Agents and Peer-to-Peer Computing: Second International by Beng Chin Ooi, Wee Siong Ng, Kian-Lee Tan, AoYing Zhou

By Beng Chin Ooi, Wee Siong Ng, Kian-Lee Tan, AoYing Zhou (auth.), Gianluca Moro, Claudio Sartori, Munindar P. Singh (eds.)

Peer-to-peer (P2P) computing is at present attracting huge, immense public recognition, spurred via the recognition of file-sharing platforms similar to Napster, Gnutella, Morpheus, Kaza, and several other others. In P2P structures, a truly huge variety of independent computing nodes, the friends, depend on one another for companies. P2P networks are rising as a brand new disbursed computing paradigm due to their strength to harness the computing strength and the garage means of the hosts composing the community, and since they notice a totally open decentralized setting the place everyone can take part autonomously. even if researchers engaged on allotted computing, multiagent platforms, databases, and networks were utilizing related recommendations for a very long time, it's only lately that papers encouraged via the present P2P paradigm have begun showing in prime quality meetings and workshops. specifically, examine on agent platforms seems to be such a lot correct simply because multiagent platforms have continually been considered networks of independent friends due to the fact their inception. brokers, which are superimposed at the P2P structure, include the outline of job environments, decision-support services, social behaviors, belief and recognition, and interplay protocols between friends. The emphasis on decentralization, autonomy, ease, and pace of development that provides P2P its benefits additionally results in major capability difficulties. so much admired between those are coordination – the power of an agent to make judgements by itself activities within the context of actions of alternative brokers, and scalability – the price of the P2P structures in how good they self-organize as a way to scale alongside numerous dimensions, together with complexity, heterogeneity of colleagues, robustness, site visitors redistribution, etc.

This booklet brings jointly an creation, 3 invited articles, and revised models of the papers awarded on the moment overseas Workshop on brokers and Peer-to-Peer Computing, AP2PC 2003, held in Melbourne, Australia, July 2003.

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Extra resources for Agents and Peer-to-Peer Computing: Second International Workshop, AP2PC 2003, Melbourne, Australia, July 14, 2003, Revised and Invited Papers

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They rely on trust and privacy, and these are only partly enhanced by technological solutions. They are further exposed to manipulative behavior, for which solutions may be too complex. And, at the computational level, they introduce both communication and computation overheads in terms of location protocols, caching requirements and security mechanisms. Thus, although P2P systems, and even more so multi-agent systems, present a shift of paradigm in system architecture, and although several advantages of P2P systems place them by far ahead of competing systems, their success as a generic solution for system design depends, to a large extent, on the success of the P2P and Agents research communities to overcomes P2P weaknesses and increase its robustness.

The 32 Y. Wang and J. Vassileva Successful interactions(%) Trust and reputation system with BN Trust and reputation system without BN Trust system with BN Trust system without BN 90 80 70 60 50 40 30 20 10 0 100 200 300 400 500 600 700 800 900 1000 The number of interactions Fig. 3. The comparison of four systems interactions between the same seller and the same buyer are not repeatable. The buyer’s trust in a seller is only based on one direct interaction. The seller’s reputation is mostly built on the buyers’ having a single experience with the seller.

The agent might receive several such recommendations at the same time, which may come from the trustworthy acquaintances, untrustworthy acquaintances, or strangers. If the references are untrustworthy, the agent can discard their recommendations immediately. Then the agent needs to combine the recommendations from trustworthy references and from unknown references together to get the total recommendation for the file provider: rij = wt ∗ k l=1 tril ∗ tlj k l=1 tril + ws ∗ g z=1 tzj g , where wt + ws = 1 (6) 28 Y.

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