Due to the dynamic and anonymous nature of open environments, it is critically important for agents to identify trustful cooperators which work consistently as they claim. In the e-services and e-commerce communities, trust and reputation systems are applied broadly as one kind of decision support systems, and aim to cope with the consistency problems caused by uncertain trust relationships. However, challenges still exist: on the one hand, we require more flexible trust computation models to satisfy various personal requirements since agents in these communities are heterogeneous; on the other hand, trust and reputation systems calculate the trustworthiness of agents based on the agents' past behavior. The open environments are dynamic, agents are anonymous and the records about agents' past behavior are distributed in the environments, so agents have to search the required records through the environments due to their lack of valid information. Thus, efficient, scalable and effective information collection strategies are required to address these issues. In this paper we present a distributed trust and reputation system to cope with the challenges. We propose a novel and flexible trust computation model based on artificial neural networks. With the advantages of ANN, our trust model tunes the parameters automatically to adapt to various personal requirements. We propose a broker-assisting information collection strategy based on clustering method. With the support of brokers, subcommunities are managed by reputation mechanism in an efficient and scalable way and help their members collect information with high quality. We show the performance of our trust and reputation system by simulation.
['Bo Zong', 'Feng Xu', 'Jun Jiao', 'Jian Lv']
A broker-assisting trust and reputation system based on artificial neural network