Minimax Algorithm for Tic Tac Toe (Coding Challenge 154)

The speaker introduces a coding challenge involving the Tic-Tac-Toe game enhanced with the Minimax algorithm. They recount their previous attempt at creating a two-player random spot picker version of the game and the subsequent adjustments made to allow human interaction. The speaker demonstrates a game where they play against a random computer picker, winning as 'O'. The main objective of the video is to implement the Minimax algorithm, a search algorithm used to find the optimal move for the AI player. The speaker recommends resources for understanding the Minimax algorithm and alpha-beta pruning, which they do not plan to implement. The Minimax algorithm is visualized as a tree, starting from the current game board state, with each node representing a possible move and its subsequent outcomes. The speaker uses a Tic-Tac-Toe board to diagram the possible moves for 'X' and evaluates the outcomes, marking the board with wins and losses.

The speaker explains the scoring system for the Tic-Tac-Toe game in the context of the Minimax algorithm. They assign a score of +1 for an 'X' win, -1 for an 'O' win, and 0 for a tie. The speaker discusses the decision-making process, where 'X' is the maximizing player trying to achieve the highest score, and 'O' is the minimizing player aiming to select the least favorable outcome for 'X'. The speaker uses the tree diagram to illustrate how 'X' would choose the best move by considering all possible outcomes and selecting the one with the highest score. The concept of Minimax is further explained through the algorithm's recursive nature, where the function calls itself to evaluate each possible move and its subsequent positions, eventually selecting the optimal move.

The speaker begins the process of coding the Minimax algorithm for the Tic-Tac-Toe game. They discuss the need to avoid altering the global board state and consider making a copy of the board or undoing moves after each evaluation. The speaker outlines the structure of the Minimax function, which includes a placeholder that always returns a score of 1, simulating a tie in every scenario. They then discuss the need to check if the game has reached a terminal state (win, loss, or tie) before proceeding with the recursive evaluation of possible moves. The speaker also introduces the concept of depth in the Minimax algorithm, which is used to track the level of recursion and could potentially be used to influence the scoring based on the speed of achieving a win.

The speaker implements the core of the Minimax algorithm, detailing the process of checking all possible moves for both 'X' and 'O', and determining the best move based on the algorithm's criteria. They discuss the importance of correctly handling the turns of the maximizing and minimizing players and the need to return the best score after evaluating all possibilities. The speaker encounters and corrects errors in their code, such as misplaced return statements and incorrect handling of the players' turns. They also consider refactoring the code for readability and efficiency, including the use of max and min functions to simplify the score comparison logic.

The speaker tests the Minimax algorithm with the AI playing first and notes the AI's ability to make optimal moves. They experiment with the AI's behavior by changing the starting conditions and observe that the AI consistently plays optimally, leading to ties or wins depending on the human player's moves. The speaker identifies and corrects a critical error in the code that caused the AI to make suboptimal moves and discusses potential improvements and variations of the game, such as considering the depth of the game tree, changing the board size, or adapting the algorithm to other games like Connect Four.

The speaker concludes the video by suggesting potential extensions and modifications to the Minimax algorithm for Tic-Tac-Toe. They propose ideas such as creating a 3D version of Tic-Tac-Toe, applying the algorithm to more complex games like Chess, and incorporating heuristics or estimations for games with vast move sets. The speaker also encourages viewers to experiment with the algorithm, share their creations, and engage with the coding community for support and inspiration.