Watch a Game AI Think: Minimax and Alpha-Beta, in a Browser Tab
Every "AI" opponent in a board game — Tic-Tac-Toe, Connect 4, Checkers, Othello, Chess — tends to run the same idea: search the game tree, assume the opponent plays their best, and pick the move with the best guaranteed outcome. That idea is minimax , and alpha-beta pruning is what makes it fast enough to run in a browser tab with no backend. I built an interactive version where you can step…
Minimax and alpha-beta pruning are techniques employed by AI opponents in board games like Tic-Tac-Toe, Connect 4, Checkers, Othello, and Chess. Minimax works by searching the game tree, assuming the opponent plays optimally, and selecting the move with the best guaranteed outcome. Alpha-beta pruning optimizes this process, reducing the number of nodes evaluated, allowing the AI to think deeply within a short time frame.
The process involves assigning scores to finished positions, with positive 1 for AI wins, negative 1 for human wins, and 0 for draws. The algorithm alternates between maximizing and minimizing scores as it traverses the tree, determining the optimal move. Alpha-beta pruning introduces two bounds, alpha and beta, which help prune branches of the tree that are no longer necessary, leading to significant reductions in the number of nodes evaluated.
For instance, a search that looks eight moves ahead in chess would require evaluating approximately 2.3 trillion positions without pruning, but alpha-beta pruning can cut this down by up to 93%. The same fundamental idea applies to various board games, with slight modifications to accommodate differences in board size, scoring methods, and search optimizations.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
