Machine Learning Game Generation System
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Solution Overview
Problem
Current computer game design is a manual, iterative process that requires weeks of prototyping and playtesting, lacking an automatic, computerized generative system to produce varying game mechanics and test them.
Innovation Solution
A computer-based system and method using a machine learning model to generate positions and properties of in-game units over time, by training the model with data from existing games, and generating new games by deriving a computerized restrictive environment and dispersing in-game units based on the generated positions and properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual iterative design process is used, then game design flexibility and creativity are maintained, but development time is significantly increased
Solution Approach 1:
The patent replaces the manual mechanical design process with an automated machine learning system. The ML model learns from gameplay data and automatically generates game mechanics, rules, and elements, substituting human designers' manual work with an automated computational system that maintains design flexibility while dramatically reducing development time.
Solution Approach 2:
The system enables self-service by allowing the ML model to autonomously generate game designs without continuous human intervention. The model learns from provided gameplay data and independently creates new game mechanics, rules, and elements, reducing reliance on manual iterative design while preserving adaptability through configurable parameters.
2Productivity
If automated machine learning system is used, then development time is significantly reduced, but design control and creativity may be lost
Solution Approach 1:
The system incorporates feedback mechanisms where the ML model learns from actual gameplay data and player interactions. This feedback loop allows the automated system to continuously improve and adapt its generated designs, maintaining design control by using real gameplay insights to guide and refine the automated generation process rather than operating in complete isolation.
Solution Approach 2:
The ML model serves multiple functions: it analyzes gameplay data, generates new game mechanics, creates game rules, and suggests design improvements. This multi-functionality allows a single automated system to handle various aspects of game design, maintaining versatility and control while improving productivity across the entire design pipeline.
3Manufacturing precision
If extensive prototyping and playtesting are performed, then game mechanics fidelity is improved, but the iterative process becomes more time-consuming
Solution Approach 1:
The system performs preliminary action by using the ML model to pre-generate and pre-test multiple game mechanics variations before final implementation. The model learns from existing gameplay data and proactively creates optimized game designs, reducing the need for extensive subsequent prototyping and playtesting while maintaining high mechanics fidelity through data-driven design.
Data Source
AI summary
A system and method for generating a new computer game including: training a machine learning model to generate positions and properties of second in-game units over time in the second computer game, by providing the machine learning model data descriptive of positions and properties of a plurality of first in-game units over time in a first computer game; and generating the second computer game by deriving a computerized restrictive environment and dispersing the second in-game units in the second computer game based on the generated positions and properties of the second in-game units over time in the second computer game.