Evolving game playing strategies for Othello

There has been a fair amount of research into the use of genetic programming for the induction of game playing strategies for board games such as chess, checkers, backgammon and Othello. A majority of this research has focused on developing evaluation functions for use with standard game playing algorithms such as the alpha-beta algorithm or Monte Carlo tree search. The research presented in this paper proposes a different approach based on heuristics. Genetic programming is used to evolve game playing strategies composed of heuristics. Each evolved strategy represents a player. While in previous work the game playing strategies are generally created offline, in this research learning and generation of the strategies takes place online, in real time. An initial population of players created using the ramped half-and-half method is iteratively refined using reproduction, mutation and crossover. Tournament selection is used to choose parents. The board game Othello, also known as Reversi, is used to illustrate and evaluate this novel approach. The evolved players were evaluated against human players, Othello WZebra, AI Factory Reversi and Math is fun Reversi. This study has revealed the potential of the proposed novel approach for evolving game playing strategies for board games. It has also identified areas for improvement and based on this future work will investigate mechanisms for incorporating mobility into the evolved players.