Minimax explained for an idiot

Question

I've wasted my entire day trying to use the minimax algorithm to make an unbeatable tictactoe AI. I missed something along the way (brain fried).

I'm not looking for code here, just a better explanation of where I went wrong.

Here is my current code (the minimax method always returns 0 for some reason):

from copy import deepcopy


class Square(object):
    def __init__(self, player=None):
        self.player = player
    
    @property
    def empty(self):
        return self.player is None


class Board(object):
    winning_combos = (
        [0, 1, 2], [3, 4, 5], [6, 7, 8], [0, 3, 6], [1, 4, 7], [2, 5, 8],
        [0, 4, 8], [2, 4, 6],
    )
    
    def __init__(self, squares={}):
        self.squares = squares
        for i in range(9):
            if self.squares.get(i) is None:
                self.squares[i] = Square()
    
    @property
    def available_moves(self):
        return [k for k, v in self.squares.iteritems() if v.empty]
    
    @property
    def complete(self):
        for combo in self.winning_combos:
            combo_available = True
            for pos in combo:
                if not pos in self.available_moves:
                    combo_available = False
            if combo_available:
                return self.winner is not None
        return True
    
    @property
    def player_won(self):
        return self.winner == 'X'
    
    @property
    def computer_won(self):
        return self.winner == 'O'
    
    @property
    def tied(self):
        return self.complete == True and self.winner is None
    
    @property
    def winner(self):
        for player in ('X', 'O'):
            positions = self.get_squares(player)
            for combo in self.winning_combos:
                win = True
                for pos in combo:
                    if pos not in positions:
                        win = False
                if win:
                    return player
        return None
    
    @property
    def heuristic(self):
        if self.player_won:
            return -1
        elif self.tied:
            return 0
        elif self.computer_won:
            return 1
    
    def get_squares(self, player):
        return [k for k,v in self.squares.iteritems() if v.player == player]
    
    def make_move(self, position, player):
        self.squares[position] = Square(player)
    
    def minimax(self, node, player):
        if node.complete:
            return node.heuristic
        a = -1e10000
        for move in node.available_moves:
            child = deepcopy(node)
            child.make_move(move, player)
            a = max([a, -self.minimax(child, get_enemy(player))])
        return a


def get_enemy(player):
    if player == 'X':
        return 'O'
    return 'X'

Answer 1

Step 1: Build your game tree

Starting from the current board generate all possible moves your opponent can make. Then for each of those generate all the possible moves you can make. For Tic-Tac-Toe simply continue until no one can play. In other games you'll generally stop after a given time or depth.

This looks like a tree, draw it yourself on a piece of paper, current board at top, all opponent moves one layer below, all your possible moves in response one layer below etc.

Step 2: Score all boards at the bottom of the tree

For a simple game like Tic-Tac-Toe make the score 0 if you lose, 50 tie, 100 win.

Step 3: Propagate the score up the tree

This is where the min-max come into play. The score of a previously unscored board depends on its children and who gets to play. Assume both you and your opponent always choose the best possible move at the given state. The best move for the opponent is the move that gives you the worst score. Likewise, your best move is the move that gives you the highest score. In case of the opponent's turn, you choose the child with the minimum score (that maximizes his benefit). If it is your turn you assume you'll make the best possible move, so you choose the maximum.

Step 4: Pick your best move

Now play the move that results in the best propagated score among all your possible plays from the current position.

Try it on a piece of paper, if starting from a blank board is too much for you start from some advanced Tic-Tac-Toe position.

Using recursion: Very often this can be simplified by using recursion. The "scoring" function is called recursively at each depth and depending on whether or not the depth is odd or even it select max or min respectively for all possible moves. When no moves are possible it evaluates the static score of the board. Recursive solutions (e.g. the example code) can be a bit trickier to grasp.

Answer 2

As you already know the idea of Minimax is to deep search for the best value, assuming the opponent will always play the move with the worst value (worst for us, so best for them).

The idea is, you will try to give a value to each position. The position where you lose is negative (we don't want that) and the position where you win is positive. You assume you will always try for the highest-value position, but you also assume the opponent will always aim at the lowest-value position, which has the worst outcome for us, and the best for them (they win, we lose). So you put yourself in their shoes, try to play as good as you can as them, and assume they will do that.
So if you find out you have possible two moves, one giving them the choice to win or to lose, one resulting in a draw anyway, you assume they will go for the move that will have them win if you let them do that. So it's better to go for the draw.

Now for a more "algorithmic" view.

Imagine your grid is nearly full except for two possible positions.
Consider what happens when you play the first one :
The opponent will play the other one. It's their only possible move so we don't have to consider other moves from them. Look at the result, associate a resulting value (+∞ if won, 0 if draw, -∞ if lost : for tic tac toe you can represent those as +1 0 and -1).
Now consider what happens when you play the second one :
(same thing here, opponent has only one move, look at the resulting position, value the position).

You need to choose between the two moves. It's our move, so we want the best result (this is the "max" in minimax). Choose the one with the higher result as our "best" move. That's it for the "2 moves from end" example.

Now imagine you have not 2 but 3 moves left.
The principle is the same, you want to assign a value to each of your 3 possible moves, so that you can choose the best.
So you start by considering one of the three moves.
You are now in the situation above, with only 2 possible moves, but it's the opponent's turn. Then you start considering one of the possible moves for the opponent, like we did above. Likewise, you look at each of the possible moves, and you find an outcome value for both of them. It's the opponent move, so we assume they will play the "best" move for them, the one with the worst turnout for us, so it's the one with the lesser value (this is the "min" in minimax). Ignore the other one ; assume they will play what you found was best for them anyway. This is what your move will yield, so it's the value you assign to the first of your three moves.

Now you consider each of your other possible 2 moves. You give them a value in the same manner. And from your three moves, you choose the one with the max value.

Now consider what happens with 4 moves. For each of your 4 moves, you look what happens for the 3 moves of your opponent, and for each of them you assume they will choose the one that gives you the worst possible outcome of the best of the 2 remaining moves for you.

You see where this is headed. To evaluate a move n steps from the end, you look at what may happen for each of the n possible moves, trying to give them a value so that you can pick the best. In the process, you will have to try to find the best move for the player that plays at n-1 : the opponent, and choose the step with the lesser value. In the process of evaluating the n-1 move, you have to choose between the possible n-2 moves, which will be ours, and assume we will play as well as we can at this step. Etc.

This is why this algorithm is inherently recursive. Whatever n, at step n you evaluate all possible steps at n-1. Rinse and repeat.

For tic-tac-toe todays machines are far powerful enough to compute all possible outcomes right off from the start of the game, because there are only a few hundred of them. When you look to implement it for a more complex game, you will have to stop computing at some point because it will take too long. So for a complex game, you will also have to write code that decides whether to continue looking for all possible next moves or to try to give a value to the position now and return early. It means you will also have to compute a value for position that is not final - for example for chess you would take into account how much material each opponent has on the board, the immediate possibilities of check without mate, how many tiles you control and all, which makes it not trivial.