Notice of Violation of IEEE Publication PrinciplesReinforcement learning for P2P searching

Notice of Violation of IEEE Publication Principles

"Reinforcement Learning for P2P Searching,"
By L. Gatani, G. Lo Re, A. Urso, S. Gaglio.,
in the Proceedings of the Seventh International Workshop on Computer Architecture for Machine Perception, 2005. CAMP 2005. pp. 303-308, 4-6 July 2005

After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE\´s Publication Principles.

This paper contains substantial duplication of original text from the paper cited below. The original text was copied without attribution (including appropriate references to the original author(s) and/or paper title) and without permission.

Due to the nature of this violation, reasonable effort should be made to remove all past references to this paper, and future references should be made to the following article:

6S: "Distributing Crawling and Searching Across Web Peers"
By Filippo Menczer, Ruj Akavipat and Le-Shin Wu
available at the following URL: http://www.informatics.indiana.edu/research/publications/6S.pdfFor a peer-to-peer (P2P) system holding a massive amount of data, an efficient and scalable search for resource sharing is a key determinant to its practical usage. Unstructured P2P networks avoid the limitations of centralized systems and the drawbacks of a highly structured approach, because they impose few constraints on topology and data placement, and they support highly versatile search mechanisms. However their search algorithms are usually based on simple flooding schemes, showing severe inefficiencies. In this paper, to address this major limitation, we propose and evaluate the adoption of a local adaptive routing protocol. The routing algorithm adopts a simple reinforcement learning scheme (driven by query interactions among neighbors), in order to dynamically adapt the topology to peer interests. Preliminary evaluati- ons show that the approach is able to dynamically group peer nodes in clusters containing peers with shared interests and organized into a small world network.