Automated Game Parameter Tuning via AI Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Manual adjustment of game parameters in online games is time-consuming and often results in suboptimal tuning, leading to player dissatisfaction and increased dropout rates due to the large parameter space and reliance on expert tuning.
Innovation Solution
An automated method for tuning game parameters using an iterative process that involves simulating gameplay with AI players, comparing target performance metrics to actual results, and adjusting parameters to optimize game settings, such as those in FarmVille-type games, to achieve desired player engagement and revenue targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual adjustment of game parameters is used, then expert tuning can achieve optimal results, but the process is time-consuming and cannot keep up with rapid game development
Solution Approach 1:
The system enables automated self-tuning of game parameters through computer agents that independently adjust parameters based on simulated player behavior and performance metrics, eliminating the need for manual expert intervention and significantly reducing tuning time while maintaining precision
Solution Approach 2:
The patent replaces manual mechanical adjustment processes with automated computer-based systems that use algorithms to simulate gameplay, analyze performance data, and automatically modify parameters, substituting human expertise with computational mechanisms
2Manufacturing precision
If manual parameter tuning is performed, then detailed optimization is possible, but the large parameter space makes comprehensive tuning impractical
Solution Approach 1:
The system divides the complex parameter space into manageable groups and uses hierarchical tuning approaches, where different parameters are adjusted at different levels of optimization, making the vast parameter space tractable for comprehensive analysis
Solution Approach 2:
The patent creates virtual copies of the game environment through simulation frameworks that replicate gameplay scenarios, allowing extensive parameter testing without modifying the actual game code and enabling comprehensive exploration of the parameter space
3Productivity
If ad hoc parameter adjustment is used, then quick changes are possible, but the results are suboptimal and inconsistent
Solution Approach 1:
The system implements closed-loop feedback mechanisms where simulated player performance data is continuously analyzed and fed back into the tuning process, allowing automated agents to iteratively adjust parameters and converge on optimal values, ensuring both speed and quality of tuning
Solution Approach 2:
The patent employs systematic parameter modification strategies where multiple parameters are adjusted in coordinated sequences based on simulated performance data, transforming ad hoc changes into structured optimization processes that maintain consistency
4Manufacturing precision
If expert tuning is relied upon, then high-quality optimization is achieved, but the process becomes dependent on specialized knowledge and resources
Solution Approach 1:
The automated system performs self-service tuning operations, executing complex optimization tasks independently without requiring expert intervention, thereby making high-quality tuning accessible to anyone with access to the game platform and basic parameter definitions
Data Source
AI summary
A system for automated tuning of a computer-implemented game is configured to enable definition of a performance metric indicative of player performance in a computer-implemented game that has tunable gameplay parameters. A performance target is defined that represents target values for the performance metric during progress in the game. The system executes a gameplay simulation using an automated player, and performs an iterative tuning operation based on results of the simulation. The tuning operation automatically determines a suggested value set for the tunable parameters.