# MiniMax and AlphaBeta Search

I try to give an overview of & explain (perhaps mainly to myself) the MiniMax algorithm and its AlphaBeta pruning optimisation.

## List of Listings

Though they are quite different from each other Chess, Checkers, Go,
Othello, Connect-4 and Tic-Tac-Toe also have similarities. They are
all two-player zero sum perfect information games. The term *two-player*
just means that there must be two opposing sides (football is
considered two-player, for example). A *zero sum* game is one where an
advantage for one player is an equally large disadvantage for the
other. *Perfect information* basically rules out any game that has an
element of chance. Yatzee? Right out the window. Poker? Forget about
it. Jenga? Not even *close*.

For games that have these properties it is possible to set up a game
tree to aid in the selection of the next move. For simplicity, consider
the starting state of Tic-Tac-Toe to be the root of a tree. The root has
nine branches, each leading to a successor state. Each of these has 8
branches leading to *its* successor states and so on. Some of the paths
through the tree will end before others (a winning state is reached
before all the slots have been filled) but some paths continue until
depth 9 (or *ply* 9 in game-tree terminology), when all the slots have
been filled.

After having exhausted the search space of the game, it is easy to find
the paths that will lead to victory for either player. Knowing the path
that X can take to the fastest victory is generally of little use,
however, because O can thwart X's plans of a swift victory any time it
is her turn to move. Instead of traversing the path leading to the
fastest possible victory, X's best aim is to pick a path where her
*worst* outcome will be victory (the *best worst-case* path). The
Minimax game-tree search algorithm is designed to do just this, and
alpha-beta pruning improves on it. This article tries to explain how
both works.

## The Minimax algorithm

Let us use Tic-Tac-Toe as an example when explaining how the Minimax algorithm works. The player whose turn it is to move at the root is called Max, and all even plies in the game-tree (i.e. the states where it is Max' turn to move) are labelled accordingly. Max' opponent is called Min. These names are adapted from their actions; Max is always trying to maximise her score, Min is always trying to minimise Max' score.

To determine who won, a function to evaluate a game state is needed. The evaluation function takes a game state as its argument and should return a value indicating whether the state is a win, loss or draw for the player whose turn it is at that state. The function could for example return 1 if the state is a win, -1 if the state is a loss and zero if the state is a draw. The evaluation function is applied to all the leaf states. Leafs are game states without children, i.e. either a draw, or a win for one of the players.

After exhausting the tree and evaluating all leaf states, Minimax values are assigned to the internal states (all non-leaf states) of the game-tree. The Minimax values found in the leaf nodes are inverted and returned to their parent. Each parent picks the highest value returned to it, negates it, then returns the result to its parent, et cetera. The Minimax values returned to the root denote Max' worst outcome for each corresponding move. All that remains for Max is to pick a move where her worst outcome is a win (if such a move exists–it does not in Tic-Tac-Toe).

The following method shows how the Minimax algorithm can be implemented in Objective-C. A noticeable difference from the description above is that the Minimax value is negated on return from the recursive call instead of before the return; this is how the Minimax algorithm is normally implemented.

-(int)minimaxWithState:(id)state player:(int)player { if ([state isEndOfGame]) return [state evaluateWithPlayer:player]; int score = -1; // value for loss NSArray *moves = [state movesAvailable]; id enumerator = [moves objectEnumerator]; for (id m; m = [enumerator nextObjnextObject]; ) { id s2 = [state successorStateWithMove:m]; int sc = -[self minimaxWithState:s2 player:3 - player]; if (sc > score) score = sc; } return score; }

The next listing shows the special Minimax function applied to the root of the tree (i.e. the position for which a move is sought). In discussions of the algorithm the notion of any special treatment of the root is often omitted; it is included here for the sake of completeness. It retains the best move in addition to its score, so we can later apply it to advance the game.

