An evidential model of distributed reputation management
Proceedings of the first international joint conference on Autonomous agents and multiagent systems: part 1
Best-Response Multiagent Learning in Non-Stationary Environments
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Coping with inaccurate reputation sources: experimental analysis of a probabilistic trust model
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Learning trust strategies in reputation exchange networks
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Challenges for trust, fraud and deception research in multi-agent systems
AAMAS'02 Proceedings of the 2002 international conference on Trust, reputation, and security: theories and practice
Soft security: isolating unreliable agents from society
AAMAS'02 Proceedings of the 2002 international conference on Trust, reputation, and security: theories and practice
Agent trust evaluation and team formation in heterogeneous organizations
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Extending virtual organizations to improve trust mechanisms
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Action-Based Environment Modeling for Maintaining Trust
Trust in Agent Societies
Protecting buying agents in e-marketplaces by direct experience trust modelling
Knowledge and Information Systems
On the modeling of honest players in reputation systems
Journal of Computer Science and Technology - Special section on trust and reputation management in future computing systmes and applications
Partners selection in multi-agent systems by using linear and non-linear approaches
Transactions on computational science I
Evidence-based trust: A mathematical model geared for multiagent systems
ACM Transactions on Autonomous and Adaptive Systems (TAAS)
Attribute-based authentication for multi-agent systems with dynamic groups
Computer Communications
A probabilistic approach for maintaining trust based on evidence
Journal of Artificial Intelligence Research
Trust as dependence: a logical approach
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
Facing openness with socio-cognitive trust and categories
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
A robust trust model for service-oriented systems
Journal of Computer and System Sciences
AT'13 Proceedings of the Second international conference on Agreement Technologies
Trust-based role coordination in task-oriented multiagent systems
Knowledge-Based Systems
A framework to choose trust models for different e-marketplace environments
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
From blurry numbers to clear preferences: A mechanism to extract reputation in social networks
Expert Systems with Applications: An International Journal
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Trust is essential when an agent must rely on others to provide resources for accomplishing its goals. When deciding whether to trust, an agent may rely on, among other types of trust information, its past experience with the trustee or on reputations provided by third-party agents. However, each type of trust information has strengths and weaknesses: trust models based on past experience are more certain, yet require numerous transactions to build, while reputations provide a quick source of trust information, but may be inaccurate due to unreliable reputation providers. This research examines how the accuracy of experience- and reputation-based trust models is influenced by parameters such as: frequency of transactions with the trustee, trustworthiness of the trustee, and accuracy of provided reputations. More importantly, this research presents a technique for dynamically learning the best source of trust information given these parameters. The demonstrated learning technique achieves payoffs equal to those achieved by the best single trust information source (experience or reputation) in nearly every scenario examined.