Example of Alpha-Beta Pruning
Consider a simple game tree where the branching factor is 2 and the depth is 3.
- Start at the root (Max node). Initialize alpha to -∞ and beta to ∞.
- Traverse to the first child (Min node) and evaluate its children (Max nodes).
- For each Max node, traverse and evaluate its children (leaf nodes). Update alpha and beta accordingly.
- Prune branches where alpha ≥ beta for Max nodes or beta ≤ alpha for Min nodes.
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.
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