Evolutionary Othello Players Boosted by Opening Knowledge
Abstract
The evolutionary approach for gaming is different from the traditional one that exploits knowledge of the opening, middle, and endgame stages. It is therefore sometimes inefficient to evolve simple heuristics that may be created easily by humans because it is based purely on a bottom-up style of construction. Incorporating domain knowledge into evolutionary computation can improve the performance of evolved strategies and accelerate the speed of evolution by reducing the search space. In this paper, we develop an evolutionary Othello player with the systematic insertion of opening knowledge into the framework of evolution. The probability of opening selection is coming from the expert’s opening list. Preliminary experimental results show that the proposed method is promising for generating better strategies for Othello players.
Authors: Kyung-Joong Kim, Sung-Bae Cho