Decentralized AI in Gaming Using Blockchain to Optimize Minimax and Alpha-Beta Pruning

SHANKAR DEV

Authors

SHANKAR DEV INDIA Author

Keywords:

Decentralized AI, Blockchain, Game Development, Alpha-Beta Pruning, Minimax Algorithm, Scalability

Abstract

In recent years, the integration of artificial intelligence (AI) in gaming has led to significant advancements in game strategy and player engagement. One of the most effective AI techniques employed in game development is the Minimax algorithm, often enhanced with Alpha-Beta pruning for improved efficiency. However, traditional centralized AI systems face challenges related to scalability, security, and trust. This paper explores the potential of leveraging blockchain technology to decentralize AI in gaming, specifically focusing on optimizing Minimax and Alpha-Beta pruning algorithms. By decentralizing AI processes using blockchain, we aim to enhance the transparency, security, and robustness of game AI. This approach not only mitigates the risks associated with centralized systems but also provides a tamper-proof environment for AI decision-making. Our findings indicate that blockchain can significantly optimize the performance of Minimax and Alpha-Beta pruning, offering a promising pathway for the future of decentralized game AI development.

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