# Toward Automated Quest Generation in Text-Adventure Games

arXiv: https://arxiv.org/abs/1909.06283 · HTML: https://mlsanity.com/paper/1909.06283/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2019.

## Built on by (most cited first, 14 of 14)

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- [Dungeons and Dragons as a Dialog Challenge for Artificial Intelligence](https://mlsanity.com/paper/2210.07109/) (2022)
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- [Human Evaluation of Creative NLG Systems: An Interdisciplinary Survey on Recent Papers](https://mlsanity.com/paper/2108.00308/) (2021)
- [A Systematic Survey of Text Worlds as Embodied Natural Language Environments](https://mlsanity.com/paper/2107.04132/) (2021)
- [Generalization in Text-based Games via Hierarchical Reinforcement Learning](https://mlsanity.com/paper/2109.09968/) (2021)
- [Situated Dialogue Learning through Procedural Environment Generation](https://mlsanity.com/paper/2110.03262/) (2021)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [Situated Language Learning via Interactive Narratives](https://mlsanity.com/paper/2103.09977/) (2021)
- [What if Red Can Talk? Dynamic Dialogue Generation Using Large Language Models](https://mlsanity.com/paper/2407.20382/) (2024)
- [Collaborative Storytelling and LLM: A Linguistic Analysis of Automatically-Generated Role-Playing Game Sessions](https://mlsanity.com/paper/2503.20623/) (2025)
- [Infusing Commonsense World Models with Graph Knowledge](https://mlsanity.com/paper/2301.05746/) (2023)
- [Generalizability of experimental studies](https://mlsanity.com/paper/2406.17374/) (2024)
- [GenQuest: An LLM-based Text Adventure Game for Language Learners](https://mlsanity.com/paper/2510.04498/) (2025)

## Abstract (arXiv)

Interactive fictions, or text-adventures, are games in which a player interacts with a world entirely through textual descriptions and text actions. Text-adventure games are typically structured as puzzles or quests wherein the player must execute certain actions in a certain order to succeed. In this paper, we consider the problem of procedurally generating a quest, defined as a series of actions required to progress towards a goal, in a text-adventure game. Quest generation in text environments is challenging because they must be semantically coherent. We present and evaluate two quest generation techniques: (1) a Markov model, and (2) a neural generative model. We specifically look at generating quests about cooking and train our models on recipe data. We evaluate our techniques with human participant studies looking at perceived creativity and coherence.