Learning algorithms for quality of service multicast routing

Stochastic learning automata are used to `grow´ quality of service bounded multicast trees in a dynamic membership environment. It is found that learning automata, which use minimal state information and require only local connectivity knowledge, provide superior results to alternative shortest path approaches by learning to minimise the number of blocked connection attempts via load balancing