# Active Learning for Classifying 2D Grid-Based Level Completability

arXiv: https://arxiv.org/abs/2309.04367 · HTML: https://mlsanity.com/paper/2309.04367/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Procedural Content Generation via Machine Learning (PCGML)](https://mlsanity.com/paper/1702.00539/) (2017)
- [The VGLC: The Video Game Level Corpus](https://mlsanity.com/paper/1606.07487/) (2016)
- [Video Game Level Repair via Mixed Integer Linear Programming](https://mlsanity.com/paper/2010.06627/) (2020)
- [Automatic Playtesting for Game Parameter Tuning via Active Learning](https://mlsanity.com/paper/1908.01417/) (2019)

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

- [Game Generation via Large Language Models](https://mlsanity.com/paper/2404.08706/) (2024)
- [SmoothHess: ReLU Network Feature Interactions via Stein's Lemma](https://mlsanity.com/paper/2311.00858/) (2023)

## Abstract (arXiv)

Determining the completability of levels generated by procedural generators such as machine learning models can be challenging, as it can involve the use of solver agents that often require a significant amount of time to analyze and solve levels. Active learning is not yet widely adopted in game evaluations, although it has been used successfully in natural language processing, image and speech recognition, and computer vision, where the availability of labeled data is limited or expensive. In this paper, we propose the use of active learning for learning level completability classification. Through an active learning approach, we train deep-learning models to classify the completability of generated levels for Super Mario Bros., Kid Icarus, and a Zelda-like game. We compare active learning for querying levels to label with completability against random queries. Our results show using an active learning approach to label levels results in better classifier performance with the same amount of labeled data.