Total performance by local agent selection strategies in multi-agent systems

In order to achieve efficient progress in activities such as e-commerce and e-transactions in an open environment like the Internet, an agent must choose appropriate partner agents for collaboration. However, agents have no global information about the whole multi-agent system (MAS) and the state of the Internet; therefore, they must select the appropriate partners based on local knowledge and local observations. In this paper, using a multi-agent simulation, we discuss how total MAS performances are affected by local decisions when agents select partners to collaborate with. We also investigate how MAS performances change and how network structures between agents shift according to the progress of agents' local learning and observations. We then discuss the relationship between task load and agent network structure. This relates to estabilishing the optimum time when agents should learn about appropriate partners in an actual environment.