摘要
Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.
Because of the anonymity and openness of online transactions and the richness of network resources, the problems of the credibility of the online trading and the exact selection of network resources have become acute. For this reason, a reputation-based multi-agent model for network resource selection (RMNRS) is presented. The model divides the network into numbers of trust domains. Each domain has one domain-agent and several entity-agents. The model prevents the inconsistency of information that is maintained by differ-ent agents through the periodically communication between the agents. The model enables the consumers to receive responses from agents significantly quicker than that of traditional models, because the global reputation values of service providers and consumers are evaluated and updated dynamically after each transaction. And the model allocates two global reputation values to each entity and takes the recognition value that how much the service provider knows the service into account. In order to make users choose the best matching services and give users with trusted services, the model also takes the similarity between services into account and uses the similarity degree to amend the integration reputation value with harmonic-mean. Finally, the effectiveness and feasibility of this model is illustrated by the experiment.
