Nova: A Dynamic Emotion-Based Narrator for Competitive Games

This paper discusses the problem of dynamic game narration and proposes a solution conceptualized to enhance player experience by providing real-time, creative and context- aware narration using large language models (LLMs). Based on the literature review, the framework would consist of three interconnected modules to solve the remaining challenges identified: an anomaly detection module, a Retrieval-Augmented Generation (RAG) module, and an emotion detection module. The anomaly detection module would identify relevant events within the game, serving as a trigger for narration. The RAG module would provide necessary context regarding the game's rules and strategies, ensuring accurate and consistent narration. The emotion detection module would gauge the atmosphere of the game and the players' emotions, allowing the LLM to craft a creative and engaging narrative. The framework's effectiveness could be evaluated in the multiplayer card game Chef's Hat, where player interactions and game data can be easily recorded and analyzed. The evaluation metrics would include Coherence, Relevance and Engagement.