Nonstationary models of learning automata routing in data communication networks

In a data communication network the message traffic has peak and slack periods and the network topology may change. When the learning approach is applied to routing, a learning automation is situation at each node in the network. Each automation selects the routing choices at its node and modifies its strategy according to network conditions. A model of a nonstationary automaton environment, with response characteristics dynamically related to the probabilities of the actions performed on it, is proposed. The limiting behavior of the model is identical to that of the earlier models. Simulation studies of automata operating in simple queuing networks reinforce the analytical results and show that the parameters of the proposed model can be chosen to predict transient behavior.