Self-trained agents optimize communication service by intelligent selection

The paper presents a method using an optimal selection of an agent in a thought client-server environment. The selection criteria are based on continuous learning and monitoring of the agent´s behavior. The work has been motivated by the different abilities and properties of the agents in the network, particularly when they act in distributed environments. The main idea presented is a permanent transfer adaptation of the requests from a client agent to an optimal service provider agent. Continuous adaptation is achieved by reinforcement Q-learning. Simulation results show that by implementing knowledge into an agent´s behavior, it is possible, in particular situations, to significantly accelerate the service system