How Minimax and Alpha-Beta Pruning Power Board Game AI in a Browser

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A developer has built an interactive browser-based tool that visualizes how minimax and alpha-beta pruning algorithms work in classic board game AI. Minimax works by searching a game's decision tree, assuming both players always make their best moves, and selecting the move with the highest guaranteed outcome. Alpha-beta pruning speeds this up by discarding branches that cannot influence the final decision, reducing nodes searched by up to 93% in tests. For example, a full-depth Tic-Tac-Toe opening move drops from 549,945 nodes to just 36,528, resolving in roughly 0.3 milliseconds on the client side. The same core approach underpins AI opponents across games from Tic-Tac-Toe to Chess, with each game adding its own scoring methods and search optimizations to handle larger decision trees.

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