By Victor Lesser (auth.), Longbing Cao, Yifeng Zeng, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Philip S. Yu, Munindar P Singh (eds.)
This booklet constitutes the completely refereed post-workshop complaints of the eighth foreign Workshop on brokers and knowledge Mining interplay, ADMI 2012, held in Valencia, Spain, in June 2012. The sixteen revised complete papers have been rigorously reviewed and chosen from a variety of submissions. The papers are equipped in topical sections on brokers for info mining, info mining for brokers, and agent mining applications.
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Additional resources for Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers
Parameters of the Model Ni bi vi j αi j P(αi j , bi j ) The set of peer i’s neighbors. The vector (bi0 , . . , bi Ni ), (bi j denotes the bandwidth peer i assigned to j). The value to peer i of a contribution from peer j to peer i. The fraction of the total pieces that peer i can provide to peer j. The probability of peer i receiving a contribution from j, having made a bandwidth contribution of bi j while it has αi j to provide to j. determines the choice. We denote by P(αi j , bi j ) the probability of a peer i receiving a contribution from j to which it has made a bandwidth contribution of bi j while it has an αi j fraction of the pieces to provide.
We assume that the probability of reciprocation is a monotonically increasing function in both αi j and bi j and greater than zero (zero when a peer owns the entire file). The logic here is that contributing more bandwidth results in a higher chance of reciprocation and since the more pieces a peer can provide the more likely it is to have a rare piece, a higher α also results in a higher chance of reciprocation. Table 1 summarizes the parameters of our model. A strategy of a peer i in our game theoretical model is equivalent to the vector bi .
Likewise, the request getGroup(grpType) returns list of groups in the MAS with speciﬁed type grpType. 34 5 O. Kaz´ık and R. Neruda Conclusions In order to support the real-world data-mining processes, the models of computational group, data group, search group, and pre-processing agent have been included. The computational agent can stand for whole computational group and realize various ensemble methods. The meta-learning scenario as well as external learning can be described and implemented in this model by means of general search schema.