This document discusses using machine learning techniques like graph embeddings and classification models to analyze and generate interactive narrative structures represented as graphs. It presents research on applying these methods to a dataset of over 700 narrative graphs from gamebooks to learn their properties and classifications. The goal is to develop a web app for automated narrative graph generation to assist writers and game developers. Key techniques discussed include generating graph embeddings, using them for classification into categories like "Basic" or "Broad", and exploring autoencoders for graph generation.

2. The Tree of Babel
Machine Learning with narrative graphs
Youjin Chung

3. Interactive Narrative

4. choices + branches

5. Interactive Narrative Structure
= design pattern
= flow chart
= graph

6. Why ML?
…Checking 14M possibilities

7. 1. ML implementation
to interactive narrative structure
as graphs
2. Provide a web app
for graph generation
for writers/game makers
Goal

8. 1. Graph Embeddings
2. Classification
3. Generation
Flow

9. 1. Graph embeddings

10. Graph embeddings
↔
[-0.13391995429992676,
-0.03493423014879227,
0.005581242498010397,
-0.05787748470902443,]

11. Transverse Reading Gallery
~740 graphs from gamebooks

12. Raw data to graph embeddings
Graph representation
{“nodes": [[1], [2], [18], … ],
"edge_index": [[1, 2], [2, 18], [2, 12], … ]}
Data formatting
{-0.13786359131336212,
-0.028311695903539658,
0.017919035628437996,
… ,
-0.05109648406505585}
Graph embeddings

13. 2. Classification

14. Classification
Clustering Algorithms Self Classification
[Basic,
Broad,
Long,
Merged,
Clustered,
Cycle]
using graph properties

15. Basic
basic directed graphs
tree shapes
clean + straightforward

16. Broad
Larger widths
Short playthrough
Strong replayability

17. Linear
The linearity of narrative structure
Easy control of plot
engaging storytelling through long playtime

18. Merged
The graphs merge into certain points.
important events in a story

19. modular structure
detailed choices in a single situation
organized by geography.
Fragmentary, episodic.
Clustered

20. Cyclic
loop back
unlock new status while looping
time loops / geographic travel

21. 3. Generation

22. Autoencoder

23. Visualization

24. https://treebabel.herokuapp.com/

25. What’s next?
Labeling nodes with sentences:
similarity(node vector, sentence vector)
Train Graph CNN with my own categories.

26. Takeaways
ML implementation for narrative graphs
Understanding of Graph Theory, Vector Embeddings,
ML models and implementation
Refining and Formatting Custom Data

27. Special Thanks:
Allison Parrish, Gene Kogan, Dan Shiffman, Jeremy Douglass and Dongphil Yoo