DocumentCode
869997
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
Volume
32
Issue
6
fYear
2002
fDate
12/1/2002 12:00:00 AM
Firstpage
750
Lastpage
758
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;
fLanguage
English
Journal_Title
Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publisher
ieee
ISSN
1083-4419
Type
jour
DOI
10.1109/TSMCB.2002.1049609
Filename
1049609
Link To Document