Improving Alpha-Beta Pruning Efficiency in Chess Engines

Alpha-Beta Pruning is a critical optimization technique in chess engines, helping them make fast, accurate decisions by reducing unnecessary calculations. This paper focuses on enhancing its efficiency to improve search depth and speed with-out compromising accuracy. This investigation examines current challenges in traditional implementations, such as inefficiencies in move ordering and redundant computations, especially in complex game scenarios. The proposed innovative strategies include dynamic move ordering powered by machine learning and improved transposition table management. These techniques streamline the pruning process, ensuring the most promising moves are evaluated first and avoiding repetitive calculations. Adaptive depth control is explored to make the algorithm more responsive to the game's context. Extensive testing on various chess positions demonstrated clear improvements in pruning efficiency. The enhanced algorithm can analyze deeper game trees with reduced computational costs, making chess engines faster and smarter. These advancements not only benefit chess engines but also have broader implications for Artificial Intelligence (AI) systems requiring efficient decision-making in dynamic environments. Improved Alpha-Beta Pruning methods make chess engines smarter and faster, allowing them to analyze deeper strategies while using less computing power.