  N-tuple networks have been successfully used as position evaluation functions
for board games such as Othello or Connect Four. The effectiveness of such
networks depends on their architecture, which is determined by the placement of
constituent n-tuples, sequences of board locations, providing input to the
network. The most popular method of placing n-tuples consists in randomly
generating a small number of long, snake-shaped board location sequences. In
comparison, we show that learning n-tuple networks is significantly more
effective if they involve a large number of systematically placed, short,
straight n-tuples. Moreover, we demonstrate that in order to obtain the best
performance and the steepest learning curve for Othello it is enough to use
n-tuples of size just 2, yielding a network consisting of only 288 weights. The
best such network evolved in this study has been evaluated in the online
Othello League, obtaining the performance of nearly 96% --- more than any other
player to date.
