Enhancing AI Strategy in Checkers through Minimax and Alpha-Beta Pruning Techniques

This paper focuses on artificial intelligence in the game of Checkers and thus looks to further advance strategic decision-making within AI game agents. This is a problem arising in developing computationally efficient AI to scan complex board states so that optimum moves may be selected. Alpha-Beta Pruning applied with the Minimax algorithm reduces the amount of computing that needs to be done when assessing any move, thus enabling its speed and strategic playing capabilities to be increased. Results will come out of experiments regarding the proficiency of AI in competitive environments with the benefits of more efficient play, strategic placement, and adaptability. In summing this up, this way leads toward the improvement in advanced algorithms to enhance the strategic capabilities of AI as in games, thus toward generating responsive high-performance game agents.