The distributed ASCI Supercomputer project
ACM SIGOPS Operating Systems Review
Formal Analysis of Models for the Dynamics of Trust Based on Experiences
MAAMAW '99 Proceedings of the 9th European Workshop on Modelling Autonomous Agents in a Multi-Agent World: MultiAgent System Engineering
Trust Dynamics: How Trust Is Influenced by Direct Experiences and by Trust Itself
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
The Knowledge Engineering Review
An Adaptive Agent Model Estimating Human Trust in Information Sources
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 02
Personalised and dynamic trust in social networks
Proceedings of the third ACM conference on Recommender systems
Comparing a Cognitive and a Neural Model for Relative Trust Dynamics
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part I
Modeling and mining of dynamic trust in complex service-oriented systems
Information Systems
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
Methods for model-based reasoning within agent-based Ambient Intelligence applications
Knowledge-Based Systems
Modelling biased human trust dynamics
Web Intelligence and Agent Systems
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When considering intelligent agents that interact with humans, having an idea of the trust levels of the human, for example in other agents or services, can be of great importance. Most models of human trust that exist, are based on some rationality assumption, and biased behavior is not represented, whereas a vast literature in Cognitive and Social Sciences indicates that humans often exhibit non-rational, biased behavior with respect to trust. This paper reports how some variations of biased human trust models have been designed, analyzed and validated against empirical data. The results show that such biased trust models are able to predict human trust significantly better.