Modeling Worlds in Text

TL;DR

This paper introduces a dataset for training language-based agents to build knowledge graph-based world models in interactive text-adventure games, enabling better understanding and interaction within complex, partially observable environments.

Contribution

It provides a large, annotated dataset linking natural language observations to structured knowledge graphs and actions, facilitating research in language-based world modeling.

Findings

Dataset includes 24,198 training mappings and 7,836 test instances.

Baseline models demonstrate initial approaches to learning in this environment.

Analysis highlights challenges and opportunities for future research.

Abstract

We provide a dataset that enables the creation of learning agents that can build knowledge graph-based world models of interactive narratives. Interactive narratives -- or text-adventure games -- are partially observable environments structured as long puzzles or quests in which an agent perceives and interacts with the world purely through textual natural language. Each individual game typically contains hundreds of locations, characters, and objects -- each with their own unique descriptions -- providing an opportunity to study the problem of giving language-based agents the structured memory necessary to operate in such worlds. Our dataset provides 24198 mappings between rich natural language observations and: (1) knowledge graphs that reflect the world state in the form of a map; (2) natural language actions that are guaranteed to cause a change in that particular world state. The training data is collected across 27 games in multiple genres and contains a further 7836 heldout instances over 9 additional games in the test set. We further provide baseline models using rules-based, question-answering, and sequence learning approaches in addition to an analysis of the data and corresponding learning tasks.