New learning algorithm for hierarchical structure learning automata operating in p-model stationary random environment

Authors:
Yoshio Mogami
Affiliations:
Faculty of Engineering, The University of Tokushima, Tokushima, Japan
Venue:
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
Year:
2005

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Abstract

In this paper, based on the concept of Discretized Generalized Pursuit Algorithm (DGPA), the discretized generalized pursuit hierarchical structure learning algorithm is constructed which is applied to the hierarchical structure learning automata oprating in the P-model stationary random environment. The efficacy of our algorithm is demonstrated by the numerical simulation, in which the hierarchical structure learning automata is applied to the problem of the mobile robots going through an unknown maze (the maze passage problem of mobile robots).