This and the recursive nature of the algorithm help to increase the system overheads of the algorithm. One of the ways developed to help improve the efficiency of the Minimax was to introduce a way of cutting off or pruning any branches or nodes that are obviously worse than any of those that have previously been visited. Although this only saves us one node in this situation if this technique could have just as easily have saved us from examining a whole branch if the tree had been any larger. The addition of Alpha-Beta pruning reduces the number of nodes visited by assign Alpha values and Beta values as well as the normal scores to the nodes. Now when searching the game tree we can examine the alpha and beta values to see whether the all of the child nodes are going to be worth searching.
Alpha then return Node.
Alpha, Value if Node. Alpha then return Node. The problem with the fixed -depth method of searching the tree can be seen in the above examples in that of the tree was only searched to the first level the results were drastically different to searches that went just one level deeper. To try to overcome this to add an additional search called a quiescence search this search tries to evaluate any special or tactical moves that might actually be in operation at the level that the occurs. This is useful in games that include lots of tactical moves like chess.