Adversarial Search and Game Theory Applications in AI
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Imagine an AI playing a game against you. It cannot simply choose a move that looks good — it must think about how you might respond. This is Adversarial Search: the AI explores possible moves and the responses that could follow. In Tic-Tac-Toe, every move creates a new game state, forming a Game Tree that lets the AI look several moves ahead. Minimax then has the AI act as MAX, seeking the highest score, while the opponent, MIN, tries to lower it. But searching every move can be expensive. Alpha-Beta Pruning removes branches that cannot affect the outcome, letting the AI reach the same decision much faster.
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