🎯 Minimax Algorithm

📜 Explanation

Minimax is a kind of backtracking algorithm to find the optimal move for a player, assuming that your opponent also plays optimally.
In Minimax the two players are called maximizer and minimizer. The maximizer tries to get the highest score possible while the minimizer tries to do the opposite and get the lowest score possible.
Every board state has a value associated with it. In a given state if the maximizer has upper hand then, the score of the board will tend to be some positive value. If the minimizer has the upper hand in that board state then it will tend to be some negative value.
Since this is a backtracking based algorithm, it tries all possible moves, then backtracks and makes a decision.

👩‍💻 Get Maximizer value

#include <bits\stdc++.h>
using namespace std;

int getLeftChild(int node) { return node * 2; }
int getRightChild(int node) { return node * 2 + 1; }

int minimax(int curDepth, int nodeIndex, bool isMaximizer, vector<int> scores, int h) {
 if (curDepth == h) { // we finished the tree
  return scores[nodeIndex];
 }
 if (isMaximizer) { // I am a maximizer node
    int leftValue = minimax(curDepth + 1, getLeftChild(nodeIndex), false, scores, h);
    int rightValue = minimax(curDepth + 1, getRightChild(nodeIndex), false, scores, h);
    return max(leftValue, rightValue); // get max from my children
 }
 else {
    int leftValue = minimax(curDepth + 1, getLeftChild(nodeIndex), true, scores, h);
    int rightValue = minimax(curDepth + 1, getRightChild(nodeIndex), true, scores, h);
    return min(leftValue, rightValue);
 }
}

int main() {
 vector<int> scores = { 3, 5, 2, 9, 12, 5, 23, 23 };
 int n = scores.size();
 int h = ceil(log2(n));
 int maximizerValue = minimax(0, 0, true, scores, h);
 cout << maximizerValue << endl; // 12
}

🔎 Introduction to Evaluation Function

In the above example, the scores (leaves of Game Tree) are given to us. For a typical game, we need to derive these values by a function, This function is often known as Evaluation Function, It is sometimes also called Heuristic Function.
The basic idea behind the evaluation function is to give a high value for a board if maximizer‘s turn or a low value for the board if minimizer‘s turn.
let us consider X as the maximizer and O as the minimizer.
1. If X wins on the board we give it a positive value of +10
2. If O wins on the board we give it a negative value of -10.
3. If no one has won or the game results in a draw then we give a value of +0.

💡 Evaluation function for the game Tic-Tac-Toe

#include <bits\stdc++.h>
using namespace std;


const int maximizerValue = 10;
const int minimizerValue = -10;

int evaluate(char grid[3][3]) {
 // check winning state in rows
 for (int row = 0; row < 3; row++) {
  if (grid[row][0] == grid[row][1] && grid[row][0] == grid[row][2]) {
   if (grid[row][0] == 'x') return maximizerValue;
   else if (grid[row][0] == 'o') return minimizerValue;
  }
 }

 // check winning state in cols
 for (int col = 0; col < 3; col++) {
  if (grid[0][col] == grid[1][col] && grid[0][col] == grid[2][col]) {
   if (grid[0][col] == 'x') return maximizerValue;
   else if (grid[0][col] == 'o') return minimizerValue;
  }
 }

 // check winning state in main diagonal
 if (grid[0][0] == grid[1][1] && grid[0][0] == grid[2][2]) {
  if (grid[0][0] == 'x') return maximizerValue;
  else if (grid[0][0] == 'o') return minimizerValue;
 }

 // check winning state in anti diagonal
 if (grid[0][2] == grid[1][1] && grid[1][1] == grid[2][0]) {
  if (grid[0][2] == 'x') return maximizerValue;
  else if (grid[0][2] == 'o') return minimizerValue;
 }

 return 0; // tie state
}


int main() {
 // x for maximizer player
 // o for minimizer player
 // _ empty cell
 char board[3][3] =
 {
  { 'x', '_', 'o' },
  { '_', 'x', 'o' },
  { '_', '_', 'x' }
 };
 int evalValue = evaluate(board);
 cout << evalValue << endl;
}

🏆 Finding the Best Move

We shall be introducing a new function called findBestMove(). This function evaluates all the available moves using minimax() and then returns the best move the maximizer can make

function findBestMove(board):
    bestMove = NULL
    for each move in board :
        if current move is better than bestMove
            bestMove = current move
    return bestMove

To check whether or not the current move is better than the best move we take the help of minimax() function which will consider all the possible ways the game can go and returns the best value for that move, assuming the opponent also plays optimally

function minimax(board, depth, isMaximizingPlayer):

    if current board state is a terminal state :
        return value of the board
    if isMaximizingPlayer :
        bestVal = -INFINITY
        for each move in board :
            value = minimax(board, depth+1, false)
            bestVal = max( bestVal, value)
        return bestVal

    else :
        bestVal = +INFINITY
        for each move in board :
            value = minimax(board, depth+1, true)
            bestVal = min( bestVal, value)
        return bestVal

To check whether the game is over and to make sure there are no moves left we use isMovesLeft() function

function isMovesLeft(board):
   for each cell in board:
      if current cell is empty:
         return true
   return false

