Key insights
4 slides
The new model
A novel framework allows dynamic genre-blending in digital games.
This framework uses floating-point variables to adjust core gameplay elements in real-time. It moves away from static, designer-defined genre constraints.
Core idea. A new framework enables dynamic genre-blending by using floating-point variables to adjust core mechanics like movement, puzzle complexity, and AI behavior in real-time.
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- 01A new framework enables dynamic genre-blending by using floating-point variables to adjust core mechanics like movement, puzzle complexity, and AI behavior in real-time.See it on page 2
- 02The model successfully generated three distinct gameplay experiences—a platformer, a shoot 'em up, and a puzzle game—using only three parameters: gravity, puzzle factor, and AI aggressiveness.See it on page 3
- 03An empirical study of 44 participants confirmed that players could clearly distinguish between genre-specific concepts like jumping, shooting, and logical thinking within the prototype.See it on page 7
- 04User testing showed no significant difference in perceived quality, fun, or learnability between the generated variants, indicating that the parametric approach maintains consistent game standards.See it on page 6
- 05This methodology provides a scalable solution for developers to move away from static, designer-defined genre constraints toward more flexible, player-adaptive gameplay systems.See it on page 2
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Summary
The research introduces a novel framework for designing digital games that allows for dynamic blending between genres through the use of parametrizable game mechanics. By moving away from static, designer-defined genre constraints, the approach utilizes floating-point variables to adjust core gameplay elements in real-time. The primary thesis is that fundamental game mechanics—specifically movement, puzzle complexity, and artificial intelligence behavior—can be systematically modeled and adjusted to create distinct, high-quality gaming experiences that cater to individual player preferences or moods.
The study evaluates this model by implementing a prototype that generates three distinct game variants: a platformer, a shoot 'em up, and a puzzle game. These variants are controlled by three specific parameters: gravity, which dictates movement metaphors; a puzzle factor, which adjusts object dependencies; and an aggressiveness factor, which modifies AI behavior. Empirical evidence was gathered through an online user study involving 44 participants. The results indicate that while users perceived no significant difference in the overall quality, fun, or learnability of the three variants, they clearly identified and attributed distinct gameplay concepts—such as jumping, shooting, and logical thinking—to the respective genres.
The findings demonstrate that the proposed parametric model successfully maintains a consistent level of game quality while enabling clear genre differentiation. This methodology offers a scalable solution for developers to incorporate dynamic genre-blending into their titles. Future work aims to expand the number of variants to test the scalability of the model and to refine the runtime blending of these mechanics to ensure a seamless and engaging player experience.
