Agents and Peer-to-Peer Computing: 7th International by Sharmila Savarimuthu, Maryam Purvis, Martin K. Purvis

By Sharmila Savarimuthu, Maryam Purvis, Martin K. Purvis (auth.), Domenico Beneventano, Zoran Despotovic, Francesco Guerra, Sam Joseph, Gianluca Moro, Adrián Perreau de Pinninck (eds.)

This booklet constitutes the completely refereed post-workshop lawsuits of the seventh foreign Workshop on brokers and Peer-to-Peer Computing, AP2PC 2008, held in Estoril, Portugal, in could 2008 and the eighth foreign Workshop on brokers and Peer-to-Peer Computing, AP2PC 2009, held in Budapest, Hungary, might 2009, co-located with the overseas Joint convention on self sustaining brokers and Multi-Agent platforms, AAMAS. The thirteen revised complete papers offered have been conscientiously reviewed and chosen from various submissions. the amount is prepared in topical sections on social welfare, disbursed info sharing, and community association and efficiency.

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Extra info for Agents and Peer-to-Peer Computing: 7th International Workshop, AP2PC 2008, Estoril, Portugal, May 13, 2008 and 8th International Workshop, AP2PC 2009, Budapest, Hungary, May 11, 2009. Revised Selected Papers

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An agent may receive few or no responses, or responses of poor quality. Figure 1(b) depicts that, under philanthropy, over 20 percent of the agents in the network obtain low relative performance compared to other agents. This is depicted by the spread of the data points. In our simulation, relative performance ranges from -15 to +5. High negative values point to agents who are performing more work than they receive in the network. These agents are primarily those with high expertise values or high sociability values.

Figure 1(b) shows that Reciprocity and Incentives result in a fair network. The range of the relative performance of the agents in the network is closer together on the vertical axis. The difference between the fairness values of both settings are very small. This is because both Reciprocity and Incentives control agent interaction and this enforces fairness. With Reciprocity, an agent only responds to those agents that have been helpful to it in the past and the responses given are usually good.

That is, their relative performances are not widely distributed, which means each agent obtains a relative performance that is close to zero. An unfair network means that some agents are being exploited—they are the ones who do more work than they receive. Under Philanthropy, agents may not receive sufficient help from the others. An agent may receive few or no responses, or responses of poor quality. Figure 1(b) depicts that, under philanthropy, over 20 percent of the agents in the network obtain low relative performance compared to other agents.

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