Artificial Intelligence in Games: Enriching Game Content and Enhancing Player Experience - Jiang, Chengzi (Transactions on Computer Science and Intelligent Systems Research, 2025)
This study explores the application of generative artificial intelligence (AI) in video games, focusing on improving content richness and personalized player experience. Traditional non-player character (NPC) systems have limitations in adaptability and interactivity. This study explores how generative AI can improve NPC behavior and dialogue. Then, this paper proposes a framework that combines generative agents, Transformer-based dialogue models, and reinforcement learning. Specifically, the generative agent simulates memory-driven planning, the Transformer model generates context-aware dialogues, and reinforcement learning supports adaptive interactions. This study is based on a generative agent dataset and a Role-playing game (RPG) dialogue corpus. The results show that the proposed method enhances the realism of NPCs, the coherence of game narratives, and the responsiveness of player interactions, and improves player immersion and the diversity of interactions. This provides practical insights for scalable intelligent game development and shows the potential of artificial intelligence in automating complex content creation and points out the direction for the future combination of games and artificial intelligence.