LLM-Driven NPCs: Cross-Platform Dialogue System for Games and Social Platforms
This addresses the issue of rigid NPC interactions in games and social platforms, though it is incremental as it builds on existing LLM technology for a specific application.
The study tackled the problem of static, platform-limited NPC dialogue by developing a prototype system using LLMs to enable NPCs to interact with players across both game environments (Unity) and social platforms (Discord), with initial experiments confirming technical feasibility for cross-platform interaction.
NPCs in traditional games are often limited by static dialogue trees and a single platform for interaction. To overcome these constraints, this study presents a prototype system that enables large language model (LLM)-powered NPCs to communicate with players both in the game en vironment (Unity) and on a social platform (Discord). Dialogue logs are stored in a cloud database (LeanCloud), allowing the system to synchronize memory between platforms and keep conversa tions coherent. Our initial experiments show that cross-platform interaction is technically feasible and suggest a solid foundation for future developments such as emotional modeling and persistent memory support.