Reputation in Artificial Societies: Social Beliefs for Social Order
Reputation in Artificial Societies: Social Beliefs for Social Order
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
The Knowledge Engineering Review
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
Fuzzy referral based cooperation in social networks of agents
AI Communications
Detecting noise in recommender system databases
Proceedings of the 11th international conference on Intelligent user interfaces
Convergence of agent reputation with Alpha-Beta filtering vs. a fuzzy system
CIMCA '05 Proceedings of the International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce Vol-1 (CIMCA-IAWTIC'06) - Volume 01
A survey of trust and reputation systems for online service provision
Decision Support Systems
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Inferring Trust in dynamic and subjective environments is a key interesting issue in the way to obtain a complete delegation of human-like decisions in autonomous agents. With this final intention several trust models and strategies have been proposed by researchers, and some of them were tested using the Agent Reputation and Trust (ART) testbed. competitions. In this paper we propose to apply a temporal statistical model to the noisy observations perceived (reputations) that filter out noise (subjectivity) and estimate future state variable (trust). Specifically we have implemented agents that apply Kalman and Interacting Multiple Model (IMM) adaptive filters as a part of ART trust models. Kalman and IMM have been largely applied to make time-dependent predictions in noisy environments, furthermore they are recognized as a reasoning paradigm for time-variable facts, so they seem to be appropriate for agents to infer trust from direct observations and indirect references. In order to show its viability as part of a trust model, we have run ART games with other agents that took part in past ART competitions.