Machine-Learned Game Configuration for Adaptive Gameplay Tuning
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Solution Overview
Problem
Existing computer games lack flexibility in gameplay adjustments, limiting user engagement and game provider enhancements.
Innovation Solution
A client-server environment utilizing machine learning to analyze gameplay data and device data, detecting complex associations, predicting parameter changes, and configuring games to improve defined metrics such as user engagement, through a process involving communication modules, prediction modules, and configuration modules.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional game configuration methods are used, then game development is simple and straightforward, but game flexibility and adaptability are limited
Solution Approach 1:
A machine learning model serves as an intermediary between gameplay data and game parameter adjustments. The system collects gameplay data, processes it through ML algorithms to detect complex associations, and uses these insights to automatically adjust game parameters. This intermediary layer enables flexible adaptation without requiring complex manual configuration systems.
Solution Approach 2:
The game system performs self-configuration through automated machine learning processes. The ML model continuously analyzes gameplay data and automatically adjusts game parameters without human intervention, enabling the game to adapt and optimize itself based on player behavior patterns.
2Productivity
If game parameters are manually adjusted, then implementation is straightforward, but real-time adaptation to user behavior is impossible
Solution Approach 1:
The system implements continuous feedback loops where gameplay data is collected, analyzed by ML models to detect associations with target metrics, and used to adjust game parameters. These adjusted parameters then influence subsequent gameplay, creating ongoing feedback that enables real-time optimization of user engagement and other target metrics.
Solution Approach 2:
Manual game configuration mechanics are replaced with automated machine learning systems. Instead of developers manually adjusting parameters based on intuition, ML models automatically analyze gameplay data and adjust parameters based on detected complex associations, dramatically improving optimization efficiency.
3Adaptability or versatility
If complex machine learning models are deployed, then game adaptability improves, but computational resources and processing time increase
Solution Approach 1:
The system applies machine learning selectively to detect specific complex associations between gameplay parameters and target metrics rather than analyzing all possible parameters. This partial action approach maintains high adaptability while reducing computational overhead by focusing only on the most relevant associations for game optimization.
Data Source
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AI summary
A game modification engine modifies configuration settings affecting game play and the user experience in computer games after initial publication of the game, based on device level and game play data associated with a user or cohort of users and on machine-learned relationships between input data and a use metric for the game. The modification is selected to improve performance of the game as measured by the use metric. The modification may be tailored for a user cohort. The game modification engine may define the cohort automatically based on correlations discovered in the input data relative to a defined use metric.