Discovering frequent patterns to bootstrap trust

Name: Discovering frequent patterns to bootstrap trust
Author: Şensoy, Murat, Yilmaz, B., Norman, T. J.

İsim	Discovering frequent patterns to bootstrap trust
Yazar	Şensoy, Murat, Yilmaz, B., Norman, T. J.
Basım Tarihi:	2013
Basım Yeri	- Springer International Publishing
Tür	Kitap
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-3-642-36288-0
Kayıt Numarası	0204eda4-1072-48df-a5a5-5fc0c3c986d8
Lokasyon	Computer Science
Tarih	2013
Notlar	Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Örnek Metin	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.
DOI	10.1007/978-3-642-36288-0_9

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Discovering frequent patterns to bootstrap trust

Yazar Şensoy, Murat, Yilmaz, B., Norman, T. J.

Basım Tarihi 2013

Basım Yeri - Springer International Publishing

Tür Kitap

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-3-642-36288-0

Kayıt Numarası 0204eda4-1072-48df-a5a5-5fc0c3c986d8

Lokasyon Computer Science

Tarih 2013

Notlar Due to copyright restrictions, the access to the full text of this article is only available via subscription.

Örnek Metin 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.

DOI 10.1007/978-3-642-36288-0_9