Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
Estimation with Applications to Tracking and Navigation
Estimation with Applications to Tracking and Navigation
The Knowledge Engineering Review
Task delegation using experience-based multi-dimensional trust
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
Rumours and reputation: evaluating multi-dimensional trust within a decentralised reputation system
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Computing Confidence Values: Does Trust Dynamics Matter?
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Combining statistics and arguments to compute trust
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Engaging the dynamics of trust in computational trust and reputation systems
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Trust estimation using contextual fitness
KES-AMSTA'10 Proceedings of the 4th KES international conference on Agent and multi-agent systems: technologies and applications, Part I
Multi-variate Distributed Data Fusion with Expensive Sensor Data
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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In this paper we develop a novel probabilistic model of computational trust that allows agents to exchange and combine reputation reports over heterogeneous, correlated multi-dimensional contracts. We consider the specific case of an agent attempting to procure a bundle of services that are subject to correlated quality of service failures (e.g. due to use of shared resources or infrastructure), and where the direct experience of other agents within the system consists of contracts over different combinations of these services. To this end, we present a formalism based on the Kalman filter that represents trust as a vector estimate of the probability that each service will be successfully delivered, and a covariance matrix that describes the uncertainty and correlations between these probabilities. We describe how the agents' direct experiences of contract outcomes can be represented and combined within this formalism and we empirically demonstrate that our formalism provides significantly better trustworthiness estimates than the alternative of using separate single-dimensional trust models for each separate service (where information regarding the correlations between each estimate is lost).