Python Connect 4 - Minimax AI Implementation

This is a text-based Connect 4 game implemented in Python, featuring an intelligent AI opponent. The AI leverages the Minimax algorithm with Alpha-Beta Pruning to analyze the game state and make optimal moves. It prioritizes winning moves while blocking the human player's threats, ensuring a challenging and competitive gameplay experience.

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🔹 Features:

✅ Interactive Gameplay

• The game presents a clear and structured UI, displaying the current game board after each move.
• The human player can input their moves by selecting a column number.
• The AI responds with an intelligent move.

✅ Intelligent AI Opponent (Minimax Algorithm)

• The AI uses the Minimax algorithm with Alpha-Beta pruning to search the best possible move.
• It maximizes its chances of winning while blocking the opponent's potential victories.
• The AI evaluates the board by:
  • Prioritizing four-in-a-row wins.
  • Blocking the human's three-in-a-row threats.
  • Maximizing its own board position strategically.

✅ Game Result Determination

• The program automatically checks for a winner after each move.
• If the board is full without a winner, it declares a stalemate.

✅ Replay Feature

• After the game ends, the user can choose to play again without restarting the program.

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🔹 Algorithm Implementation

⚡ Minimax Algorithm with Alpha-Beta Pruning

The AI's decision-making is powered by the Minimax algorithm with Alpha-Beta pruning to improve efficiency.

How Minimax Works:

1. The algorithm simulates all possible future board states up to a certain depth.
2. It assigns a score to each board state based on the evaluation function:
   • Winning Move: +1000
   • Blocking Opponent's Win: +100
   • Three-in-a-row (AI): +10
   • Three-in-a-row (Human): -50
   • Other strategic placements.
3. The AI selects the move with the highest score.

Optimizations:

• Depth-Limited Search: The algorithm searches up to 4 moves ahead for efficiency.
  • 🔹 You can modify this by changing the depth global variable in the code.
• Alpha-Beta Pruning: Eliminates unnecessary moves to reduce computation time.

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🔹 How to Play

1. Run the Python script to start the game.
2. Choose the board size:
   • Rows: Between 5 and 7.
   • Columns: Between 6 and 8.
3. Choose who starts first (Human or AI).
4. The game board is displayed, and the player selects a column (1-column count).
5. The AI makes its move, and the board updates.
6. The game continues until:
   • A player wins (four in a row).
   • The board is full (stalemate).
7. The game announces the result and asks if you want to play again.

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