Investigating evolutionary checkers by incorporating individual and social learning, N-tuple systems and a round robin tournament - Al-Khateeb, Belal (2011)
In recent years, much research attention has been paid to evolving self-learning game players. Fogel's 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 of 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.