The Minimax Algorithm
The Minimax algorithm operates on the principle of minimizing the possible loss for a worst-case scenario. It assumes that both players are playing optimally. The algorithm recursively evaluates all possible moves, constructing a game tree where:
- Max nodes represent the current player’s move, aiming to maximize their advantage.
- Min nodes represent the opponent’s move, aiming to minimize the current player’s advantage.
Limitations of Minimax
While effective, the Minimax algorithm can be computationally intensive, particularly for games with large search spaces like chess. The time complexity of Minimax is [Tex]O(b^d)[/Tex], where b is the branching factor and d is the depth of the tree. This exponential growth makes it impractical for deep searches without optimization techniques like Alpha-Beta pruning.
Alpha-Beta pruning in Adversarial Search Algorithms
In artificial intelligence, particularly in game playing and decision-making, adversarial search algorithms are used to model and solve problems where two or more players compete against each other. One of the most well-known techniques in this domain is alpha-beta pruning.
This article explores the concept of alpha-beta pruning, its implementation, and its advantages and limitations.
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