REGRET: reputation in gregarious societies
Proceedings of the fifth international conference on Autonomous agents
Supporting Trust in Virtual Communities
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 6 - Volume 6
Social ReGreT, a reputation model based on social relations
ACM SIGecom Exchanges - Chains of commitment
Agent-Based Framework for Dynamic Supply Chain Configuration
HICSS '04 Proceedings of the Proceedings of the 37th Annual Hawaii International Conference on System Sciences (HICSS'04) - Track 7 - Volume 7
Review on Computational Trust and Reputation Models
Artificial Intelligence Review
Task delegation using experience-based multi-dimensional trust
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
An integrated trust and reputation model for open multi-agent systems
Autonomous Agents and Multi-Agent Systems
A fuzzy approach to reasoning with trust, distrust and insufficient trust
CIA'06 Proceedings of the 10th international conference on Cooperative Information Agents
A distributed reputation and trust management scheme for mobile peer-to-peer networks
Computer Communications
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Agents in open and dynamic environments face the challenge of uncertainty while interacting with others to achieve their goals. They face quick and unforeseen changes to the behaviour of other agents and the population itself, as agents join and leave at will. Since agents are assumed to be self-interested, it is essential for them to be able to choose the most reliable interaction partners to maximise the success of their interactions. Efficient agent selection requires information about their behaviour in different situations. This information can be obtained from direct experience as well as from recommendations. This paper presents a trust and reputation model, which allows agents to select interaction partners efficiently by adapting quickly to a dynamic environment. Our approach is built upon a number of components from several existing models to assess trustworthiness from direct interactions and recommendations. We take a multidimensional approach to evaluate trust and reputation and include indirect recommendations as another source of trust. This reinforces our previous work on recommendation sharing, which includes information about the recency and relevance of interactions, allowing an evaluator to select recommenders based on trust.