Active Learning for Classifying 2D Grid-Based Level Completability
Mahsa Bazzaz Seth Cooper
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.