  Dots-and-Boxes is a child's game which remains analytically unsolved. We
implement and evolve artificial neural networks to play this game, evaluating
them against simple heuristic players. Our networks do not evaluate or predict
the final outcome of the game, but rather recommend moves at each stage.
Superior generalisation of play by co-evolved populations is found, and a
comparison made with networks trained by back-propagation using simple
heuristics as an oracle.
