REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Trust evaluation through relationship analysis
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Monotonic concession protocols for multilateral negotiation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Learning systems of concepts with an infinite relational model
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
An adaptive group-based reputation system in peer-to-peer networks
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Towards Machine Learning on the Semantic Web
Uncertainty Reasoning for the Semantic Web I
TrustWalker: a random walk model for combining trust-based and item-based recommendation
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Computing Confidence Values: Does Trust Dynamics Matter?
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Learning a user-thread alignment manifold for thread recommendation in online forum
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Leveraging Network Properties for Trust Evaluation in Multi-agent Systems
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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We address the learning of trust based on past observations and context information. We argue that from the truster's point of view trust is best expressed as one of several relations that exist between the agent to be trusted (trustee) and the state of the environment. Besides attributes expressing trustworthiness, additional relations might describe commitments made by the trustee with regard to the current situation, like: a seller offers a certain price for a specific product. We show how to implement and learn contextsensitive trust using statistical relational learning in form of the Infinite Hidden Relational Trust Model (IHRTM). The practicability and effectiveness of our approach is evaluated empirically on user-ratings gathered from eBay. Our results suggest that (i) the inherent clustering achieved in the algorithm allows the truster to characterize the structure of a trust-situation and provides meaningful trust assessments; (ii) utilizing the collaborative filtering effect associated with relational data does improve trust assessment performance; (iii) by learning faster and transferring knowledge more effectively we improve cold start performance and can cope better with dynamic behavior in open multiagent systems. The later is demonstrated with interactions recorded from a strategic two-player negotiation scenario.