Research and Development of Artificial Intelligence in Electronic Games
Bing Yu · Transactions on Computer Science and Intelligent Systems Research · 2024
This study examines the multiple applications of Artificial Intelligence (AI) technologies in game design and development and their implications. First, this paper outlines the main types of machine learning, including supervised learning, unsupervised learning, semi-supervised learning, and reinforcement learning, and introduces deep learning methods such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial networks (GANs). Subsequently, the specific applications of AI in game design are analyzed in detail in the article, encompassing procedural content generation, game balancing, behavioral control of non-player characters (NPCs) and social AI implementation. In game testing and quality assurance, AI technology significantly improves game development efficiency and user experience through automated error detection and user feedback analysis. In addition, AI also shows great potential in adaptive difficulty adjustment and personalized content recommendation, further enhancing players' game experience. In particular, this article also discusses the application of AlphaGo, DeepMind's StarCraft II AI, and OpenAI Five in Dota 2, demonstrating the superior performance of AI in complex gaming environments. Finally, the article discusses the future direction and challenges of AI in gaming, emphasizing the importance of technical security, data privacy, and ethical issues. Overall, AI technology shows great potential in the gaming field, which not only improves the intelligence level of games, but also brings new opportunities and challenges for game development and industry development.