engleski

Self-trained agents optimize communication service by intelligent selection

The paper presents a method using an optimal selec-tion of an agent in a thought client-server environ-ment. 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 here is a permanent trans-fer adaptation of the requests from a client agent to an optimal service provider agent. Continuous ad-aptation is achieved by reinforcement Q-learning. Simulation results show that by implementing knowl-edge into an agent's behavior it is possible, in parti-cular situations, to significantly accelerate the service system.

Self-training; Communicaton service; Intelligent selection

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