By Victor Lesser (auth.), Longbing Cao, Yifeng Zeng, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Philip S. Yu, Munindar P Singh (eds.)
This ebook 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 conscientiously reviewed and chosen from quite a few submissions. The papers are prepared in topical sections on brokers for facts mining, information mining for brokers, and agent mining applications.
Read or Download Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers PDF
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Additional info for Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers
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 . Naturally the sum of peer i’s bandwidth contributions to its neighbors cannot exceed its upload capacity. Using the notation b−i to denote the strategy of peers other than i, we describe the expected utility of a peer i as follows: ui (bi , b−i ) = − bi + ∑ [P(αi j , bi j ) × vi j × bj ] (1) j∈Ni Equation 1 states that the expected utility of i is equal to the sum over all neighbors j of the probability P(αi j , bi j ) that i receives a contribution from j multiplied by the value that i assigns to receiving the contribution (vi j ) and the bandwidth at which it receives the contribution, minus its own contribution − bi .
Neruda Fig. 3. Left: Example of computational MAS conﬁguration with two computational agents controlled by single manager, which process the same data in parallel. Right: Example of computational MAS conﬁguration with a pre-processing agent. task manager who controls the computation. The pre-processing agent gains properties of both the data source (it provides data) and computational agent (it receives data from another source and waits for control messages). g. computational agent) as a data source in data group.
Keywords: MAS, role-based models, data-mining, computational intelligence, description logic, matchmaking, closed-world assumption. 1 Introduction An agent is a computer system situated in an environment that is capable of autonomous action in this environment in order to meet its design objectives . Its important features are adaptivity to changes in the environment and collaboration with other agents. Interacting agents join in more complex societies, multi-agent systems (MAS). The eﬀort to reuse MAS patterns brings the need of separation of the interaction logic from the inner algorithmic logic of an agent.
Agents and Data Mining Interaction: 8th International Workshop, ADMI 2012, Valencia, Spain, June 4-5, 2012, Revised Selected Papers by Victor Lesser (auth.), Longbing Cao, Yifeng Zeng, Andreas L. Symeonidis, Vladimir I. Gorodetsky, Philip S. Yu, Munindar P Singh (eds.)