimes be left unplayed.

Polkowski, L. ) Transactions on Rough Sets II. LNCS, vol. 3135, pp. 319–342. Springer, Heidelberg (2004) 14. : Fuzzy rough sets and multiple-premise gradual decision rules. International Journal of Approximate Reasoning 41, 179–211 (2006) 15. : A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis, C. ) Operational Tools in the Management of Financial Risks, pp. 121–136. Kluwer Academic Publishers, Dordrecht (1998) 16. : The use of rough sets and fuzzy sets in MCDM. , Hanne, T.

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