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dc.contributor.authorŞensoy, Murat
dc.contributor.authorYilmaz, B.
dc.contributor.authorNorman, T. J.
dc.date.accessioned2016-06-30T12:33:35Z
dc.date.available2016-06-30T12:33:35Z
dc.date.issued2013
dc.identifier.issn978-3-642-36288-0
dc.identifier.urihttp://hdl.handle.net/10679/4237
dc.identifier.urihttp://link.springer.com/chapter/10.1007%2F978-3-642-36288-0_9
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractWhen 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.
dc.language.isoengen_US
dc.publisherSpringer International Publishing
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.rightsrestrictedAccess
dc.titleDiscovering frequent patterns to bootstrap trusten_US
dc.typeBook chapteren_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID(ORCID & YÖK ID 41438) Şensoy, Murat
dc.contributor.ozuauthorŞensoy, Murat
dc.identifier.startpage93
dc.identifier.endpage104
dc.identifier.doi10.1007/978-3-642-36288-0_9
dc.identifier.scopusSCOPUS:2-s2.0-84873859952
dc.contributor.authorMale1


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