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Min-Max Algorithm for Game Playing

Certification: Advanced Level Accuracy: 50% Submissions: 2 Points: 15

Implement the Min-Max algorithm with Alpha-Beta pruning for a Tic-Tac-Toe game. It should choose the best move by simulating all future outcomes.

Example 1
    

  • Input:
    board = [['X', 'O', ' '], [' ', 'X', ' '], [' ', ' ', ' ']]
    player = 'O'
    • 

  • Output:
    (2, 2)
    • 

  • Explanation:

      

    • 'O' blocks winning diagonal for 'X' by choosing (2, 2).
      • 

    

    •
Example 2
    

  • Input:
    board = [['X', ' ', ' '], [' ', 'O', ' '], [' ', ' ', 'X']]
    • 

  • Output:
    (0, 1)
    • 

  • Explanation:

      

    • 'O' creates two winning paths by selecting (0, 1).
      • 

    

    •
Constraints
    

  • Board: 3x3
    • 

  • Player is 'X' or 'O'
    • 

  • Empty cell = ' '
    • 

  • Time Complexity: O(b^d)
    • 

  • Space Complexity: O(d)
    •
ArraysFunctions / MethodsHCL TechnologiesZomato
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Hint
Solution

Solution Hints

    
  
  • Implement a function to evaluate the board from a player's perspective
    • 
  
  • Use recursion to explore all possible moves to a certain depth
    • 
  
  • Apply alpha-beta pruning to optimize the search
    • 
  
  • Maximize score for the player, minimize for the opponent
    • 
  
  • Handle base cases like win, loss, and draws
    •

Steps to solve by this approach:

 Step 1: Create a function to evaluate the current state of the board (win, loss, draw, or ongoing).
 Step 2: Implement the minimax algorithm with alpha-beta pruning to efficiently search the game tree.
 Step 3: For each empty cell, simulate placing the player's mark and evaluate the resulting position.
 Step 4: Recursively alternate between maximizing player's score and minimizing opponent's score.
 Step 5: Return the move with the highest evaluated score for the current player.

Submitted Code :