Advertisement

Robust Online Reputation Mechanism by Stochastic Approximation

  • Takamichi Sakai
  • Kenji Terada
  • Tadashi Araragi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3394)

Abstract

Recently, online reputation mechanisms have attracted much attention in many areas. They have been widely adopted and worked well, although their reliability is still a major concern. Because of online properties such as openness and anonymity, it is necessary to consider rating errors, noise and unfair lies. Furthermore, these disturbances (attacks) have a significant effect on multi-agent systems containing malicious agents who tell lies or engage in strategic manipulations. Current online reputation mechanisms are not sufficiently robust against such disturbances. In an attempt to solve this problem, we propose a stochastic approximation-based online reputation mechanism. Our mechanism assigns one global trustworthiness value to each agent and updates estimates of these values dynamically from mutual ratings of agents. Experimental results show that our mechanism is able to identify good and bad agents effectively under condition of the above disturbances and also trace the changes in agents’ true trustworthiness values adaptively.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Kollock, P.: The production of trust in online markets. In: Lawler, E.J., Macy, M., Thyne, S., Walker, H.A. (eds.) Advances in Group Processes, vol. 16. JAI Press, Greenwich (1999)Google Scholar
  2. 2.
    Resnick, P., Zeckhauser, R., Friedman, E., Kuwabara, K.: Reputation systems. Communications of the ACM 43 (2000) 45–48CrossRefGoogle Scholar
  3. 3.
    Dellarocas, C.: The digitization of word-of-mouth: Promise and challenges of online reputation systems. In: Management Science (2003)Google Scholar
  4. 4.
    Ishida, Y.: An immune network approach to sensor-based diagnosis by self-organization. Complex Systems 10, 73–90 (1996)Google Scholar
  5. 5.
    Zacharia, G., Moukas, A., Maes, P.: Collaborative reputation mechanisms in electronic marketplaces. In: Proceedings of the 32nd Hawaii International Conference on System Sciences, HICSS-32 (1999)Google Scholar
  6. 6.
    Dellarocas, C.: Immunizing online reputation reporting systems against unfair ratings and discriminatory behavior. In: Proceedings of the 2nd ACM Conference on Electronic Commerce, EC 2000 (2000)Google Scholar
  7. 7.
    Yu, B., Singh, M.P.: Detecting deception in reputation management. In: Proceedings of Second International Joint Conference on Autonomous Agents and Multi-Agent Systems, AAMAS 2003 (2003)Google Scholar
  8. 8.
    Wang, Y., Vassileva, J.: Bayesian network-based trust model. In: Proceedings of the IEEE/WIC International Conference on Web Intelligence, WI 2003 (2003)Google Scholar
  9. 9.
    Kushner, H.J., Yin, G.G.: Stochastic Approximation and Recursive Algorithms and Applications, 2nd edn. Springer, Heidelberg (2003)zbMATHGoogle Scholar
  10. 10.
    Sutton, R.S., Barto, A.G.: Reinforcement Learning. MIT Press, Cambridge (1998)Google Scholar
  11. 11.
    Benveniste, A., Métivier, M., Priouret, P.: Adaptive Algorithms and Stochastic Approximations. Springer, Heidelberg (1990)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Takamichi Sakai
    • 1
  • Kenji Terada
    • 2
  • Tadashi Araragi
    • 1
  1. 1.NTT Communication Science LaboratoriesNTT CorporationKyotoJapan
  2. 2.NTT East Research and Development CenterNTT East CorporationTokyoJapan

Personalised recommendations