-(id)minimaxRootWithState:(id)state player:(int)player { id bestmove = nil; int score = -1; // value for loss NSArray *moves = [state movesAvailable]; id enumerator = [moves objectEnumerator]; for (id m; m = [enumerator nextObject]; ) { id s2 = [state successorStateWithMove:m]; int sc = -[self minimaxWithState:s2 player:3 - player]; if (sc > score) { bestmove = m; score = sc; } } return bestmove; }

## Depth-limited Minimax

Only when the search space is sufficiently small, like in our Tic-Tac-Toe example, is it possible to exhaust it fully using the Minimax algorithm. For practical applications this is almost never the case. Computers are not powerful enough to exhaust game-trees for practical applications where game-tree search would be desired. For example, it has been claimed that the game of Chess has more states than there are atoms in the known universe. Suffice to say that waiting for a search of that magnitude to finish becomes impractical.

A simple way of ensuring that the search will terminate in a practical timespan is to set a maximum limit on the depth of the search. The following method shows how the Minimax algorithm presented earlier can be amended to unconditionally stop after reaching a certain depth. Notice that the initial value for score has changed.

-(int)minimaxWithState:(id)state player:(int)player ply:(int)ply { if (!ply || [state isEndOfGame]) return [state evaluateWithPlayer:player]; int score = -1000; // value for loss NSArray *moves = [state movesAvailable]; id enumerator = [moves objectEnumerator]; for (id m; m = [enumerator nextObject]; ) { id s2 = [state successorStateWithMove:m]; int sc = -[self minimaxWithState:s2 player:3 - player]; if (sc > score) score = sc; } return score; }

Since the search may be terminated before it has reached the leaf nodes, the end states of many paths are lost. Thus the evaluation function will have to be enhanced: it must now be able to indicate how good non-terminal states in the game-tree are, in contrast to simply determining a win, loss or draw for an end state. Instead of returning -1, 0 or 1 the evaluation function must now return a value in a certain range (say, -1000 to 1000) indicating how good the state is. Performance of depth-limited Minimax algorithms greatly depends on how well the evaluation function identifies strong states.

## Alpha-Beta pruning

In the late 50s it was realised that it was not necessary to visit all
the nodes in a game-tree to correctly deduce its Minimax value.
Uninteresting branches of the tree can be pruned away. Remember that the
Minimax algorithm produces the value of the best worst-case. Alpha-Beta
pruning terminates the search of a subtree as soon as it knows that the
worst-case for the subtree is worse than previously searched paths. The
idea is that if a path is worse than the current best path, time is not
wasted trying to find out *how* bad it is.

To accomplish the pruning mentioned above two bounds are passed to a modified Minimax algorithm. The bounds are the highest (beta) and lowest (alpha) value that can affect the Minimax value at that point, and are continually updated as the search progresses. Since the Minimax value is negated at each step, the states of the bounds must also be negated and their states switched as they are passed on to the next level. If the Minimax value returned from a path is greater than or equal to the high bound, the path is pruned. Here's an example:

-(int)alphaBetaWithState:(id)state player:(int)player ply:(int)ply alpha:(int)alpha beta:(int)beta { if (!ply || [state isEndOfGame]) return [state evaluateWithPlayer:player]; NSArray *moves = [state movesAvailable]; id enumerator = [moves objectEnumerator]; for (id m; m = [enumerator nextObject]; ) { id s2 = [state successorStateWithMove:m]; int sc = -[self alphaBetaWithState:s2 player:3 - player ply:ply-1 alpha:-beta beta:-alpha]; if (sc > alpha) alpha = sc; if (alpha >= beta) break; // prune branch. } return alpha; }

In a worst-ordered tree (where the paths are ordered so that no pruning occurs) the Alpha-Beta algorithm visits the same number of leaf nodes as Minimax. On average it performs a lot better. Given a perfectly ordered tree, where the branches are pruned as early as possible, the Alpha-Beta algorithm can search twice as deep as the Minimax algorithm in the same timespan.

*This post has been adapted from a section of my 2003 BSc Artificial Intelligence report on Generalised Game-Tree Search at the University of Westminster.*