Title :
A new learning algorithm for the hierarchical structure learning automata operating in the nonstationary S-model random environment
Author :
Baba, Norio ; Mogami, Yoshio
Author_Institution :
Dept. of Inf. Sci., Osaka Kyoiku Univ., Kashiwara, Japan
fDate :
12/1/2002 12:00:00 AM
Abstract :
An extended algorithm of the relative reward strength algorithm is proposed. It is shown that the proposed algorithm ensures the convergence with probability I to the optimal path under the certain type of nonstationary environment. Several computer simulation results confirm the effectiveness of the proposed algorithm.
Keywords :
learning (artificial intelligence); learning automata; convergence; hierarchical structure learning automata; learning algorithm; nonstationary S-model random environment; nonstationary environment; optimal path; relative reward strength algorithm; Cities and towns; Computer simulation; Convergence; Helium; Hierarchical systems; Information science; Intelligent systems; Learning automata; Power engineering and energy; Pursuit algorithms;
Journal_Title :
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
DOI :
10.1109/TSMCB.2002.1049609
