Macro self-organization based on distributed multi-agent control in local view

With centralized collaborative based information filtering, the central node becomes the bottleneck. A distributed multi agent collaborative based information filtering method is proposed based on the limited friends list and trust relationships. The agent has only a local viewpoint and changes the trust value through interaction. The trust update control rules are classified into three classes, which are proportion, integral, and differential coefficient (PID). Our simulation experiments used proportion and integral control rules. Through learning, the agent learns the righter and righter trust value to its friends. The ranked connectivity of different agents forms a ranked graph which also reflects macro clustering and macro self organization processes.