Machine Learning
Formal Analysis of Models for the Dynamics of Trust Based on Experiences
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Multidimensional Context Representations for Situational Trust
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International Journal of Approximate Reasoning
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Trust Modeling with Context Representation and Generalized Identities
CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
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WIMOB '08 Proceedings of the 2008 IEEE International Conference on Wireless & Mobile Computing, Networking & Communication
SRM: a tool for supplier performance
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Proceedings of the third ACM conference on Recommender systems
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ACM SIGKDD Explorations Newsletter
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EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Dynamic Evolution of Role Taxonomies through Multidimensional Clustering in Multiagent Organizations
PRIMA '09 Proceedings of the 12th International Conference on Principles of Practice in Multi-Agent Systems
Trust management model and architecture for context-aware service platforms
OTM'07 Proceedings of the 2007 OTM confederated international conference on On the move to meaningful internet systems: CoopIS, DOA, ODBASE, GADA, and IS - Volume Part II
Recommendations Over Domain Specific User Graphs
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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
Trustworthiness Tendency Incremental Extraction Using Information Gain
WI-IAT '10 Proceedings of the 2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
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Trust estimation is a fundamental process in several multiagent systems domains, from social networks to electronic business scenarios. However, the majority of current computational trust systems is still too simplistic and is not situation-aware, jeopardizing the accuracy of the predicted trustworthiness values of agents. In this paper, we address the inclusion of context in the trust management process. We first overview recently proposed situation-aware trust models, all based on the predefinition of similarity measures between situations. Then, we present our computational trust model, and we focus on Contextual Fitness, a component of the model that adds a contextual dimensional to existing trust aggregation engines. This is a dynamic and incremental technique that extracts tendencies of behavior from the agents in evaluation and that does not imply the predefinition of similarity measures between contexts. Finally, we evaluate our trust model and compare it with other trust approaches in an agent-based, open market trading simulation scenario. The results obtained show that our dynamic and incremental technique outperforms the other approaches in open and dynamic environments. By analyzing examples derived from the experiments, we show why our technique get better results than situation-aware trust models that are based on predefined similarity measures.