Game Playing Algorithms are a class of AI algorithms used to determine the best possible move for a player in a competitive environment (games). These algorithms simulate different game states to find a strategy that maximizes the player’s chance of winning while minimizing the opponent’s chances.
They are used in:
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Turn-based games like Chess, Tic-Tac-Toe, Checkers, etc.
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Often based on adversarial search and minimax strategy.
Key Concepts
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Two-player game (MAX vs MIN player)
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Game Tree – Represents all possible moves from a current state.
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Minimax Algorithm – Chooses the move that maximizes the player’s minimum gain (assuming opponent plays optimally).
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Alpha-Beta Pruning – Optimizes minimax by eliminating branches that don’t affect the final decision.