Mechanic Maker: Accessible Game Development via Symbolic Learning Program Synthesis

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.

Authors

Institutions

Publication Details

Journal
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Published
2024-11-15
DOI
https://doi.org/10.1609/aiide.v20i1.31884
Citations
1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.41

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Mechanic Maker: Accessible Game Development via Symbolic Learning Program Synthesis

Matthew Guzdial, Michael D. Sumner, Vardan Saini
1 citations
Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment
Teaching and Learning Programming
0.41
article

Mechanic Maker: Accessible Game Development via Symbolic Learning Program Synthesis

Matthew Guzdial, Michael D. Sumner, Vardan Saini
article en
1 citations

Abstract

Game development is a highly technical practice that traditionally requires programming skills. This serves as a barrier to entry for would-be developers or those hoping to use games as part of their creative expression. While there have been prior game development tools focused on accessibility, they generally still require programming, or have major limitations in terms of the kinds of games they can make. In this paper we introduce Mechanic Maker, a tool for creating a wide-range of game mechanics without programming. It instead relies on a backend symbolic learning system to synthesize game mechanics from examples. We conducted a user study to evaluate the benefits of the tool for participants with a variety of programming and game development experience. Our results demonstrated that participants' ability to use the tool was unrelated to programming ability. We conclude that tools like ours could help democratize game development, making the practice accessible regardless of programming skills.

Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital EntertainmentVol. 20(1)
University of Alberta (CA)
Canadian Institute for Advanced Research, Alberta Machine Intelligence Institute, Natural Sciences and Engineering Research Council of Canada
Quality Education
Openalex Percentile: Top 25%
Teaching and Learning Programming
0.41
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Mechanic Maker: Accessible Game Development via Symbolic Learning Program Synthesis — Matthew Guzdial, Michael D. Sumner, et al. · Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (2024) | TGRS Research Map | TGRS