SEATER: a simulation environment using learning automata for telephone traffic routing

The authors present SEATER, an environment in which any general telephone traffic routing problem can be set up and simulated by using a variety of routing methods. The routing methods available are the fixed rule, random routing, and routing utilizing a complete assortment of different learning automata. The general telephone traffic routing problem is described, and various existing fixed rule routing schemes supported by the system are explained. Additionally, most learning automata routing techniques are briefly described, and are supported by the system implemented. These schemes have been implemented and compared to the existing fixed rule algorithms in terms of minimizing the blocking probability of the network. The simulations showed that learning automata solutions were far superior to any fixed solutions. The advantage of the former lies in their adaptability to changes in telephone traffic. The system was written in SMALLTALK V and runs on a Mac II