Games have long been seen as the perfect test-bed for artificial intelligence (AI) methods and are also becoming an increasingly important area of application. Game AI is a broad field, covering everything from the challenges of making super-human AI for difficult games such as Go or StarCraft, to create applications such as the automated generation of visual novel games. Implementing AI in games aims at simulating human players. Significant progress has been made especially in relation to classic board games like chess (Deep blue), Go(AlphaGo), etc. which are powerful game-playing computer programs. In this thesis, I took an interest in the strategy board game name Othello (Reversi) and tried to create a more flexible and simple game playing program by implementing an algorithm similar to the minimax to teach our computer player always to find us an optimizing move at the expense of the opponent. Our game agent takes into account the evaluation functions have been set to help it decide the next optimal move in order for it to win the game.