Competing neural networks: Finding a strategy for the game of matching pennies
arXiv:cond-mat/0002335 · doi:10.1103/PhysRevE.62.4049
Abstract
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.
arXiv comment9 pages, 5 figures