A Probabilistic Trust Model for Handling Inaccurate Reputation Sources

  • Jigar Patel
  • W. T. Luke Teacy
  • Nicholas R. Jennings
  • Michael Luck
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3477)


This research aims to develop a model of trust and reputation that will ensure good interactions amongst software agents in large scale open systems in particular. The following are key drivers for our model: (1) agents may be self-interested and may provide false accounts of experiences with other agents if it is beneficial for them to do so; (2) agents will need to interact with other agents with which they have no past experience. Against this background, we have developed TRAVOS (Trust and Reputation model for Agent-based Virtual OrganisationS) which models an agent’s trust in an interaction partner. Specifically, trust is calculated using probability theory taking account of past interactions between agents. When there is a lack of personal experience between agents, the model draws upon reputation information gathered from third parties. In this latter case, we pay particular attention to handling the possibility that reputation information may be inaccurate.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Buchegger, S., Boudec, J.Y.L.: A robust reputation system for mobile ad-hoc networks ic/2003/50. Technical report, EPFL-IC-LCA (2003)Google Scholar
  2. 2.
    DeGroot, M., Schervish, M.: Probability & Statistics. Addison-Wesley, Reading (2002)Google Scholar
  3. 3.
    Dellarocas, C.: Mechanisms for coping with unfair ratings and discriminatory behavior in online reputation reporting systems. In: ICIS, pp. 520–525 (2000)Google Scholar
  4. 4.
    Foster, I., Jennings, N.R., Kesselman, C.: Brain meets brawn: Why grid and agents need each other. In: Proceedings of the 3rd Int. Conf. on Autonomous Agents and Multi-Agent Systems, pp. 8–15 (2004)Google Scholar
  5. 5.
    Gambetta, D.: Can we trust trust? In: Gambetta, D. (ed.) Trust: Making and Breaking Cooperative Relations, ch. 13, pp. 213–237. Basil Blackwell, Malden (1988)Google Scholar
  6. 6.
    Huynh, T.D., Jennings, N.R., Shadbolt, N.: Developing an integrated trust and reputation model for open multi-agent systems. In: Proceedings of the 7th Int. Workshop on Trust in Agent Societies, pp. 62–77 (2004)Google Scholar
  7. 7.
    Ismail, R., Jøsang, A.: The beta reputation system. In: Proceedings of the 15th Bled Conference on Electronic Commerce, Bled, Slovenia (2002)Google Scholar
  8. 8.
    Moukas, A., Zacharia, G., Maes, P.: Amalthaea and histos: Multi-agent systems for www sites and reputation recommendations. In: Klusch, M. (ed.) Intelligent Information Agents, ch. 13. Springer, Heidelberg (1999)Google Scholar
  9. 9.
    Norman, T.J., Preece, A., Chalmers, S., Jennings, N.R., Luck, M., Dang, V., Nguyen, T.D., Deora, V., Shao, J., Gray, A., Fiddian, N.J.: Agent-based formation of virtual organisations. Knowledge-Based Systems 17(2–4), 103–111 (2004)CrossRefGoogle Scholar
  10. 10.
    Ramchurn, S.D., Hunyh, D., Jennings, N.R.: Trust in multi-agent systems. Knowledge Engineering Review 19(1) (2004)Google Scholar
  11. 11.
    Sabater, J., Sierra, C.: Regret: A reputation model for gregarious societies. In: 4th Workshop on Deception Fraud & Trust in Agent Societies, pp. 61–70 (2001)Google Scholar
  12. 12.
    Whitby, A., Jøsang, A., Indulska, J.: Filtering out unfair ratings in bayesian reputation systems. In: Proceedings of the Workshop on Trust in Agent Societies, at the 3rd Int. Conf. on Autonomous Agents & Multi Agent Systems (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Jigar Patel
    • 1
  • W. T. Luke Teacy
    • 1
  • Nicholas R. Jennings
    • 1
  • Michael Luck
    • 1
  1. 1.Electronics & Computer ScienceUniversity of SouthamptonSouthamptonUK

Personalised recommendations