# Aesthetic Bot: Interactively Evolving Game Maps on Twitter

arXiv: https://arxiv.org/abs/2208.05017 · HTML: https://mlsanity.com/paper/2208.05017/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 9 Aug 2022.

## Builds on (most cited first, 9 of 9)

- [Evolving Mario Levels in the Latent Space of a Deep Convolutional Generative Adversarial Network](https://mlsanity.com/paper/1805.00728/) (2018)
- [Learning Controllable Content Generators](https://mlsanity.com/paper/2105.02993/) (2021)
- [Generative Design in Minecraft (GDMC), Settlement Generation Competition](https://mlsanity.com/paper/1803.09853/) (2018)
- [Baba is Y'all: Collaborative Mixed-Initiative Level Design](https://mlsanity.com/paper/2003.14294/) (2020)
- [Tile Pattern KL-Divergence for Analysing and Evolving Game Levels](https://mlsanity.com/paper/1905.05077/) (2019)
- [Deep Interactive Evolution](https://mlsanity.com/paper/1801.08230/) (2018)
- [Multi-Objective level generator generation with Marahel](https://mlsanity.com/paper/2005.08368/) (2020)
- [TropeTwist: Trope-based Narrative Structure Generation](https://mlsanity.com/paper/2204.09672/) (2022)
- [Procedural Content Generation using Behavior Trees (PCGBT)](https://mlsanity.com/paper/2107.06638/) (2021)

## Built on by (most cited first, 2 of 2)

- [The Five-Dollar Model: Generating Game Maps and Sprites from Sentence Embeddings](https://mlsanity.com/paper/2308.04052/) (2023)
- [DreamGarden: A Designer Assistant for Growing Games from a Single Prompt](https://mlsanity.com/paper/2410.01791/) (2024)

## Abstract (arXiv)

This paper describes the implementation of the Aesthetic Bot, an automated Twitter account that posts images of small game maps that are either user-made or generated from an evolutionary system. The bot then prompts users to vote via a poll posted in the image's thread for the most aesthetically pleasing map. This creates a rating system that allows for direct interaction with the bot in a way that is integrated seamlessly into a user's regularly updated Twitter content feed. Upon conclusion of the each voting round, the bot learns from the distribution of votes for each map to emulate user preferences for design and visual aesthetic in order to generate maps that would win future vote pairings. We discuss the ongoing results and emerging behaviors that have occurred since the release of this system from both the bot's generation of game maps and the participating Twitter users.