Selection of relevant features and examples in machine learning
Artificial Intelligence - Special issue on relevance
A tutorial on learning with Bayesian networks
Learning in graphical models
REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Mixture reduction via predictive scores
Statistics and Computing
Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
Collaborative Reputation Mechanisms in Electronic Marketplaces
HICSS '99 Proceedings of the Thirty-second Annual Hawaii International Conference on System Sciences-Volume 8 - Volume 8
AAMAS '04 Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems - Volume 2
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
Regret-based utility elicitation in constraint-based decision problems
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Sequential decision making with untrustworthy service providers
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Exchanging reputation information between communities: a payment-function approach
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A trust model for supply chain management
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Trust alignment: a sine qua non of open multi-agent systems
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Engineering trust alignment: Theory, method and experimentation
International Journal of Human-Computer Studies
Talking about trust in heterogeneous multi-agent systems
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
PRep: a probabilistic reputation model for biased societies
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
SARC: subjectivity alignment for reputation computation
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 3
Trust beyond reputation: A computational trust model based on stereotypes
Electronic Commerce Research and Applications
Trust Management for VANETs: Challenges, Desired Properties and Future Directions
International Journal of Distributed Systems and Technologies
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
Modeling Decentralized Reputation-Based Trust for Initial Transactions in Digital Environments
ACM Transactions on Internet Technology (TOIT)
Improving trust modeling through the limit of advisor network size and use of referrals
Electronic Commerce Research and Applications
Bayesian Inference in Trust Networks
ACM Transactions on Management Information Systems (TMIS)
A framework to choose trust models for different e-marketplace environments
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Misleading opinions provided by advisors: dishonesty or subjectivity
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
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We present a model for buying agents in e-marketplaces to interpret evaluations of sellers provided by other buying agents, known as advisors. The interpretation of seller evaluations is complicated by the inherent subjectivity of each advisor, the possibility that advisors may deliberately provide misleading evaluations to deceive competitors and the dynamic nature of seller and advisor behaviours that may naturally change seller evaluations over time. Using a Bayesian approach, we demonstrate how to cope with subjectivity, deception and change in a principled way. More specifically, by modeling seller properties and advisor evaluation functions as dynamic random variables, buyers can progressively learn a probabilistic model that naturally and "correctly" calibrates the interpretation of seller evaluations without having to resort to heuristics to explicitely detect and filter/discount unreliable seller evaluations. Our model, called BLADE, is shown empirically to achieve lower mean error in the estimation of seller properties when compared to other models for reasoning about advisor ratings of sellers in electronic maketplaces.