Mechanic Maker: Accessible Game Development Via Symbolic Learning Program Synthesis

TL;DR

Mechanic Maker, a tool for creating a wide-range of game mechanics without programming, relies on a backend symbolic learning system to synthesize game mechanics from examples, concluding that tools like the authors' could help democratize game development, making the practice accessible regardless of programming skills.

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.

Figures (9)

• Figure 1: The Mechanic Maker editor. (a) The user defines frames of their game by placing objects on a grid, (b) the SLPS backend attempts to learn the underlying rules suggested by the changes across the frames, and (c) the user can test the learned rules in real-time via the Play Mode.
• Figure 2: Frame error grouped by programming experience for the (a) Sokoban and (b) Flappy Bird activities. From the survey results None and Limited programming were merged into the non-programmer category and Moderate and Expert programming experience were merged into programmer. The All box plot shows all programming experience combined. The red line marks the performance of our baseline.
• Figure 3: Example game outputs from the Free Play portion of the user study.
• Figure 4: T-distributed Stochastic Neighbor Embedding (TSNE) to visualize the twenty dimensions of the GMM as two dimensions. This Figure displays a visualization of our GMM as seven clusters.
• Figure 5: Survey results for the tool in general.