Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Bayesian Network-Based Trust Model
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
The Knowledge Engineering Review
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
Protecting buying agents in e-marketplaces by direct experience trust modelling
Knowledge and Information Systems
Modelling reputation in agent-based marketplaces to improve the performance of buying agents
UM'03 Proceedings of the 9th international conference on User modeling
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Trust assurance levels of cybercars in v2x communication
Proceedings of the 2013 ACM workshop on Security, privacy & dependability for cyber vehicles
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In this paper, we present an approach for modeling user trustworthiness when traffic information is exchanged between vehicles in transportation environments. Our multi-faceted approach to trust modeling combines priority-based, role-based and experience-based trust, integrated with a majority consensus model influenced by time and location, for effective route planning. The proposed representation for the user model is outlined in detail (integrating ontological and propositional elements) and the algorithm for updating trust values is presented as well. This trust modeling framework is validated in detail through an extensive simulation testbed that models vehicle route planning. We are able to show decreased average path time for vehicles when all facets of our trust model are employed in unison. Included is an interesting confirmation of the value of distinguishing direct and indirect observations of users.