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Associative Learning of Boolean functions

Mukhopadhyay, Snehasis and Thathachar, MAL (1989) Associative Learning of Boolean functions. In: IEEE Transactions on Systems, Man and Cybernetics, 19 (5). pp. 1008-1015.


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A cooperative-game-playing learning automata model is presented for a complex nonlinear associative task, namely, learning of Boolean functions. The unknown Boolean function is expressed in terms of minterms, and a team of automata is used to learn the minterms present in the expansion. Only noisy outputs of the Boolean function are assumed to be available for the team of automata that use a variation of the rapidly converging estimator learning algorithm called the pursuit algorithm. A divide-and-conquer approach is proposed to overcome the storage and computational problems of the pursuit algorithm. Extensive simulation experiments have been carried out for six-input Boolean tasks. The main advantages offered by the model are generality, proof of convergence, and fast learning.

Item Type: Journal Article
Publication: IEEE Transactions on Systems, Man and Cybernetics
Publisher: IEEE
Additional Information: ©1989 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Department/Centre: Division of Electrical Sciences > Electrical Engineering
Date Deposited: 05 Oct 2005
Last Modified: 19 Sep 2010 04:20
URI: http://eprints.iisc.ac.in/id/eprint/3773

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