Alpha-beta pruning for Minimax

Question

I have spent a whole day trying to implement minimax without really understanding it. Now, , I think I understand how minimax works, but not alpha-beta pruning.

Thi

Answer 1

You don't need to evaluate the entire subtree of a node to decide its value. Alpha Beta Pruning uses two dynamically computed bounds alpha and beta to bound the values that nodes can take.

Alpha is the minimum value that the max player is guaranteed (regardless of what the min player does) through another path through the game tree. This value is used to perform cutoffs (pruning) at the minimizing levels. When the min player has discovered that the score of a min node would necessarily be less than alpha, it need not evaluate any more choices from that node because the max player already has a better move (the one which has value alpha).

Beta is the maximum value that the min player is guaranteed and is used to perform cutoffs at the maximizing levels. When the max player has discovered that the score of a max node would necessarily be greater than beta, it can stop evaluating any more choices from that node because the min player would not allow it to take this path since the min player already has a path that guarantees a value of beta.

I've written a detailed explanation of Alpha Beta Pruning, its pseudocode and several improvements: http://kartikkukreja.wordpress.com/2014/06/29/alphabetasearch/