Elysian: Building Emotionally Responsive Nonplayable Characters Using Integration of FSM and Generative AI for Real-Time Dialogues in Visual Games

Elysian is a modular architecture designed to generate emotionally expressive and contextually coherent dialogue for non-playable characters (NPCs) in interactive games. As the first contribution in a larger research program, this paper focuses on the system's dialogue generation module, which leverages large language models (LLMs) under strict runtime and hardware constraints. We investigate two practical approaches for enabling emotion-aware dialogue on consumer-grade devices: parameter-efficient LoRA fine-tuning and structured prompt orchestration. Targeting deployment on 6 GB VRAM hardware with sub-5-second response latency, we evaluate whether small, 4-bit-quantized 1B-parameter models can reliably produce emotionally aligned NPC dialogue. Across 1,000 generated samples, LoRA fine-tuning yields substantial performance gains in lexical alignment (BLEU: 0.0587 vs. 0.0006), semantic similarity (BERTScore: 0.8645 vs. 0.2712), and emotion-tag accuracy (100% vs. 0%) compared to prompting alone. Meanwhile, prompt orchestration demonstrates strong structural reliability, achieving 78% valid JSON generation for branching narrative formats and reaching 100% accuracy using a lightweight two-pass correction scheme. This work establishes the technical foundation for future research on multimodal NPC behavior, including integration with speech synthesis, behavioral animation, and visually grounded interaction.