Minimax or Maximin? – Becoming Human: Artificial Intelligence Magazine

Minimax, as the name suggest, is a method in decision theory for minimizing the maximum loss. Alternatively, it can be thought of as maximizing the minimum gain, which is also know as Maximin.

It all started from a two player zero-sum game theory, covering both the cases where players take alternate moves and those where they made simultaneous moves. It has also been extended to more complex games and to general decision making in the presence of uncertainty.

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Zero-sum

In the above explanation, it has been mentioned that the minimax algorithms started off with the concept of zero-sum. Well then, what is zero-sum?

Zero sum games are a special case of constant sum of games, in which choices by players can neither increase nor decrease the available resources. In zero-sum games the total benefit to all players in the game, for every combination of strategies, always adds to zero(more informally, a player benefits only at the expense of the other).

Poker exemplifies a zero-sum, because one wins exactly the same amount one’s opponents lose.

Non-Zero-Sum

Many games studied by game theorists are non-zero-sum games, because of some outcomes have net results greater or less than zero.

Informally, in non-zero-sum games, a gain by one player does not necessarily correspond with a loss by another.

“Maximin” is a term commonly used for non-zero-sum games to describe the strategy which maximizes one’s own minimum payoff.

Now, getting back to the minimax algorithm

The minimax algorithm is a recursive algorithm for choosing the next move in an n-player game, usually a two-player game.

• A value is associated with each position or state of the game.
• This value is computed by means of a position evaluation function and it indicates how good it would be for a player to reach that position.
• The player then makes the move that maximizes the minimum value of the position results from the opponent’s possible following moves.
• If it is A’s turn to move, A gives a value to each of his legal moves.

Pseudo-code of Minimax is as follows:

function minimax(node,depth)
if node is a terminal node or depth = 0
return the heuristic value of node
if the adversary is to play at node
let beta := +∞
for each child of node
beta:=min(beta,minimax(child,depth+1))
return beta
else {we are to play at node}
let alpha := -∞
for each child of node
alpha:=max(alpha,minimax(child,depth+1))
return alpha

Suppose the game being played only has a maximum of two possible moves per player each turn. The algorithm generates the tree below, where the circles represent the moves of the player running the algorithm(maximizing player), and squares represent the moves of the opponent(minimizing player). Because of the limitation of computation resources, the tree is limited to a look-ahead of 4 moves

Alpha-Beta Pruning

The minimax algorithm is a way of finding an optimal move in a two player game. Alpha-beta pruning is a way of finding the optimal minimax solution while avoiding searching subtrees of moves which won’t be selected. In the search tree for a two-player game, there are two kinds of nodes, nodes representing your moves and nodes representing your opponent’s moves. Nodes representing your moves are generally drawn as squares (or possibly upward pointing triangles):

These are also called MAX nodes. The goal at a MAX node is to maximize the value of the subtree rooted at that node. To do this, a MAX node chooses the child with the greatest value, and that becomes the value of the MAX node.

Nodes representing your opponent’s moves are generally drawn as circles (or possibly as downward pointing triangles):

These are also called MIN nodes. The goal at a MIN node is to minimize the value of the subtree rooted at that node. To do this, a MIN node chooses the child with the least (smallest) value, and that becomes the value of the MIN node.

Alpha-beta pruning gets its name from two bounds that are passed along during the calculation, which restrict the set of possible solutions based on the portion of the search tree that has already been seen. Specifically,

Beta is the minimum upper bound of possible solutions

Alpha is the maximum lower bound of possible solutions

Thus, when any new node is being considered as a possible path to the solution, it can only work if:

where N is the current estimate of the value of the node.

To visualize this, we can use a number line. At any point in time, alpha and beta are lower and upper bounds on the set of possible solution values, like so:

As the problem progresses, we can assume restrictions about the range of possible solutions based on min nodes (which may place an upper bound) and max nodes (which may place a lower bound). As we move through the search tree, these bounds typically get closer and closer together:

This convergence is not a problem as long as there is some overlap in the ranges of alpha and beta. At some point in evaluating a node, we may find that it has moved one of the bounds such that there is no longer any overlap between the ranges of alpha and beta:

At this point, we know that this node could never result in a solution path that we will consider, so we may stop processing this node. In other words, we stop generating its children and move back to its parent node. For the value of this node, we should pass to the parent the value we changed which exceeded the other bound.

To demonstrate minimax with alpha-beta pruning, we use the following minimax tree as an example:

For the purposes of this minimax tutorial, this tree is equivalent to the list representation:

(
(((3 17) (2 12)) ((15)  (25 0)))
(((2 5) (3)) ((2 14)))
)

As an aside, if this were a real-world minimax problem, you wouldn’t have the tree all pre-generated like that. If you’ve already wasted the space to generate all the states and the time to calculate all the evaluation values at the determined depth, then the time to do minimax is negligible. The alpha-beta pruning is meant to avoid having to generate all the states and calculate all the evaluation functions. Check this page out if you want some notes on how a real world version of minimax with alpha-beta pruning would vary from your version.

For the rest of this example, I’ll show only the part of the tree that’s been evaluated so far or is currently being evaluated. I’ll also describe the behavior as if this were a situation where you were generating the child states instead of just traversing the tree that’s given to you. In that spirit, we’re trying to find the best move by looking ahead two full moves (i.e. two moves each my me and my opponent). Thus we will go to a depth of 4 in the tree, then evaluate the state.