🤖 Let's make our AI smarter

Even though the following AI plays perfectly, it might choose to make a move which will result in a slower victory or a faster loss. Lets take an example and explain it.
Assume that there are 2 possible ways for X to win the game from a give board state.
- Move A : X can win in 2 move
- Move B : X can win in 4 moves
Our evaluation function will return a value of +10 for both moves A and B. Even though the move A is better because it ensures a faster victory, our AI may choose B sometimes.
To overcome this problem we subtract the depth value from the evaluated score. This means that in case of a victory it will choose a the victory which takes least number of moves and in case of a loss it will try to prolong the game and play as many moves as possible. So the new evaluated value will be
- Move A will have a value of +10 – 2 = 8
- Move B will have a value of +10 – 4 = 6

#include <bits\stdc++.h>
using namespace std;


const int maximizerValue = 10;
const int minimizerValue = -10;

const char player = 'x';
const char ai = 'o';


struct Move {
	int row, col;
};

int evaluate(char grid[3][3]) {
	// check winning state in rows
	for (int row = 0; row < 3; row++) {
		if (grid[row][0] == grid[row][1] && grid[row][0] == grid[row][2]) {
			if (grid[row][0] == 'x') return maximizerValue;
			else if (grid[row][0] == 'o') return minimizerValue;
		}
	}

	// check winning state in cols
	for (int col = 0; col < 3; col++) {
		if (grid[0][col] == grid[1][col] && grid[0][col] == grid[2][col]) {
			if (grid[0][col] == 'x') return maximizerValue;
			else if (grid[0][col] == 'o') return minimizerValue;
		}
	}

	// check winning state in main diagonal
	if (grid[0][0] == grid[1][1] && grid[0][0] == grid[2][2]) {
		if (grid[0][0] == 'x') return maximizerValue;
		else if (grid[0][0] == 'o') return minimizerValue;
	}

	// check winning state in anti diagonal
	if (grid[0][2] == grid[1][1] && grid[1][1] == grid[2][0]) {
		if (grid[0][2] == 'x') return maximizerValue;
		else if (grid[0][2] == 'o') return minimizerValue;
	}

	return 0; // tie state
}


bool isMoveLeft(char board[3][3]) {
	for (int row = 0; row < 3; row++) {
		for (int col = 0; col < 3; col++) {
			if (board[row][col] == '_') return true;
		}
	}
	return false;
}


int minimax(char board[3][3], int depth, int isMazimizer) {
	int score = evaluate(board);
	if (score == maximizerValue) return maximizerValue;
	if (score == minimizerValue) return minimizerValue;
	if (!isMoveLeft(board)) return 0;

	// If this maximizer's move
	if (isMazimizer)
	{
		int best = -1000;
		// Traverse all cells
		for (int row = 0; row<3; row++)
		{
			for (int col = 0; col<3; col++)
			{
				// Check if cell is empty
				if (board[row][col] == '_')
				{
					// Make the move
					board[row][col] = player;

					// Call minimax recursively and choose
					// the maximum value
					best = max(best,
						minimax(board, depth + 1, !isMazimizer));

					// Undo the move
					board[row][col] = '_';
				}
			}
		}
		return best;
	}

	// If this minimizer's move
	else
	{
		int best = 1000;

		// Traverse all cells
		for (int row = 0; row<3; row++)
		{
			for (int col = 0; col<3; col++)
			{
				// Check if cell is empty
				if (board[row][col] == '_')
				{
					// Make the move
					board[row][col] = ai;

					// Call minimax recursively and choose
					// the minimum value
					best = min(best,
						minimax(board, depth + 1, !isMazimizer));

					// Undo the move
					board[row][col] = '_';
				}
			}
		}
		return best;
	}
}


Move findBestMove(char board[3][3]) {
	int bestVal = INT_MIN;
	Move bestMove;
	bestMove.row = -1;
	bestMove.col = -1;

	for (int row = 0; row < 3; row++) {
		for (int col = 0; col < 3; col++) {
			// Check if cell is empty
			if (board[row][col] == '_') {
				// Make the move
				board[row][col] = player;

				int moveValue = minimax(board, 0, false);
				if (moveValue > bestVal) {
					bestVal = moveValue;
					bestMove.row = row;
					bestMove.col = col;
				}
			}
		}
	}
	cout << "The value of the best move is : " << bestVal << endl;
	return bestMove;
}


int main() {
	// x for maximizer player
	// o for minimizer player
	// _ empty cell
	char board[3][3] =
	{
		{ 'x', 'o', 'x' },
		{ 'o', 'o', 'x' },
		{ '_', '_', '_' }
	};
	Move bestMove = findBestMove(board);

	cout << "The Optimal Move is : " << endl;
	cout << bestMove.row << ' ' << bestMove.col << endl;
}

This image depicts all the possible paths that the game can take from the root board state. It is often called the Game Tree

abdallahhemdan / minimax Goto Github PK

minimax's Introduction

🎯 Minimax Algorithm

📜 Explanation

👩‍💻 Get Maximizer value

🔎 Introduction to Evaluation Function

💡 Evaluation function for the game Tic-Tac-Toe

🏆 Finding the Best Move

🤖 Let's make our AI smarter

🥇 Unbeatable tic tac toe using Minimax algorithm

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