Research on Artificial Intelligence in Game Strategy Optimization

Abstract

This paper provides a comprehensive review of the current state and future directions of artificial intelligence (AI) in game strategy optimization. It explores the key AI techniques driving advancements in this field, including machine learning, reinforcement learning, neural networks, and Monte Carlo tree search. Through detailed case studies of landmark AI systems such as Deep Blue, AlphaGo, Libratus, and AlphaStar, the paper illustrates the remarkable progress made in domains ranging from chess and go to poker and real-time strategy games. Despite these achievements, significant challenges remain, including multi-domain generalization, explainability, and effective human-AI collaboration. The paper also delves into promising future research directions, such as developing more flexible AI architectures and improving AI's ability to work alongside human players. Finally, it addresses the ethical considerations surrounding the advancement of AI in game strategy, including issues of fairness in competitive gaming, potential societal impacts, and the responsible development of these technologies. This research not only highlights the transformative potential of AI in gaming but also its broader implications for strategic decision-making in real-world scenarios.