Alpha Beta Pruning

Alpha-beta pruning is a search algorithm used in decision-making, game theory in AI. Alpha-beta pruning is optimization technique for minimax algorithm or you can say it is the advanced version of minimax algorithm. It is used in Games like tic-tac-toe, chess and checkers.

Alpha: In Alpha-Beta Pruning Alpha is the best choice for highest value found and the initial value of Alpha is -.

Beta: Beta is the best choice for lowest value found and the initial value of Beta is + ∞.

The technique by which, without checking each node of the Game tree we can compute the correct minmax decision, and this technique is called Pruning. This involves two threshold parameter Alpha and Beta for future expansion, so it is called Pruning.


Alpha-beta pruning searches the best path for the max player. It eliminates parts of the tree, explores a smaller number of nodes so time is reduced. It returns the same number of moves like minimax algorithm but it will prune away branches that won’t affect the final decision.
Significance of Alpha-beta Pruning

  • 1. The Game helps in practical skills
  • 2. A good exercise for to brain to sharpen it
  • 3. Used for educational simulation or psychological roles
  • 4. Helps in strategic decision making

Advantages of Alpha-Beta pruning
  • • Helps in reducing the number of nodes.
  • • Reduce the time and space complexity.
  • • Improves search and it is a fast algorithm.
  • • Advanced AI algorithm compared to min-max algorithm.

Disadvantages of Alpha-Beta Pruning
  • • It doesn’t solve all the problems associated with minimax algorithm.
  • • Requires depth limit, not feasible for most cases.
  • • Calculates the value of all legal moves.

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