Project Overview
Our project focuses on an AI game bot utilizing the Min-Max algorithm and alpha-beta pruning to engage in strategic gameplay against human opponents. The bot consistently prevents the human player from winning by minimizing potential losses and maximizing gains. This showcases the effectiveness of these advanced algorithms in creating a challenging and competitive gaming experience, demonstrating their application in strategic decision-making.
The Min-Max algorithm, enhanced by alpha-beta pruning, is a strategic decision-making approach commonly employed in two-player turn-based games. This algorithm aims to maximize the potential gain for a player while minimizing the possible loss in a worst-case scenario. By recursively evaluating possible moves and outcomes, the algorithm creates a decision tree, allowing it to assess various game states. Alpha-beta pruning further optimizes this process by eliminating unnecessary branches in the decision tree, reducing the number of nodes evaluated. This efficiency improvement results in quicker and more informed decision-making, making the Min-Max algorithm with alpha-beta pruning particularly effective for creating intelligent and competitive game-playing bots.