Steering Narrative Agents through a Dynamic Cognitive Framework for Guided Emergent Storytelling
Chen Yang, M. Gross, R. WampflerProceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (Edmonton, Canada, Nov 10-14, 2025), pp. 1-11
Abstract
Narrative generation frameworks often face a trade-off between character believability and storyline adherence. Planner-based approaches ensure adherence to authorial designs at the cost of character agency and believability. In contrast, emergent narratives excel at presenting believable characters but often lack meaningful plot progression. We present DiriGent, a novel cognitive framework for agent modeling and narrative generation that enables authentic behavior while maintaining storyline adherence. Our agents possess a dynamic belief system and role-based ideal worlds that encode their basic values and relationships. We leverage Large Language Models (LLMs) to analyze the tensions between an agent's ideal worlds and their perceived actual world, which motivates their belief-driven actions. The system then adjusts the narrative world to amplify these tensions, thereby steering agent behavior toward the desired storyline. This dynamic framework allows agents to evolve meaningfully as the story unfolds, overcoming limitations of static agent profiles such as OCEAN. We evaluate our approach by generating stories for five story prompts. Our evaluation, consisting of automated LLM judges and human assessments, demonstrated significant improvements in character development and character motivation compared to baselines, while preserving storyline adherence. Our work presents a path toward interactive narratives that deliver rich characters and enable unique user experiences while adhering to desired storylines.
