Abstract
Multilevel hierarchical systems of learning stochastic automata are investigated in this paper. The system under consideration consists of several levels of automata with different number of outputs. Each automaton is a variable-structure stochastic automaton. A learning scheme which is based on the Bush-Mosteller reinforcement scheme is used to adjust the probabilities associated with the actions of the automata of the hierarchical learning system. Boolean and continuous automaton inputs have been considered. Convergence and convergence rate analysis are presented. The optimization of multimodal functions using this multilevel system of automata is also described. Simulation results indicate the effectiveness of such multilevel learning systems.
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