An algorithm chooses the best move out of some or all possible moves. It usually works by thinking what would happen if a move is done. It does that by examining the heuristic value of each board which results after a move.

First some graph theory: a node is a board state. The children of this node are all boards that can be reached in one move.


Minimax is an algorithm for games with more as one player. It searches the states beneath the current one to a current depth. This depth is called the ply depth, and the computer "looks ahead" for this many moves.

Minimax tries to maximize the advantage for the current player, while minimizing the advantage for the other player. It assumes that the other player does the same.

When it is black's turn, he can choose the best move out of all possible moves. The moves resulting in bad states are therefore not relevant. However, when white is moving, it will probably pick the move that is best for him and hence, very bad for black. Then the good moves (for black, that is) don't matter as much.

In minimax, all heuristic values of the nodes on a certain ply depth are evaluated. These values are then traversed to the top in a particular way: if in this ply it is black's turn, the heuristic values of a node is the maximum of the node's below. If white is moving in this ply, the heuristic values of the nodes are the minimum of the node's below.

For all leaves (nodes on the maximum search depth):
h(n) = h1(n)
where h1(n) is a certain heuristic function for state n.
For all nodes where it is black's turn:
h(n) = MAX(child1, child2, ..., childn)
For all nodes where it is white's turn:
h(n) = MIN(child1, child2, ..., childn)

With the functions above, all board states have a heuristic value and the best move can be chosen.

A disadvantage of the minimax algorithm is that each board state has to be visited twice: one time to find its children and a second time to evaluate the heuristic value.

Alpha-beta pruning

Alpha-beta pruning is a optimized form of minimax. It is almost the same, except that it searches the state space in a depth-first search. This enables it to cleverly skip parts of the search tree.

For example, it could be that the computer has to calculate the following in order to evaluate a heuristic value for a node:
MIN(3, MAX(5, a))
Here the variable a is unknown, but that is not really a problem. MAX(5, a) is at least 5, and MIN(3, b) where b is 5 or more is just 3. Alpha-beta pruning thus skips the tree under a.