Learning behaviors of the hierarchical structure stochastic automata under the nonstationary multiteacher environments and their applications to intelligent robot manipulators

Abstract

Learning behaviors of hierarchical structure stochastic automata are considered in a nonstationary multiteacher environment. It is shown that an extended form of an algorithm proposed by M.A.L. Thathachar and K.R. Ramakrishnan (1981) ensures absolute expediency under some conditions. As a practical application of hierarchical structure stochastic automata, intelligent behavior is considered of robot manipulators going through a maze having a large number of gates that close with unknown rejecting probabilities. It is shown that hierarchical structure stochastic automata can be successfully utilized to let robot manipulators find the best way through the maze.