An accessible introduction to the Monte Carlo Method applied to game AI, using Four-in-a-Row (Connect Four) as a practical example. Instead of building a large move tree like chess engines do, the approach runs thousands of random game simulations from the current board state and picks the move that wins most often. The post explains the space efficiency advantage over tree-based search, demonstrates the technique with an interactive Flash game, and honestly discusses its weakness: the AI assumes the opponent plays randomly, so it can miss obvious winning moves for the human. Monte Carlo Tree Search is briefly mentioned as a more advanced evolution of the idea.
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