minimax logic in matlab

The minimax algorithm is a common technique used in artificial intelligence and game theory to determine the best move for a player at a particular point in a game. In Matlab, the minimax algorithm can be implemented using recursion. The basic idea is to evaluate all possible moves that can be made from the current state of the game, and assign a score to each possible state based on how favorable it is for the current player. The algorithm then chooses the move that leads to the state with the highest score if the current player is the maximizing player, or the lowest score if the current player is the minimizing player.

Here's an implementation of the minimax algorithm in Matlab for a two-player game:

main.m
function [bestScore, bestMove] = minimax(board, maximizingPlayer)
    % check if game is over and return score
    if game_is_over(board)
        bestScore = evaluate_score(board);
        bestMove = [];
        return;
    end

    % initialize variables for storing best score and move
    if maximizingPlayer
        bestScore = -inf;
    else
        bestScore = inf;
    end
    bestMove = [];

    % evaluate all possible moves
    for i = 1:num_possible_moves(board)
        % simulate the move
        newBoard = make_move(board, i, maximizingPlayer);

        % use recursion to evaluate the new state
        [score, ~] = minimax(newBoard, ~maximizingPlayer);

        % update best score and move
        if maximizingPlayer
            if score > bestScore
                bestScore = score;
                bestMove = i;
            end
        else
            if score < bestScore
                bestScore = score;
                bestMove = i;
            end
        end
    end
end
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In this implementation, board is a data structure representing the current state of the game, maximizingPlayer is a boolean flag indicating whether the current player is the maximizing player or not, num_possible_moves() returns the number of possible moves that can be made from the current state, make_move() simulates a move by the current player, game_is_over() checks if the game has ended, and evaluate_score() returns a score for the current state. bestScore and bestMove are updated during the recursive calls to minimax(), and are returned by the function once all possible moves have been evaluated.