How Alpha-Beta Pruning Works
The Alpha-Beta pruning algorithm traverses the game tree similarly to Minimax but prunes branches that do not need to be explored. The steps are discussed below:
- Initialization: Start with alpha set to negative infinity and beta set to positive infinity.
- Max Node Evaluation:
- For each child of a Max node:
- Evaluate the child node using the Minimax algorithm with Alpha-Beta pruning.
- Update alpha: [Tex]\alpha = max(\alpha, \text{child value}[/Tex].
- If alpha is greater than or equal to beta, prune the remaining children (beta cutoff).
- For each child of a Max node:
- Min Node Evaluation:
- For each child of a Min node:
- Evaluate the child node using the Minimax algorithm with Alpha-Beta pruning.
- Update beta: [Tex]β=min(β,child value)[/Tex].
- If beta is less than or equal to alpha, prune the remaining children (alpha cutoff).
- For each child of a Min node:
Alpha-Beta pruning in Adversarial Search Algorithms
In artificial intelligence, particularly in game playing and decision-making, adversarial search algorithms are used to model and solve problems where two or more players compete against each other. One of the most well-known techniques in this domain is alpha-beta pruning.
This article explores the concept of alpha-beta pruning, its implementation, and its advantages and limitations.