how to perform minimax in matlab

The minimax algorithm is used in game theory and artificial intelligence to find the optimal moves for a player, assuming that the opponent is also playing optimally. Here's how you can implement the minimax algorithm in MATLAB:

main.m
function [ bestMove, bestScore ] = minimax( gameState, depth, isMaximizingPlayer )
% gameState - the current state of the game
% depth - the current depth of the search tree
% isMaximizingPlayer - a boolean indicating if it's the maximizing player's turn

    if depth == 0 || isGameOver(gameState)
        % If we've reached the maximum depth or the game is over, evaluate the state
        bestScore = evaluate(gameState);
        bestMove = [];
        return;
    end

    if isMaximizingPlayer
        % If it's the maximizing player's turn, find the move with the highest score
        bestScore = -Inf;
        moves = getPossibleMoves(gameState);
        for i = 1:length(moves)
            childState = applyMove(gameState, moves(i));
            [ score, ~ ] = minimax(childState, depth - 1, false);
            if score > bestScore
                bestScore = score;
                bestMove = moves(i);
            end
        end
    else
        % If it's the minimizing player's turn, find the move with the lowest score
        bestScore = Inf;
        moves = getPossibleMoves(gameState);
        for i = 1:length(moves)
            childState = applyMove(gameState, moves(i));
            [ score, ~ ] = minimax(childState, depth - 1, true);
            if score < bestScore
                bestScore = score;
                bestMove = moves(i);
            end
        end
    end

end
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In the code above, gameState represents the current state of the game, depth represents the current depth of the search tree, and isMaximizingPlayer is a boolean indicating if it's the maximizing player's turn. The getPossibleMoves, applyMove, evaluate, and isGameOver functions should be replaced with functions that implement the rules of the game. The output of the minimax function is the best move and the score of that move.

Note that the code above assumes that the maximizing player is attempting to maximize the score, and the minimizing player is attempting to minimize the score. If you're working with a game where the goal is to minimize some value (for example, the number of moves required to solve a puzzle), you'll need to modify the evaluate function to return negative scores for better states.

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