How exactly does minimax recursion work?

Question

So I was looking up Mini-max for a Tic-Tac-Toe Game, but couldn\'t understand how the recursion worked? Okay, so basically here are my questions:

1. How does mini

Answer 1

We'll use your tic-tac-toe as an example first.

• A minimax algorithm works best for games where players alternate turns, but can be adapted to games where players may make multiple moves per turn. We'll assume the former, for simplicity. In that case, you need not store 'X to move' or 'O to move' with each node, because that can just be determined by the parity of the node depth (whether I'm an even number of steps, or an odd number of steps, from the top).
• Generating possible moves from each position requires that you know whose move it is (which can be determined as before), and the rules for legal moves from a particular position. For a simple game like tic-tac-toe, given a position, it suffices to enumerate all the states that consist of a copy of the current position plus a new piece, belonging to the current player, placed at each empty square in turn. For games like Othello, you must also check each placement to ensure that it follows the rules, and update the final position according to the consequences of the rule (for Othello, flipping the colors of a bunch of pieces). In general, from each valid position you're tracking, you enumerate all the possible placings of a new piece and check to see which ones are allowed by the ruleset.
• In general, you NEVER generate the entire tree, since game tree sizes can easily exceed the storage capacity of Earth. You always set a maximum depth of iteration. A terminal node, then, is simply a node at the maximum depth, or a node from which no legal moves exist (for tic-tac-toe, a board with every square filled). You don't generate the terminal nodes beforehand; they get generated naturally during game tree construction. Tic-tac-toe is simple enough that you can generate the entire game tree, but then don't try to use your tic-tac-toe code for e.g. Othello.

Looking at your pseudocode:

• max(a, b) is any function that returns the larger of a or b. This is usually provided by a math library or similar.
• The depth is the maximum depth to which you will search.
• The heuristic value you're computing is some numerical value that describes the value of the board. For a game like tic-tac-toe, which is simple enough that you CAN enumerate the entire game tree, you can designate 1 for a board position that wins for the player doing the analysis, -1 for a board position that wins for the other player, and 0 for any inconclusive position. In general, you'll have to cook up a heuristic yourself, or use a well-accepted one.
• You generate the nodes on the fly during your analysis based on their parent nodes. Your root node is always the position from which you're doing analysis.

If you haven't worked with graphs or trees yet, I suggest you do so first; the tree primitive, in particular, is essential to this problem.

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As an answer to a comment in this thread asking for an example of determining whose turn it is for a given node, I offer this pseudo-Python:

who_started_first = None

class TreeNode:
    def __init__(self, board_position = EMPTY_BOARD, depth = 0):
        self.board_position = board_position
        self.children = []
        self.depth = depth
    def construct_children(self, max_depth):
        # call this only ONCE per node!
        # even better, modify this so it can only ever be called once per node
        if max_depth > 0:

            ### Here's the code you're actually interested in.
            if who_started_first == COMPUTER:
                to_move = (COMPUTER if self.depth % 2 == 0 else HUMAN)
            elif who_started_first == HUMAN:
                to_move = (HUMAN if self.depth % 2 == 0 else COMPUTER)
            else:
                raise ValueError('who_started_first invalid!')

            for position in self.board_position.generate_all(to_move):
                # That just meant that we generated all the valid moves from the
                # currently stored position. Now we go through them, and...
                new_node = TreeNode(position, self.depth + 1)
                self.children.append(new_node)
                new_node.construct_children(max_depth - 1)

Each node is capable of keeping track of its absolute depth from the 'root' node. When we try to determine how we should generate board positions for the next move, we check to see whose move it is based on the parity of our depth (the result of self.depth % 2) and our record of who moved first.