Download Abstract In recent years, much research attention has been paid to

Abstract
In recent years, much research attention has been paid to evolving self-learning game
players. Fogels Blondie24 is just one demonstration of a real success in this field and it
has inspired many other scientists. In this thesis, artificial neural networks are employed
to evolve game playing strategies for the game checkers by introducing a league
structure- into the learning phase of a system based on Blondie24. We believe that this
helps eliminate some of the randomness in the evolution. The best player obtained is
tested against an evolutionary checkers program based on Blondie24. The results
obtained are promising. In addition, we introduce an individual and social learning
mechanism into the learning phase of the evolutionary checkers system. The best player
obtained is tested against an implementation of an evolutionary checkers program, and
also against a player, which utilises a round robin tournament. The results are
promising.
N-tuple systems are also investigated and are used as position value functions for the
game of checkers. The architecture of the n-tuple is utilises temporal difference
learning. The best player obtained as compared with an implementation of evolutionary
checkers program based on Blondie24, and also against a Blondie24 inspired player,
which utilises a round robin tournament. The results are promising. We also address the
question of whether piece difference and look-ahead depth are important factors in the
Blondie24 architecture. Our Experiments show the piece difference and the look-ahead
depth have a significant effect on learning abilities.