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Design and Implementation of Agent Community Based Peer-to-Peer Information Retrieval Method

  • Tsunenori Mine
  • Daisuke Matsuno
  • Akihiro Kogo
  • Makoto Amamiya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3191)

Abstract

This paper presents an agent community based peer-to-peer information retrieval method called ACP2P method[16] and discusses the experimental results of the method. The ACP2P method uses agent communities to manage and look up information related to users. An agent works as a delegate of its user and searches for information that the user wants by communicating with other agents. The communication between agents is carried out in a peer-to-peer computing architecture. In order to retrieve information relevant to a user query, an agent uses a content file, which consists of retrieved documents and two histories : a query/retrieved document history(Q/RDH) and a query/sender agent history(Q/SAH). The former is a list of pairs of a query and the address of an agent that returned documents relevant to the query. The latter is a list of pairs of a query and the address of a sender agent and shows “who sent what query to the agent”. This is useful for finding a new information source. Making use of Q/SAH is expected to have a collaborative filtering effect, which gradually creates virtual agent communities, where agents with the same interests stay together. Our hypothesis is that a virtual agent community reduces communication loads necessary to perform a search. As an agent receives more queries, then more links to new knowledge are acquired. From this behavior, a “give and take”(or positive feedback) effect for agents seems to emerge. We implemented this method with Multi-Agent Kodama, and conducted experiments to test the hypothesis. The experimental results showed that the method employing two histories was much more efficient than a naive method employing ’multicast’ techniques only to look up a target agent. Further, making use of Q/SAH facilitates bidirectional communications between agents and thus creates virtual agent communities.

Keywords

Information Retrieval Target Agent Agent Community User Query Information Retrieval System 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Tsunenori Mine
    • 1
  • Daisuke Matsuno
    • 2
  • Akihiro Kogo
    • 2
  • Makoto Amamiya
    • 1
  1. 1.Faculty of Information Science and Electrical Engineering, Department of Intelligent SystemsKyushu UniversityFukuokaJapan
  2. 2.Graduate School of Information Science and Electrical Engineering, Department of Intelligent SystemsKyushu UniversityFukuokaJapan

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