Tic-Tac-Toe with Minimax AI

1. Introduction to the Game and Minimax

Tic-Tac-Toe is a classic two-player board game played on a 3×3 grid. Players take turns placing their symbols (usually X and O) on the grid, aiming to align three symbols in a row, column, or diagonal.

This project supports two modes: Player vs Player (PvP) and Player vs Computer (PvC). In the PvP mode, two human players can take turns on the same device. In the PvC mode, the player competes against an intelligent computer opponent powered by the Minimax algorithm.

The player also has the option to choose whether to make the first move or allow the computer to start. The minimax algorithm ensures that the AI plays optimally, making it impossible to defeat. It will win if possible or force a draw when not.

2. Core Mechanics of the Project

1. Java Swing GUI: A visually interactive 3x3 board is built using Java Swing, allowing the player to click on cells to make their move.
2. Turn Handling: The user selects whether to go first or second. Turns alternate between the player and the AI.
3. Minimax-Powered AI: On its turn, the computer uses the minimax algorithm to determine the most optimal move.
4. Game Result Detection: The game automatically detects a win, loss, or draw after every move and displays the outcome.
5. Restart Option: A button is provided to restart the game after a match ends.

3. How the Minimax Algorithm Works in This Project

The Minimax algorithm is used to simulate all possible future moves and outcomes to help the AI pick the most advantageous move. It does so by treating the game as a tree of possibilities, with each node representing a board state and each edge representing a move.

Here's a theoretical breakdown of how minimax works in this Tic-Tac-Toe project:

1. Turn Simulation: At the AI's turn, it examines all possible cells where it can place its symbol (O). For each such move, it pretends to make that move and then simulates the opponent's possible responses (X).

2. Recursive Evaluation: For every simulated move, the algorithm checks if the resulting board state leads to a win, loss, or draw. If the game is not over, it recurses deeper, alternating between the "maximizing" player (AI) and the "minimizing" player (human).

3. Scoring Terminal States: Once a terminal state (game over) is reached, it assigns a score:
- +1 if the AI (O) wins
- 0 if it's a draw
- -1 if the AI loses

4. Backtracking and Decision: As recursion unwinds, the algorithm propagates these scores upward. At each level:
- The AI (maximizer) picks the move with the highest score.
- The human (minimizer) is assumed to pick the move with the lowest score.

5. Best Move Selection: Ultimately, the AI selects the move that leads to the path with the highest guaranteed score (considering the human opponent will also play optimally). This ensures the AI never loses, and it wins when possible.

The advantage of this approach is that it makes the AI unbeatable. While a human can still draw the game by playing perfectly, they will never be able to win against this AI.