Principles of Trust for MAS: Cognitive Anatomy, Social Importance, and Quantification
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Helping based on future expectations
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
The Knowledge Engineering Review
Learning trust strategies in reputation exchange networks
AAMAS '06 Proceedings of the fifth 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
A Dynamic Trust Network for Autonomy-Oriented Partner Finding
AMT '09 Proceedings of the 5th International Conference on Active Media Technology
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
A dynamic trust network for autonomy-oriented partner finding
Journal of Intelligent Information Systems
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We study the problem of agents locating other agents that are both capable and willing to help complete assigned tasks. An agent incurs a fixed cost for each help request it sends out. To minimize this cost, the performance metric used in our work, an agent should learn based on past interactions to identify agents likely to help on a given task. We compare three trust mechanisms: success-based, learning-based, and random. We also consider different agent social attitudes: selfish, reciprocative, and helpful. We evaluate the performance of these social attitudes with both homogeneous and mixed societies. Our results show that learning-based trust decisions consistently performed better than other schemes. We also observed that the success rate is significantly better for reciprocative agents over selfish agents.