Parallelization of Alpha-Beta Pruning Algorithm For Enhancing the Two Player Games
The game application which requires extensive searching requires an effective and faster technique for the same.
The speed of game playing also depends on the rate at which the decision making process gets executed. Alpha-Beta pruning
is one of the most powerful and fundamental MinMax search improvements. The pruning helps to reduce the number of
search, which further contributes to speed of the decision making at every instance of game playing. In this paper, we would
like to further improve on execution time, by parallelizing the Alpha-Beta algorithm. To analyze the performance, we have
considered "Stacked matrix games" for two players such as tic-tac-toe, checker board and chess. The result shows an average
speed-up of 3.03 due to parallelization of Alpha-Beta pruning using Open MP.
Index terms- Alpha-Beta Pruning, MinMax Search, Game playing, Parallelization, OpenMP.