# Narrative Planning: Balancing Plot and Character

arXiv: https://arxiv.org/abs/1401.3841 · HTML: https://mlsanity.com/paper/1401.3841/

## Key facts

- Cited by 61 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2014.

## Built on by (most cited first, 30 of 61)

- [Co-Writing Screenplays and Theatre Scripts with Language Models: An Evaluation by Industry Professionals](https://mlsanity.com/paper/2209.14958/) (2022)
- [On the Creativity of Large Language Models](https://mlsanity.com/paper/2304.00008/) (2023)
- [Generating Long-form Story Using Dynamic Hierarchical Outlining with Memory-Enhancement](https://mlsanity.com/paper/2412.13575/) (2024)
- [Inferring the Reader: Guiding Automated Story Generation with Commonsense Reasoning](https://mlsanity.com/paper/2105.01311/) (2021)
- [Creating Suspenseful Stories: Iterative Planning with Large Language Models](https://mlsanity.com/paper/2402.17119/) (2024)
- [StoryVerse: Towards Co-authoring Dynamic Plot with LLM-based Character Simulation via Narrative Planning](https://mlsanity.com/paper/2405.13042/) (2024)
- [ReelFramer: Human-AI Co-Creation for News-to-Video Translation](https://mlsanity.com/paper/2304.09653/) (2023)
- [GraphPlan: Story Generation by Planning with Event Graph](https://mlsanity.com/paper/2102.02977/) (2021)
- [TVShowGuess: Character Comprehension in Stories as Speaker Guessing](https://mlsanity.com/paper/2204.07721/) (2022)
- [Persona-Guided Planning for Controlling the Protagonist's Persona in Story Generation](https://mlsanity.com/paper/2204.10703/) (2022)
- [Goal-Directed Story Generation: Augmenting Generative Language Models with Reinforcement Learning](https://mlsanity.com/paper/2112.08593/) (2021)
- [Telling Stories through Multi-User Dialogue by Modeling Character Relations](https://mlsanity.com/paper/2105.15054/) (2021)
- [Authors' Values and Attitudes Towards AI-bridged Scalable Personalization of Creative Language Arts](https://mlsanity.com/paper/2403.00439/) (2024)
- [Guiding Neural Story Generation with Reader Models](https://mlsanity.com/paper/2112.08596/) (2021)
- [Storyfier: Exploring Vocabulary Learning Support with Text Generation Models](https://mlsanity.com/paper/2308.03864/) (2023)
- [Affective and Dynamic Beam Search for Story Generation](https://mlsanity.com/paper/2310.15079/) (2023)
- [Towards Inter-character Relationship-driven Story Generation](https://mlsanity.com/paper/2211.00676/) (2022)
- [Event Causality Is Key to Computational Story Understanding](https://mlsanity.com/paper/2311.09648/) (2023)
- [Guiding and Diversifying LLM-Based Story Generation via Answer Set Programming](https://mlsanity.com/paper/2406.00554/) (2024)
- [Synopses of Movie Narratives: a Video-Language Dataset for Story Understanding](https://mlsanity.com/paper/2203.05711/) (2022)
- [WhatELSE: Shaping Narrative Spaces at Configurable Level of Abstraction for AI-bridged Interactive Storytelling](https://mlsanity.com/paper/2502.18641/) (2025)
- [Fabula Entropy Indexing: Objective Measures of Story Coherence](https://mlsanity.com/paper/2104.07472/) (2021)
- [Incorporating Commonsense Knowledge into Story Ending Generation via Heterogeneous Graph Networks](https://mlsanity.com/paper/2201.12538/) (2022)
- [ScriptViz: A Visualization Tool to Aid Scriptwriting based on a Large Movie Database](https://mlsanity.com/paper/2410.03224/) (2024)
- [Neural Story Planning](https://mlsanity.com/paper/2212.08718/) (2022)
- [Acting as Inverse Inverse Planning](https://mlsanity.com/paper/2305.16913/) (2023)
- [Narrix: Remixing Narrative Strategies from Examples for Story Writing](https://mlsanity.com/paper/2604.07643/) (2026)
- [Semantic Frame Forecast](https://mlsanity.com/paper/2104.05604/) (2021)
- [LyricJam: A system for generating lyrics for live instrumental music](https://mlsanity.com/paper/2106.01960/) (2021)
- [Automated Story Generation as Question-Answering](https://mlsanity.com/paper/2112.03808/) (2021)

## Abstract (arXiv)

Narrative, and in particular storytelling, is an important part of the human experience. Consequently, computational systems that can reason about narrative can be more effective communicators, entertainers, educators, and trainers. One of the central challenges in computational narrative reasoning is narrative generation, the automated creation of meaningful event sequences. There are many factors -- logical and aesthetic -- that contribute to the success of a narrative artifact. Central to this success is its understandability. We argue that the following two attributes of narratives are universal: (a) the logical causal progression of plot, and (b) character believability. Character believability is the perception by the audience that the actions performed by characters do not negatively impact the audiences suspension of disbelief. Specifically, characters must be perceived by the audience to be intentional agents. In this article, we explore the use of refinement search as a technique for solving the narrative generation problem -- to find a sound and believable sequence of character actions that transforms an initial world state into a world state in which goal propositions hold. We describe a novel refinement search planning algorithm -- the Intent-based Partial Order Causal Link (IPOCL) planner -- that, in addition to creating causally sound plot progression, reasons about character intentionality by identifying possible character goals that explain their actions and creating plan structures that explain why those characters commit to their goals. We present the results of an empirical evaluation that demonstrates that narrative plans generated by the IPOCL algorithm support audience comprehension of character intentions better than plans generated by conventional partial-order planners.