Download Agents and Peer-to-Peer Computing: 4th International by Thanasis G. Papaioannou, George D. Stamoulis (auth.), Zoran PDF

By Thanasis G. Papaioannou, George D. Stamoulis (auth.), Zoran Despotovic, Sam Joseph, Claudio Sartori (eds.)

ISBN-10: 3540490256

ISBN-13: 9783540490258

This booklet constitutes the completely refereed post-proceedings of the 4th foreign Workshop on brokers and Peer-to-Peer Computing, AP2PC 2005, held in Utrecht, Netherlands, on July twenty fifth, 2005, within the context of the 4th overseas Joint convention on self sufficient brokers and Multi-Agent structures, AAMAS 2005.

The thirteen revised complete papers offered have been rigorously reviewed and chosen from 27 submissions; they're absolutely revised to include reviewers' reviews and discussions on the workshop. the amount is prepared in topical sections on belief and attractiveness, P2P infrastructure, semantic infrastructure, in addition to group and cellular applications.

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Additional resources for Agents and Peer-to-Peer Computing: 4th International Workshop, AP2PC 2005, Utrecht, The Netherlands, July 25, 2005. Revised Papers

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The manager N3 updates its repository using the received message. The file of N10 is a newly appeared one because no entry matches its two identifiers, K3 and F10 . Thus, N3 adds a new row with reputation value (0,0) and file owner N10 . Whereas, the file of N20 with K3 and F6 already exists in the repository. Its reputation is positive 30, negative 2 and the other peer N7 also has the identical file. In this case, N3 just adds N20 to the list of owner. 2 Query and Response In this phase, a peer sends a search query to find a desired file and receives a filtered response from the manager.

This is the case if both Y and Z are strategic entities. 2, normative entities always disrecommend. This raises the question how a strategic Y would decide depending on its type belief 5 (t) The illustration is based on p(n) (γ) = 30%, p(u) (γ) = 5% and the prior belief pX (NZ ) = 50%. A New View on Normativeness in Distributed Reputation Systems (t) 27 (t) pY (NZ ) regarding Z. The probability pY (NZ ) provides a lower bound of the proba(t) i bility that Z decides to disrecommend. Hence, we derive pY (NZ ) > 2c b f as a sufficient condition that a strategic Y always disrecommends.

We do not claim that these costs are prohibitive. Rather, we argue that system design should make use of these tampering costs, even if they are small. This means that system design could foresee some behavior that is not fully incentive compatible. As a result, complying with the system design also incurs some costs (compliance costs). It is clear that entities are tampered whenever these compliance costs exceed the tampering costs. For this purpose, system design has to keep the compliance costs as marginal as possible.

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