Intelligent agents serving based on the society information

In this paper, we propose a serving system consisting intelligent agents processing society information in a multi-user domain. The agents use the similarity information on the user preferences for providing services with the best quality. This similarity information is formed by using the clustering techniques. The agents are allowed to interact with others and exchange information based on this information. The agents' beliefs are formed based on the clustering process results and the user feedback for the information gained by the interactions. These beliefs are used to provide a continuous service for the user. The natural division of tasks among the agents reduces the workload for each agent. The cluster structure of the society may be changed according to the satisfaction levels of the users. Some general society parameters of the system are learned by a reinforcement learning method. We designed the agents by using our proposed social interactive agent model. The system model can be used for many of the serving systems by changing the service type.