What is Alpha-Beta Pruning, and how does it make Chess AI search much faster?
In this video, we focus on the core idea behind Alpha-Beta Pruning: skipping branches that are mathematically guaranteed not to affect the final decision. Using simple game-tree examples, we understand how Alpha (α) and Beta (β) are updated, when pruning happens, and why the algorithm still produces exactly the same answer as Minimax.
You’ll learn:
What Alpha and Beta represent
How pruning actually works
The condition α ≥ β
Step-by-step solved examples
Why some branches are ignored safely
How move ordering increases pruning
This is one of the most important topics in Artificial Intelligence, Chess AI, game trees, placements, and technical interviews. By the end of the video, Alpha-Beta Pruning will feel intuitive rather than something to memorize.
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