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ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
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AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
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Artificial Intelligence Review
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IAT '05 Proceedings of the IEEE/WIC/ACM International Conference on Intelligent Agent Technology
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AIS-ADM 2005 Proceedings of the 2005 international conference on Autonomous Intelligent Systems: agents and Data Mining
Agent Methods for Network Intrusion Detection and Response
HoloMAS '07 Proceedings of the 3rd international conference on Industrial Applications of Holonic and Multi-Agent Systems: Holonic and Multi-Agent Systems for Manufacturing
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CIA '07 Proceedings of the 11th international workshop on Cooperative Information Agents XI
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CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
Collaborative Attack Detection in High-Speed Networks
CEEMAS '07 Proceedings of the 5th international Central and Eastern European conference on Multi-Agent Systems and Applications V
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WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
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HCD 09 Proceedings of the 1st International Conference on Human Centered Design: Held as Part of HCI International 2009
Situation-aware trust management
Proceedings of the third ACM conference on Recommender systems
Modeling Contexts with Dependent Types
Fundamenta Informaticae
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ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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ACM Transactions on Intelligent Systems and Technology (TIST) - Special Section on Intelligent Mobile Knowledge Discovery and Management Systems and Special Issue on Social Web Mining
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We present a universal mechanism that can be combined with existing trust models to extend their capabilities towards efficient modelling of the situational (context-dependent) trust. The mechanism describes the similarity between the situations using their distance in a metric space and defines a set of reference contexts in this space to which it associates the trustfulness data. The data associated with each reference context is updated and queried with the weight that decreases with distance between the current situation and the reference context. In the presented mechanism, we use Leader-Follower clustering to place the reference contexts to be representative of the data. In an empirical test, we show that context-aware models easily outperform the general trust when the situation has an impact on partner trustfulness and that their performance and efficiency is comparable with general trust models when the trustfulness is independent of the situation. Multi-context nature of the model also expands its use towards more advanced uses, allowing policy/norm learning from at the trust model at runtime, as well as reasoning based on uncertain identities.