Associative Learning of Boolean Functions

Ahstruet -A cooperative game playing learning automata model is presented for learning 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 fast 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.