Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866914962432065536 |
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| author | Sumner, Megan Saini, Vardan Guzdial, Matthew |
| author_facet | Sumner, Megan Saini, Vardan Guzdial, Matthew |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_01096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis Sumner, Megan Saini, Vardan Guzdial, Matthew Human-Computer Interaction Artificial Intelligence 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. |
| title | Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis |
| topic | Human-Computer Interaction Artificial Intelligence |
| url | https://arxiv.org/abs/2410.01096 |