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Discovering Frequent Patterns to Bootstrap Trust

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 7607)

Abstract

When a new agent enters to an open multiagent system, bootstrapping its trust becomes a challenge because of the lack of any direct or reputational evidence. To get around this problem, existing approaches assume the same a priori trust for all newcomers. However, assuming the same a priori trust for all agents may lead to other problems like whitewashing. In this paper, we leverage graph mining and knowledge representation to estimate a priori trust for agents. For this purpose, our approach first discovers significant patterns that may be used to characterise trustworthy and untrustworthy agents. Then, these patterns are used as features to train a regression model to estimate trustworthiness. Lastly, a priori trust for newcomers are estimated using the discovered features based on the trained model. Through extensive simulations, we have showed that the proposed approach significantly outperforms existing approaches.

Keywords

  • Multiagent System
  • Frequent Pattern
  • Discriminative Feature
  • Interaction History
  • Subjective Logic

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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Sensoy, M., Yilmaz, B., Norman, T.J. (2013). Discovering Frequent Patterns to Bootstrap Trust. In: Cao, L., Zeng, Y., Symeonidis, A.L., Gorodetsky, V.I., Yu, P.S., Singh, M.P. (eds) Agents and Data Mining Interaction. ADMI 2012. Lecture Notes in Computer Science(), vol 7607. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36288-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-36288-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-36287-3

  • Online ISBN: 978-3-642-36288-0

  • eBook Packages: Computer ScienceComputer Science (R0)