五子棋中的高级机器学习技术:策略、实施和分析

Jiajun Han
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引用次数: 0

摘要

战略棋盘游戏 "五子棋 "已成为人工智能(AI)研究的一个引人注目的领域,特别是在开发和应用机器学习技术方面。本文全面分析了 Gomoku 中的高级机器学习策略,重点关注用于棋盘评估的逻辑回归、用于模式识别的神经网络以及用于战略游戏的强化学习。我们讨论了如何将这些技术整合在一起,创建一个能够进行高水平游戏并具有适应性的复杂人工智能。通过这一探索,我们强调了人工智能在战略决策方面的潜力及其在棋盘游戏之外的更广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Advanced Machine Learning Techniques in Gomoku: Strategy,Implementation, and Analysis
The strategic board game Gomoku has become a compelling domain for artificial intelligence (AI) research, particularly in developing and applying machine learning techniques. This paper comprehensively analyzes advanced machine learning strategies in Gomoku, focusing on logistic regression for board evaluation, neural networks for pattern recognition, and reinforcement learning for strategic gameplay. We discuss integrating these techniques in creating a sophisticated AI capable of high-level play and adaptability. Through this exploration, we highlight the potential of AI in strategic decision-making and its broader applications beyond board games.
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