Multi-agent Based Personal File Management Using Case Based Reasoning

  • Xiaolong Jin
  • Jianmin Jiang
  • Geyong Min
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5572)


Computer users have been facing a progressively serious problem, namely, how to efficiently manage computer files so as to not only facilitate themselves to use the files, but also save the scare storage resource. Although there are a lot of file management systems available so far, none of them, to the best of our knowledge, can automatically address the deletion/preservation problem of files. To fill this gap, this study explores the value of artificial intelligence techniques in file management. Specifically, this paper develops an intelligent agent based personal file management system, where Case Based Reasoning (CBR) is employed to guide file deletion and preservation. Through some practical experiments, we validate the effectiveness and efficiency of the developed file management system.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Carlton, G.H.: A critical evaluation of the treatment of deleted files in microsoft windows operation systems. In: Proceedings of the 38th Hawaii International Conference on System Sciences (HICSS 2005), pp. 1–8 (2005)Google Scholar
  2. 2.
    You, L.L., Pollack, K.T., Long, D.D.E.: Deep store: An archival storage system architecture. In: Proceedings of the 21st International Conference on Data Engineering (ICDE 2005), pp. 804–815 (2005)Google Scholar
  3. 3.
    Chmiel, K., Gawinecki, M., Kaczmarek, P., Szymczak, M., Paprzycki, M.: Efficiency of JADE agent platform. Scientific Programming 13(2), 159–172 (2005)CrossRefGoogle Scholar
  4. 4.
    Zambonelli, F., Omicini, A.: Challenges and research directions in agent-oriented software engineering. Autonomous Agents and Multi-Agent Systems 9(3), 253–283 (2004)CrossRefGoogle Scholar
  5. 5.
    Luck, M., McBurney, P., Shehory, O., Willmott, S.: Agent Technology: Computing as Interaction (A Roadmap for Agent Based Computing). AgentLink (2005)Google Scholar
  6. 6.
    Craw, S., Wiratunga, N., Rowe, R.C.: Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence 170(16-17), 1175–1192 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Khoshgoftaar, T.M., Seliya, N., Sundaresh, N.: An empirical study of predicting software faults with case-based reasoning. Software Quality Journal 14(2), 85–111 (2006)CrossRefGoogle Scholar
  8. 8.
    Iglesias, R., Ares, F., Ferneindez-Delgado, M., Rodriguei, J.A., Bregains, J., Barrol, S.: Element failure detection in linear antenna arrays using case-based reasoning. IEEE Antennas and Propagation Magazine 50(4), 198–204 (2008)CrossRefGoogle Scholar
  9. 9.
    Pous, C., Caballero, D., Lopez, B.: Diagnosing patients combining principal components analysis and case based reasoning. In: Proceedings of the 8th International Conference on Hybrid Intelligent Systems (HIS 2008), pp. 819–824 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Xiaolong Jin
    • 1
  • Jianmin Jiang
    • 1
  • Geyong Min
    • 1
  1. 1.Digital Media and Systems Research Institute, School of InformaticsUniversity of BradfordBradfordUK

Personalised recommendations