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)


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.


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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  1. 1.
    Balabanovic, M., Shoham, Y.: Content-based, collaborative recommendation. Communications of the ACM 40(3), (1997)Google Scholar
  2. 2.
    Brin, S., Page, L.: The Anatomy of a Large-Scale Hypertextual Web Search Engine. In: Proc. of 7th International World Wide Web Conference:WWW7 Conference (1998)Google Scholar
  3. 3.
    Callan, J., Connell, M.: Query-based sampling of text databases. ACM Transactions on Information Systems 19(2), 97–130 (2001)CrossRefGoogle Scholar
  4. 4.
    Callan, J., Connell, M., Du, A.: Automatic discovery of language models for text databases. In: ACM SIGMOD, pp. 479–490 (1999)Google Scholar
  5. 5.
    Clarke, I., Sandberg, O., Wiley, B., Hong, T.W.: Freenet: A distributed anonymous information storage and retrieval system. Designing Privacy Enhancing Technologies: International Workshop on Design Issues in Anonymity and Unobservability (2001),
  6. 6.
    Deerwester, S., Dumais, S.T., Furnas, G.W., Landauer, T.K., Harshman, R.: Indexing by latent semantic analysis. Journal of the American Society of Information Science (1990)Google Scholar
  7. 7.
    Gallardo-Antolin, A., et al.: I-Gaia: an information processing layer for the diet platform. In: In the first international joint conference on Autonomous Agents and Multi Agent Systems (AAMAS), vol. 7, pp. 1272–1279 (2002)Google Scholar
  8. 8.
    Gnutella (2000),
  9. 9.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35, 61–70 (1992)CrossRefGoogle Scholar
  10. 10.
    Good, N., Schafer, J.B., Konstan, J.A., Borchers, A., Sarwar, B.M., Herlocker, J.L., Riedl, J.: Combining collaborative filtering with personal agents for better recommendations. In: AAAI/IAAI, pp. 439–446 (1999)Google Scholar
  11. 11.
    Herlocker, J.L., Konstan, J.A., Borchers, A., Riedl, J.: An algorithmic framework for performing collaborative filtering. In: SIGIR 1999, pp. 230–237 (1999)Google Scholar
  12. 12.
    Kanfer, A., Sweet, J., Schlosser, A.: Humanizing the net: Social navigation with a ”know-who” email agent. In: The 3rd Conference on Human Factors & The Web (1997),
  13. 13.
    Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. Journal of the ACM 46, 604–632 (1999)zbMATHCrossRefMathSciNetGoogle Scholar
  14. 14.
    Lang, K.: NewsWeeder: learning to filter netnews. In: Proceedings of the 12th International Conference on Machine Learning, pp. 331–339. Morgan Kaufmann publishers Inc, San Mateo (1995)Google Scholar
  15. 15.
    Melville, P., Mooney, R.J., Nagarajan, R.: Content-boosted collaborative filtering. In: SIGIR-2001 Workshop on Recommender Systems (2001)Google Scholar
  16. 16.
    Mine, T., Matsuno, D., Takaki, K., Amamiya, M.: Agent community based peer-to-peer information retrieval. In: The third international joint conference on Autonomous Agents and Multi Agent Systems (AAMAS), vol. 7 (2004); posterGoogle Scholar
  17. 17.
    Napster (2000),
  18. 18.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J.: Grouplens: Open architecture for collaborative filtering of netnews (1994)Google Scholar
  19. 19.
    Ratnasamy, S., Francis, P., Handley, M., Karp, R., Shenker, S.: A scalable contentaddressable network. SIGCOMM, 161–172 (2001)Google Scholar
  20. 20.
    Robertson, S.E., Walker, S.: Some simple effective approximations to the 2- poisson model for probabilistic weighted retrieval. In: Proceedings of the 17 Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (1994)Google Scholar
  21. 21.
    Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. WWW 10, 285–295 (2001)Google Scholar
  22. 22.
    Schafer, J.B., Konstan, J.A., Riedi, J.: Recommender systems in e-commerce. In: ACM Conference on Electronic Commerce, pp. 158–166 (1999)Google Scholar
  23. 23.
    Shardanand, U., Maes, P.: Social information filtering: Algorithms for automating “word of mouth”. In: Proceedings of ACM CHI 1995 Conference on Human Factors in Computing Systems, vol. 1, pp. 210–217 (1995)Google Scholar
  24. 24.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. In: Proceedings of the 2001 conference on applications, technologies, architectures, and protocols for computer communications, pp. 149–160 (2001)Google Scholar
  25. 25.
    Tang, C., Xu, Z., Dwarkadas, S.: Peer-to-peer information retrieval using selforganizing semantic overlay networks. SIGCOMM (2003)Google Scholar
  26. 26.
  27. 27.
    Yang, B., Garcia-Molina, H.: Designing a super-peer network. In: IEEE International Conference on Data Engineering, vol. 3 (2003)Google Scholar
  28. 28.
    Yimam-Seid, D., Kobsa, A.: Expert finding systems for organizations: Problem and domain analysis and the demoir approach. Journal of Organizational Computing and Electronic Commerce 13(1), 1–24 (2003)CrossRefGoogle Scholar
  29. 29.
    Zhong, G., Amamiya, S., Takahashi, K., Mine, T., Amamiya, M.: The design and application of kodama system. IEICE Transactions INF.& SYST., E85-D 4(04), 637–646 (2002)Google Scholar

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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