Competing neural networks: finding a strategy for the game of matching pennies

The ability of a deterministic, plastic system to learn to imitate stochastic behavior is analyzed. Two neural networks-actually, two perceptrons-are put to play a zero-sum game one against the other. The competition, by acting as a kind of mutually supervised learning, drives the networks to produce an approximation to the optimal strategy, that is to say, a random signal.