Store the current state (values) of the tic-tac-toe board in an array.To make the best decision, the AI needs to do the following: And since there are three empty cells on the board, it implies that X has three possible play choices - top-middle, center, or bottom-right.īut which is the best choice? Which move will best help X minimize the maximum possibility of losing the game? Figure 3: AI player’s possible play choices In the current stage of the tic-tac-toe game (as shown in figure 2 above), it’s X’s turn to play (that is, the AI’s turn). To make this tutorial precise, the root node (the current state of the tic-tac-toe game) we will use will be a near-the-end state game board - as shown in figure 2 below.Īlso, the X mark will represent the AI’s mark, while the O mark will be the human player’s mark. Step 2: Get familiar with this tutorial’s root node In short, a minimax algorithm is a recursive function created to help a player (the maximizer) decide on the gameplay that minimizes the maximum possibility to lose a game. As such, the minimizing player is the player whose chances of winning must be minimized. In contrast, the maximizing player’s opponent is called the minimizing player. In other words, the original invocator of minimax is the player that wants to maximize any opportunity to win the game. In most cases, the player that initially invokes minimax is called the maximizing player. However, you can also use it for complex games, like chess, and general decision-making to resolve any uncertainties. Our focus in this guide is to use minimax to create an unbeatable AI for a tic-tac-toe game. Figure 1: The game tree of a concluding tic-tac-toe game In such a case, the tree's root is the game's current state - where the minimax algorithm got invoked. Graphically, we can represent minimax as an exploration of a game tree's nodes to discover the best game move to make. Step 1: Understand the basics of the minimax algorithmĪ minimax algorithm is a recursive program written to find the best gameplay that minimizes any tendency to lose a game while maximizing any opportunity to win the game. If you are also on a similar path, let me take you through the steps to build an unbeatable AI (Artificial Intelligence). However, my “Aha!” moment came when I took the time to understand how the minimax algorithm works. But as I went back and forth from articles to videos to a series of coding meditations, I only succeeded in becoming more confused. Well, I thought I could easily get an answer to this question. Recently I wondered – how can I program the computer to be unbeatable in a tic-tac-toe game?
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