Adversarial Search Alpha Beta Pruning

Alpha-beta pruning is an optimization technique for the minimax algorithm used in AI for decision-making in games like chess. It eliminates branches in the game tree that won’t influence the final decision, significantly improving efficiency. By keeping track of two values—alpha (best already found for the maximizer) and beta (best for the minimizer)—it avoids unnecessary evaluations, reducing computation time. This makes AI search faster without affecting the final result. In this video, we break down how alpha-beta pruning works, its benefits, and real-world applications in game AI.