Cledersonbc/tic-tac-toe-minimax

Implements recursive game tree search with alternating max/min player evaluation to find optimal moves in zero-sum games, scoring terminal states as win (+1), loss (-1), or draw (0). The algorithm exhaustively explores all valid board positions until reaching game-over conditions, then backtracks to select moves that maximize the current player's score. Includes Python implementation with interactive web demo and demonstrates how full game trees (549,946 nodes for tic-tac-toe) enable perfect play, while noting alpha-beta pruning optimizations for complex games like chess.

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