Author
Şensoy, Murat, Yilmaz, B., Norman, T. J.
Publication Date
2013
Publication Place
-
Springer International Publishing
Type
Book
Language
English
Digital
Yes
Manuscript
No
Library
Özyeğin University
Library Asset ID
978-3-642-36288-0
Record ID
0204eda4-1072-48df-a5a5-5fc0c3c986d8
Library Location
Computer Science
Date
2013
Notes
Due to copyright restrictions, the access to the full text of this article is only available via subscription.
Sample Text
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
