Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wroc?aw, Poland, October 2009, Proceedings

Computational Collective Intelligence: Semantic Web, Social Networks and Multiagent Systems: First International Conference, ICCCI 2009, Wroc?aw, Poland, October 2009, Proceedings

Ryszard Kowalczyk, Shyi-Ming Chen, Ngoc Thanh Nguyen

Language: English

Pages: 875

ISBN: 2:00069536

Format: PDF / Kindle (mobi) / ePub


This book constitutes the proceedings of the First International Conference on Computational Collective Intelligence, ICCCI 2009, held in Wroclaw, Poland, in October 2009. The 71 papers presented in this volume together with 3 keynote speeches were carefully reviewed and selected from 212 submissions. The papers are organized in topical sections on collective decision making, multiagent systems, social networks, semantic web, ontology management, dynamics of real-world social networks, nature-inspired collective intelligence, web systems analysis, collective intelligence for economic data analysis.

 

 

 

 

 

 

 

 

 

 

 

example represent the preference model of the jury, it can be used to evaluate new students. For example, student S9 who is “medium” in Mathematics and Physics and “good” in Literature, would be evaluated as “medium” because his profile matches the premise of rule 2), having as consequence an overall evaluation at least “medium”. The overall evaluation of S9 cannot be “good”, because his profile does not match any rule having as consequence an overall evaluation “good” (in the considered example,

[E11 ], BPnd [E7 ]) −1 y · BPnd [Ei ] · GE(Ei ) , i = (7 | 11) i=1 At this stage we are able to compute the average effort (AE d ) of all the users z who previously have surfed the domain d. Formally: z AE d = 1 (E d ) z n=1 n Finally, we can compute the ranking of web-sites by taking into account the average effort on each domain. We adopt a sigmoid function to assign almost 70 L. Luca, B. Stephen, and D. Pierpaolo null importance to those websites that have not been endorsed/viewed by

participate actively in content creation and knowledge sharing through a variety of shared interests, and leading to the unanticipated explosion of innovative ideas. However, the current state of the collaborative knowledge creation and sharing systems appears to be less successful at enabling N.T. Nguyen, R. Kowalczyk, and S.-M. Chen (Eds.): ICCCI 2009, LNAI 5796, pp. 75–86, 2009. © Springer-Verlag Berlin Heidelberg 2009 76 K. Maleewong, C. Anutariya, and V. Wuwongse community deliberation

to alter the ants’ search width. When Ant k at Location p is to select an unscheduled job j, the algorithm will also generate a variable q between 0 and 1; if q≤ q0[k] (the value of q0[k] is between 0 and 1; each q0[k] is usually set identical at the time the algorithm starts), Job j(j∈s) with the maximum value of τ pj ⋅ η pjβ is selected from the unscheduled jobs, s. On the other hand, equation (1) of Algorithm 2 will be used to calculate the probability of selecting each job; Job j is then

not fly, so the interactions between players who are separated considerably, it is null. For example, in Figure 1, players a1 and c2 does not interact between them, however, some players have key roles in a team according to their position, for example in Fig. 1, player b2 interacts with the four neighbors of zone D and with the three neighbors of the attack zone. Figure 2 shows an example of a 4:3:3 structure according to the relations described previosly. In order to assign a value to each

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