A constant dilemma facing game-playing programs is whether to emphasize searching or knowledge. This paper describes a world-championship level Othello program, BILL, that succeeds in both dimensions. The success of BILL is largely due to its understanding of many important Othello features by using a pre-compiled knowledge base of board patterns. Because of this pre-compiled nature of its know...

Monte-Carlo Tree Search (MCTS) has been found to play suboptimally in some tactical domains due to its highly selective search, focusing only on the most promising moves. In order to combine the strategic strength of MCTS and the tactical strength of minimax, MCTSminimax hybrids have been introduced, embedding shallow minimax searches into the MCTS framework. Their results have been promising e...

The machine learning algorithm of [3] is applied to the problem of learning which heuristics to apply when playing the board game Othello. The problem is large, for there are 46,875 heuristics considered. The results are respectable; the Learner is able to beat a practiced human player approximately fifty percent of the time. Suggestions for improvement are included.

This paper describes some initial experimental results in the use and limits of applying experience-based learning techniques to the area of game playing. A system (GINA) has been developed that uses experience-based learning to improve its own base performance level for the game of Othello. A variety of traditional (non-learning) Othello game playing programs have been collected to provide a r...

In this paper we present an intelligent Othello game player that combines game-specific heuristics with machine learning techniques for move selection. Five game specific heuristics have been proposed; some of which can be generalized to fit other games. For machine learning techniques, the normal Minimax algorithm along with a custom variation is used as a base. Genetic algorithms and neural n...