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MetaTrust: Discriminant Analysis of Local Information for Global Trust Assessment
Citation key XTD-MDALIFGTA-11
Author Xin, Liu and Trédan, Gilles and Datta, Anwitaman
Title of Book Proceedings of the 10th International Conference on Autonomous Agents and Multiagent Systems (AAMAS '11) - Volume 3
Pages 1071–1072
Year 2011
ISBN 978-0-9826571-7-1
Location Taipei, Taiwan
Address New York, NY, USA
Month May
Publisher ACM
Abstract A traditional approach to reasoning about the trustworthiness of a transaction is to determine the trustworthiness of the specific agent involved, based on its past behavior. As a departure from such traditional trust models, we propose a transaction centered trust model (MetaTrust) where an agent uses its previous transactions to assess the trustworthiness of a potential transaction based on associated meta-information, which is capable of distinguishing successful transactions from unsuccessful ones. This meta information is harnessed using a machine learning algorithm (namely, discriminant analysis) to extract relationships between the potential transaction and previous transactions.
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