Developing Machine Intelligence within P2P Networks Using a Distributed Associative Memory

  • Amiza Amir
  • Anang Hudaya M. Amin
  • Asad Khan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7070)


In this paper, we discuss machine intelligence for conducting routine tasks within the Internet. We demonstrate a technique, called the Distributed Associative Memory Tree (DASMET), to deal with multi-feature recognition in a peer-to-peer (P2P)-based system. Shared content in P2P-based system is predominantly multimedia files. Multi-feature is an appealing way to tackle pattern recognition in this domain. In our scheme, the information held at individual peers is integrated into a common knowledge base in a logical tree like structure and relies on the robustness of a well-designed structured P2P overlay to cope with dynamic networks. Additionally, we also incorporate a consistent and secure backup scheme to ensure its reliability. We compare our scheme to the Backpropagation network and the Radial Basis Function (RBF) network on two standard datasets, for comparative accuracy. We also show that our scheme is scalable as increasing the number of stored patterns does not significantly affect the processing time.


Radial Basis Function Associative Memory Radial Basis Function Network Convolutional Neural Network Common Knowledge Base 
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  1. 1.
    Hernández-Orallo, J., Dowe, D.L.: Measuring universal intelligence: Towards an anytime intelligence test. Artif. Intell. 174, 1508–1539 (2010)CrossRefGoogle Scholar
  2. 2.
    Solomonoff, R.: A formal theory of inductive inference: Part II. Information and Control 7(2), 224–254 (1964)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Hopfield, J.J.: Neural networks and physical system with emergent collective computational properties, pp. 2554–2558 (1982)Google Scholar
  4. 4.
    Hecht-Nielsen, R.: Theory of the backpropagation neural network. In: International Joint Conference on Neural Networks, IJCNN, pp. 593–605 (1989)Google Scholar
  5. 5.
    Browne, M., Ghidary, S.S., Mayer, N.M.: Convolutional neural networks for image processing with applications in mobile robotics. In: Speech, Audio, Image and Biomedical Signal Processing using Neural Networks, pp. 327–349 (2008)Google Scholar
  6. 6.
    Amir, A., Muhamad Amin, A., Khan, A.: A multi-feature pattern recognition for P2P-based system using in-network associative memory. Technical Report 2011/265, Clayton School of IT, Monash University, Victoria, Australia (2011)Google Scholar
  7. 7.
    Nasution, B., Khan, A.I.: A hierarchical graph neuron scheme for real-time pattern recognition. IEEE Transactions on Neural Networks, 212–229 (2008)Google Scholar
  8. 8.
    Maymounkov, P., Mazières, D.: Kademlia: A peer-to-peer information system based on the XOR metric. In: Druschel, P., Kaashoek, M.F., Rowstron, A. (eds.) IPTPS 2002. LNCS, vol. 2429, pp. 53–65. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  9. 9.
    Stoica, I., Morris, R., Karger, D., Kaashoek, M.F., Balakrishnan, H.: Chord: A scalable peer-to-peer lookup service for internet applications. SIGCOMM Comput. Commun. Rev. 31, 149–160 (2001)CrossRefGoogle Scholar
  10. 10.
    Castro, M., Liskov, B.: Practical byzantine fault tolerance and proactive recovery. ACM Transactions on Computer Systems (TOCS) 20(4), 398–461 (2002)CrossRefGoogle Scholar
  11. 11.
    Buhmann, M.D.: Radial Basis Functions: Theory and Implementations. Cambridge University Press (2003)Google Scholar
  12. 12.
    Frank, A., Asuncion, A.: UCI machine learning repository (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Amiza Amir
    • 1
    • 3
  • Anang Hudaya M. Amin
    • 2
  • Asad Khan
    • 1
  1. 1.Clayton School of ITMonash UniversityMelbourneAustralia
  2. 2.Multimedia UniversityMalaysia
  3. 3.Universiti Malaysia PerlisMalaysia

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