Detecting deception in reputation management
AAMAS '03 Proceedings of the second international joint conference on Autonomous agents and multiagent systems
PeerTrust: Supporting Reputation-Based Trust for Peer-to-Peer Electronic Communities
IEEE Transactions on Knowledge and Data Engineering
TRAVOS: Trust and Reputation in the Context of Inaccurate Information Sources
Autonomous Agents and Multi-Agent Systems
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
An adaptive probabilistic trust model and its evaluation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 3
Electronic Commerce Research and Applications
Smart cheaters do prosper: defeating trust and reputation systems
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Agents with Anticipatory Behaviors: To be Cautious in a Risky Environment
Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence August 29 -- September 1, 2006, Riva del Garda, Italy
Advanced Features in Bayesian Reputation Systems
TrustBus '09 Proceedings of the 6th International Conference on Trust, Privacy and Security in Digital Business
Formal trust model for multiagent systems
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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
The state of the art in trust and reputation systems: a framework for comparison
Journal of Theoretical and Applied Electronic Commerce Research
Handling subjective user feedback for reputation computation in virtual reality
UMAP'11 Proceedings of the 19th international conference on Advances in User Modeling
Journal of Theoretical and Applied Electronic Commerce Research
Preference-oriented QoS-based service discovery with dynamic trust and reputation management
Proceedings of the 27th Annual ACM Symposium on Applied Computing
A dynamic reputation system with built-in attack resilience to safeguard buyers in e-market
ACM SIGSOFT Software Engineering Notes
PRep: a probabilistic reputation model for biased societies
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Proceedings of the 14th Annual International Conference on Electronic Commerce
A strategic reputation-based mechanism for mobile ad hoc networks
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
Modeling Decentralized Reputation-Based Trust for Initial Transactions in Digital Environments
ACM Transactions on Internet Technology (TOIT)
A fuzzy logic based reputation model against unfair ratings
Proceedings of the 2013 international conference on Autonomous agents and multi-agent 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
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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In the absence of legal enforcement procedures for the participants of an open e-marketplace, trust and reputation systems are central for resisting against threats from malicious agents. Such systems provide mechanisms for identifying the participants who disseminate unfair ratings. However, it is possible that some of the honest participants are also victimized as a consequence of the poor judgement of these systems. In this paper, we propose a two-layer filtering algorithm that cognitively elicits the behavioral characteristics of the participating agents in an e-marketplace. We argue that the notion of unfairness does not exclusively refer to deception but can also imply differences in dispositions. The proposed filtering approach aims to go beyond the inflexible judgements on the quality of participants and instead allows the human dispositions that we call optimism, pessimism and realism to be incorporated into our trustworthiness evaluations. Our proposed filtering algorithm consists of two layers. In the first layer, a consumer agent measures the competency of its neighbors for being a potentially helpful adviser. Thus, it automatically disqualifies the deceptive agents and/or the newcomers that lack the required experience. Afterwards, the second layer measures the credibility of the surviving agents of the previous layer on the basis of their behavioral models. This tangible view of trustworthiness evaluation boosts the confidence of human users in using a web-based agent-oriented e-commerce application.