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Asymptotic behavior of a hierarchical system of learning automata

Thathachar, MAL and Ramachandran, KM (1985) Asymptotic behavior of a hierarchical system of learning automata. In: Information Sciences, 35 (2). pp. 91-110.

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Learning automata arranged in a two-level hierarchy are considered. The automata operate in a stationary random environment and update their action probabilities according to the linear-reward- -penalty algorithm at each level. Unlike some hierarchical systems previously proposed, no information transfer exists from one level to another, and yet the hierarchy possesses good convergence properties. Using weak-convergence concepts it is shown that for large time and small values of parameters in the algorithm, the evolution of the optimal path probability can be represented by a diffusion whose parameters can be computed explicitly.

Item Type: Journal Article
Publication: Information Sciences
Publisher: Elsevier Science
Additional Information: The copyright of this article belongs to Elsevier Science
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 28 May 2009 07:27
Last Modified: 19 Sep 2010 05:33
URI: http://eprints.iisc.ac.in/id/eprint/20477

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