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In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.

In the past, game applications were proved to be inefficient compared to present day. This is mostly because of limitation of computer computation and memory space and inadequate game tree algorithms to help determine the next move. Games that require extensive searching needed a faster and more efficacious technique. Alpha-Beta Pruning is one of the most fundamental optimization technique for Minimax algorithm. Just by passing 2 extra parameters in the Minimax function, namely alpha and beta, this algorithm helps increase the speed of the game drastically. The pruning helps in eliminating the number of searches whenever there already exists a better move available in the game tree. In this paper, the effects of Alpha-Beta Pruning on the game tree are discussed by taking into account bimatrix games such as Tic-Tac-Toe and Checkers etc.