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. 
I. Samengo and D.H. Zanette (Centro Atomico Bariloche)