Self-trained agents optimize communication service by intelligent selection
Marijan Kunštić, Dragan M. Jevtić, D. Sablic · 2002
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.