Design Mining for Minecraft Architecture

Authors

  • Euisun Yoon Cornell University
  • Erik Andersen Cornell University
  • Bharath Hariharan Cornell University
  • Ross Knepper Cornell University

DOI:

https://doi.org/10.1609/aiide.v14i1.13045

Keywords:

Minecraft, design mining, deep learning

Abstract

3D construction sandbox games such as Minecraft have provided new opportunities for people to express their creativity. However, individual players have few tools to help them learn about architectural style or how to improve the structure they are building. Ideally, players could utilize tools that capitalize on the large numbers of 3D models built by others to offer guidance for their particular project. We trained a neural network to classify a large collection of Minecraft models from various websites in terms of style (Ancient, Asian, Medieval, or Modern). We present experimental results demonstrating that our model can classify the user-indicated style of a structure with 55% accuracy. We further demonstrate use of this model to highlight nearest neighbors to a specific query structure. We have integrated these tools into a Minecraft Mod that allows players to classify their structure's style and view nearest neighbors in real-time.

Downloads

Published

2018-09-25

How to Cite

Yoon, E., Andersen, E., Hariharan, B., & Knepper, R. (2018). Design Mining for Minecraft Architecture. Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment, 14(1), 250-256. https://doi.org/10.1609/aiide.v14i1.13045