Enhancing Non-player Characters in Unity 3D using GPT-3.5

Abstract

This case study presents a comprehensive integration process of OpenAI's GPT-3.5 large language model (LLM) into Unity 3D to enhance non-player characters (NPCs) in video games and interactive applications. The study aims to develop an architecture and open-source software framework that enables NPCs to engage in dynamic real-time interactions with players and other characters. The background and motivation for the study are provided, highlighting the existing limitations of traditional NPC programming and the potential of advanced natural language models such as GPT-3.5 to overcome these limitations. The methodology section outlines the step-by-step process, covering framework design and preparation, core architecture development, humanoid avatar integration and animation, and important feature extensions. The progression of framework design and implementation is described, emphasizing key architectural concepts, design patterns, and essential classes and interfaces. The results of the case study are discussed, focusing on the valuable insights gained and the implications for future advancements. Lessons learned from the integration process are shared, along with suggestions for potential improvements and directions for future research. This case study provides a practical resource for game developers and researchers interested in leveraging advanced natural language processing capabilities to create more immersive and interactive NPC experiences in Unity 3D environments.