  It is well known that artificial neural networks (ANNs) can learn
deterministic automata. Learning nondeterministic automata is another matter.
This is important because much of the world is nondeterministic, taking the
form of unpredictable or probabilistic events that must be acted upon. If ANNs
are to engage such phenomena, then they must be able to learn how to deal with
nondeterminism. In this project the game of Pong poses a nondeterministic
environment. The learner is given an incomplete view of the game state and
underlying deterministic physics, resulting in a nondeterministic game. Three
models were trained and tested on the game: Mona, Elman, and Numenta's NuPIC.