At the start of the problem, you see only the current state (i.e. the current position of pieces on the game board). As for upper and lower bounds, all you know is that it’s a number less than infinity and greater than negative infinity. Thus, here’s what the initial situation looks like:

which is equivalent to

Since the bounds still contain a valid range, we start the problem by generating the first child state, and passing along the current set of bounds. At this point our search looks like this:

We’re still not down to depth 4, so once again we generate the first child node and pass along our current alpha and beta values:

And one more time:

When we get to the first node at depth 4, we run our evaluation function on the state, and get the value 3. Thus we have this:

We pass this node back to the min node above. Since this is a min node, we now know that the minimax value of this node must be less than or equal to 3. In other words, we change beta to 3.

Note that the alpha and beta values at higher levels in the tree didn’t change. When processing actually returns to those nodes, their values will be updated. There is no real gain in proagating the values up the tree if there is a chance they will change again in the future. The only propagation of alpha and beta values is between parent and child nodes.

If we plot alpha and beta on a number line, they now look like this:

Next we generate the next child at depth 4, run our evaluation function, and return a value of 17 to the min node above:

Since this is a min node and 17 is greater than 3, this child is ignored. Now we’ve seen all of the children of this min node, so we return the beta value to the max node above. Since it is a max node, we now know that it’s value will be greater than or equal to 3, so we change alpha to 3:

Notice that beta didn’t change. This is because max nodes can only make restrictions on the lower bound. Further note that while values passed down the tree are just passed along, they aren’t passed along on the way up. Instead, the final value of beta in a min node is passed on to possibly change the alpha value of its parent. Likewise the final value of alpha in a max node is passed on to possibly change the beta value of its parent.

At the max node we’re currently evaluating, the number line currently looks like this:

We generate the next child and pass the bounds along:

Since this node is not at the target depth, we generate its first child, run the evaluation function on that node, and return it’s value:

Since this is a min node, we now know that the value of this node will be less than or equal to 2, so we change beta to 2:

The number line now looks like this:

As you can see from the number line, there is no longer any overlap between the regions bounded by alpha and beta. In essense, we’ve discovered that the only way we could find a solution path at this node is if we found a child node with a value that was both greater than 3 and less than 2. Since that is impossible, we can stop evaluating the children of this node, and return the beta value (2) as the value of the node.

Admittedly, we don’t know the actual value of the node. There could be a 1 or 0 or -100 somewhere in the other children of this node. But even if there was such a value, searching for it won’t help us find the optimal solution in the search tree. The 2 alone is enough to make this subtree fruitless, so we can prune any other children and return it.

That’s all there is to beta pruning!

Back at the parent max node, our alpha value is already 3, which is more restrictive than 2, so we don’t change it. At this point we’ve seen all the children of this max node, so we can set its value to the final alpha value:

Now we move on to the parent min node. With the 3 for the first child value, we know that the value of the min node must be less than or equal to 3, thus we set beta to 3:

Now the graph of alpha and beta on a number line looks like this:

Since we still have a valid range, we go on to explore the next child. We generate the max node…

… it’s first child min node …

… and finally the max node at the target depth. All along this path, we merely pass the alpha and beta bounds along.

At this point, we’ve seen all of the children of the min node, and we haven’t changed the beta bound. Since we haven’t exceeded the bound, we should return the actual min value for the node. Notice that this is different than the case where we pruned, in which case you returned the beta value. The reason for this will become apparent shortly.

Now we return the value to the parent max node. Based on this value, we know that this max node will have a value of 15 or greater, so we set alpha to 15:

Now the graph of alpha and beta on a number line looks like this:

Once again the alpha and beta bounds have crossed, so we can prune the rest of this node’s children and return the value that exceeded the bound (i.e. 15). Notice that if we had returned the beta value of the child min node (3) instead of the actual value (15), we wouldn’t have been able to prune here.

Now the parent min node has seen all of it’s children, so it can select the minimum value of it’s children (3) and return.

Finally we’ve finished with the first child of the root max node. We now know our solution will be at least 3, so we set the alpha value to 3 and go on to the second child.

Passing the alpha and beta values along as we go, we generate the second child of the root node…

… and its first child …

… and its first child …

… and its first child. Now we are at the target depth, so we call the evaluation function and get 2:

The min node parent uses this value to set it’s beta value to 2:

Now the graph of alpha and beta on a number line looks like this:

Once again we are able to prune the other children of this node and return the value that exceeded the bound. Since this value isn’t greater than the alpha bound of the parent max node, we don’t change the bounds.

From here, we generate the next child of the max node:

Then we generate its child, which is at the target depth. We call the evaluation function and get its value of 3.

The parent min node uses this value to set its upper bound (beta) to 3:

At this point the number line graph of alpha and beta looks like this:

In other words, at this point alpha = beta. Should we prune here? We haven’t actually exceeded the bounds, but since alpha and beta are equal, we know we can’t really do better in this subtree.

The answer is yes, we should prune. The reason is that even though we can’t do better, we might be able to do worse. Remember, the task of minimax is to find the best move to make at the state represented by the top level max node. As it happens we’ve finished with this node’s children anyway, so we return the min value 3.

The max node above has now seen all of its children, so it returns the maximum value of those it has seen, which is 3.

This value is returned to its parent min node, which then has a new upper bound of 3, so it sets beta to 3:

Now the graph of alpha and beta on a number line looks like this:

Once again, we’re at a point where alpha and beta are tied, so we prune. Note that a real solution doesn’t just indicate a number, but what move led to that number.

If you were to run minimax on the list version presented at the start of the example, your minimax would return a value of 3 and 6 terminal nodes would have been examined.

Source: http://web.cs.ucla.edu/~rosen/161/notes/alphabeta.html

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