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Interactive story systems today sit at three extremes. Emergent multi‑agent simulations give each character local intelligence but no global view, often losing plot structure. Reactive systems makes fast, state‑based decisions. They form plans using hand-authored rules without searching for action sequences, so these systems can respond quickly but can wander if long-term rules are not explicitly authored. Centralized narrative planners reason globally to craft coherent, goal‑directed plots, yet are computationally expensive. In my doctoral work I treat these not as isolated choices but as the three corners of a triangle spectrum of narrative generation. I propose hybrid, landmark‑guided approaches that can scale to larger domains. I am also exploring how large language models (LLMs) can be embedded within these hybrid approaches themselves. This paper outlines research questions, methodology, progress to date, evaluation plan, and requested feedback. 

Narrative planning is the process of generating sequences of actions that form coherent and goal-oriented narratives. Classical implementations of narrative planning rely on heuristic search techniques to offer structured story generation, but often struggle with scalability because of large branching factors and deep search requirements. To improve the speed of narrative planning, we introduce Fog of War pruning, where Actions are only allowed if they involve people, places, and things that the protagonist character has discovered. This pruning technique restricts the planning to what is known from the perspective of the story's central character or characters, pruning branches of the search tree that involve actions beyond their current knowledge. This method is particularly useful in narratives where there is a strong protagonist focus and the story unfolds gradually as the character learns. This enables more efficient planning, while more closely aligning with how people would experience stories. Experiments across many narrative domains show that this technique not only speed up the search process, under identical search limits, also lets the planner solve more unique problems. 

We discuss the ongoing development of MicroTales, a collection of elements that can be combined to generate interactive narrative environments of varying size and complexity. MicroTales aims to fill a gap among existing AI benchmarks by featuring active non-player characters, a wide variety of actions, and the possibility to soft-lock the problem. Its purpose is to provide a clearly defined environment to compare different experience management algorithms without enforcing any one definition of what makes a good story. We present design goals and a sketch of an initial design, and invite community feedback to help make our benchmark reusable by other narrative intelligence researchers. 

Narrative planning is the process of generating sequences of actions that form coherent and goal-oriented narratives. Classical implementations of narrative planning rely on heuristic search techniques to offer structured story generation but face challenges with scalability due to large branching factors and deep search requirements. Large Language Models (LLMs), with their extensive training on diverse linguistic datasets, excel in understanding and generating coherent narratives. However, their planning ability lacks the precision and structure needed for effective narrative planning. This paper explores a hybrid approach that uses LLMs as heuristic guides within classical search frameworks for narrative planning. We compare various prompt designs to generate LLM heuristic predictions and evaluate their performance against h+, hmax, and relaxed plan heuristics. Additionally, we analyze the ability of relaxed plans to predict the next action correctly, comparing it to the LLMs’ ability to make the same prediction. Our findings indicate that LLMs rarely exceed the accuracy of classical planning heuristics. 

Narrative planning generates a sequence of actions which must achieve the author's goal for the story and must be composed only of actions that make sense for the characters who take them. A causally necessary action is one that would make the plan impossible to execute if it were left out. We hypothesize that action sequences which are solutions to narrative planning problems are more likely to feature causally necessary actions than those which are not solutions. In this paper, we show that prioritizing sequences with more causally necessary actions can lead to solutions faster in ten benchmark story planning problems.