Automated Game Engine Configuration for Effort–Reward Balancing
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Solution Overview
Problem
Configuring a game engine to meet user achievement requirements is challenging due to the complexity of numerous adjustable parameters and the difficulty in predicting human player response to different parameter combinations, which can have synergistic effects that are difficult to determine using scalar-based approaches.
Innovation Solution
A system and method for automatically configuring a game engine by predicting a target set of configuration parameters that include a combination of effort level and reward level parameters, using machine learning models to simulate player behavior and iteratively adjust these parameters to achieve a balance that meets user achievement requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual configuration methods are used to adjust game engine parameters, then flexibility in customization is improved, but the complexity of configuring numerous parameters and predicting player response increases significantly
Solution Approach 1:
The system enables self-service by automatically configuring game engine parameters through machine learning models that predict player responses. The automated configuration system independently adjusts effort and reward parameters without requiring manual intervention, resolving the contradiction by providing both customization flexibility and reduced complexity through automation.
Solution Approach 2:
The system applies parameter changes by using machine learning models to dynamically adjust configuration parameters based on predicted player responses. The system modifies effort level and reward level parameters automatically, transforming the complex manual configuration process into an automated parameter optimization process that maintains flexibility while reducing complexity.
2Device complexity
If automated configuration systems are implemented, then the complexity of parameter management is reduced, but the ability to capture synergistic effects between parameters diminishes
Solution Approach 1:
The system merges multiple configuration parameters into unified effort level and reward level categories that are processed by machine learning models. By combining individual parameters into broader functional groups, the system reduces management complexity while preserving the ability to capture synergistic effects through the predictive modeling of player responses to parameter combinations.
Solution Approach 2:
The system implements feedback mechanisms where machine learning models continuously learn from player responses to configuration parameters. The models use feedback data to refine predictions of how parameter combinations affect player engagement, ensuring that automated configuration maintains high prediction accuracy despite reduced management complexity.
3Ease of manufacture
If scalar-based approaches are used to adjust parameters, then the simplicity of implementation is maintained, but the difficulty in determining synergistic effects between parameters increases
Solution Approach 1:
The system replaces scalar-based mechanical adjustment approaches with machine learning-based predictive modeling. Instead of manually adjusting individual parameters using simple scalar values, the system uses trained models that automatically determine optimal parameter combinations, substituting complex mathematical relationships with learned patterns that capture synergistic effects while maintaining implementation feasibility.
4Reliability
If machine learning models are used to predict player response, then the accuracy of predicting synergistic effects is improved, but the computational resources and time required increase
Solution Approach 1:
The system applies preliminary action by pre-training machine learning models on extensive datasets before deployment. The models are prepared in advance with learned relationships between parameters and player responses, so that during actual configuration, predictions can be made quickly without requiring extensive real-time computation, thus reducing configuration time while maintaining high prediction accuracy.
Data Source
AI summary
There is provided a method of automatically configuring a gaming engine, comprising: accessing, a user achievement requirement denoting goals and/or milestones for a player to accomplish within a game, the user requirement comprising associated factors defining measurement of meeting the user achievement requirement by the player, predicting a target set of configuration parameters that when used for configuring the gaming engine, result in the player meeting the user achievement requirement as measured by the associated factors, wherein the target set of configuration parameters comprises a combination of a first and a second type of configuration parameters, the first type being associated with setting an effort level predicted to be tolerated by the player, and the second type associated with setting a reward level predicted as an incentive by the player, and providing the target set of configuration parameters for configuring the gaming engine.