Review on Computational Trust and Reputation Models
Artificial Intelligence Review
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
A survey of trust and reputation systems for online service provision
Decision Support Systems
Who are the crowdworkers?: shifting demographics in mechanical turk
CHI '10 Extended Abstracts on Human Factors in Computing Systems
Credibility: How Agents Can Handle Unfair Third-Party Testimonies in Computational Trust Models
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Amazon Mechanical Turk marketplace
XRDS: Crossroads, The ACM Magazine for Students - Comp-YOU-Ter
Stochastic Network Optimization with Application to Communication and Queueing Systems
Stochastic Network Optimization with Application to Communication and Queueing Systems
Challenges and Opportunities for Trust Management in Crowdsourcing
WI-IAT '12 Proceedings of the The 2012 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
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Trust is an important mechanism enabling agents to self-police open and dynamic multi-agent systems (ODMASs). Trusters evaluate the reputation of trustees based on their past observed performance, and use this information to guide their future interaction decisions. Existing trust models tend to concentrate trusters' interactions on a small number of highly reputable trustees to minimize risk exposure. When a trustee's servicing capacity is limited, such an approach may cause long delays for trusters and subsequently damage the reputation of trustees. To mitigate this problem, we propose a reputation management approach for trustee agents based on distributed constraint optimization. It helps a trustee to make situation-aware decisions on which incoming requests to serve and prevent the resulting reputation score from being affected by factors out of the trustee's control. The approach is evaluated through theoretical analysis and within a simulated, highly dynamic multi-agent environment. The results show that it can achieve close to optimally efficient utilization of the trustee agents' collective capacity in an ODMAS, promotes fair treatment of trustee agents based on their behavior, and significantly outperforms related work in enhancing social welfare.