Tic Tac Toe is amongst the most well-known games. It has already been shown that it is a biased game, giving more chances to win for the first player leaving only a draw or a loss as possibilities for the opponent, assuming both the players play optimally. Thus on average majority of the games played result in a draw. The majority of the latest research on how to solve a tic tac toe board state employs strategies such as Genetic Algorithms, Neural Networks, Co-Evolution, and Evolutionary Programming. But these approaches deal with a trivial board state of 3X3 and very little research has been done for a generalized algorithm to solve 4X4,5X5,6X6 and many higher states. Even though an algorithm exists which is Min-Max but it takes a lot of time in coming up with an ideal move due to its recursive nature of implementation. A Sample has been created on this link \url{https://bk-tic-tac-toe.herokuapp.com/} to prove this fact. This is the main problem that this study is aimed at solving i.e providing a generalized algorithm(Approximate method, Learning-Based) for higher board states of tic tac toe to make precise moves in a short period. Also, the code changes needed to accommodate higher board states will be nominal. The idea is to pose the tic tac toe game as a well-posed learning problem. The study and its results are promising, giving a high win to draw ratio with each epoch of training. This study could also be encouraging for other researchers to apply the same algorithm to other similar board games like Minesweeper, Chess, and GO for finding efficient strategies and comparing the results.
翻译:Tic Tac Toe 是最著名的游戏之一。 它已经显示它是一个有偏颇的游戏, 给第一个玩家赢得机会, 第一个玩家只留下一个平分或损失的机会, 假设两个玩家都玩得最优。 因此, 平均而言, 大多数游戏的游戏都产生了一个平分。 关于如何解决一个高分的棋子板国家的最新研究大多使用基因解算法、 神经网络、 共同进化和进化编程等策略。 但是, 这些游戏的比方是一个小小的棋盘状态 3X3, 并且对于一个通用算法, 解决4X4, 5X5, 6X6和许多更高状态的可能性, 给对手提供了更多的机会。 即便一个算法, 但它需要很多时间来适应一个理想的动作, 因为它的反复性执行。 在这个链接上创建了一个样本 https://bk- tick- tapac-toe- lab- lapp. com/} 来证明这个事实。, 这个比较化的算法的每一个更高级算法, 也是为了解决一个更高级的周期, 需要一个更高级的算方法。