A hierarchical system of learning automata that can learn die globally optimal path

Thathachar, M. A. L. ; Sastry, P. S. (1987) A hierarchical system of learning automata that can learn die globally optimal path Information Sciences, 42 (2). pp. 143-166. ISSN 0020-0255

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Official URL: http://www.sciencedirect.com/science/article/pii/0...

Related URL: http://dx.doi.org/10.1016/0020-0255(87)90021-1

Abstract

Systems of learning automata have been studied by various researchers to evolve useful strategies for decision making under uncertainity. Considered in this paper are a class of hierarchical systems of learning automata where the system gets responses from its environment at each level of the hierarchy. A classification of such sequential learning tasks based on the complexity of the learning problem is presented. It is shown that none of the existing algorithms can perform in the most general type of hierarchical problem. An algorithm for learning the globally optimal path in this general setting is presented, and its convergence is established. This algorithm needs information transfer from the lower levels to the higher levels. Using the methodology of estimator algorithms, this model can be generalized to accommodate other kinds of hierarchical learning tasks.

Item Type:Article
Source:Copyright of this article belongs to Elsevier Science.
ID Code:51355
Deposited On:28 Jul 2011 11:58
Last Modified:28 Jul 2011 11:58

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