By Topic

Associative learning of Boolean functions

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Mukhopadhyay, S. ; Dept. of Electr. Eng., Indian Inst. of Sci., Bangalore, India ; Thathachar, M.

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

Published in:

Systems, Man and Cybernetics, IEEE Transactions on  (Volume:19 ,  Issue: 5 )