AI Parameter Configuration for Racing Models
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Solution Overview
Problem
The existing methods for configuring AI parameters for racing AI models are inefficient, requiring significant manual adjustment time and resulting in low efficiency and poor parameter coverage, leading to suboptimal performance in simulating real player interactions.
Innovation Solution
An AI parameter configuration method using a genetic algorithm that automatically adjusts AI parameters by performing adaptation degree tests, crossover, and mutation operations to generate new parameter sets, optimizing parameter settings through iterative processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual adjustment of AI parameters is used, then the developer can control and analyze each parameter, but a large quantity of time is consumed and efficiency is low
Solution Approach 1:
The system performs self-service by automatically testing and adjusting AI parameters without continuous manual intervention. The racing AI model autonomously undergoes adaptation degree tests and the system automatically generates new parameter sets based on test results, reducing developer workload while maintaining control.
Solution Approach 2:
The system implements feedback mechanisms where the adaptation degree test results are fed back into the parameter adjustment process. The travel distance and adaptation degree metrics provide feedback that guides automatic parameter optimization, creating a closed-loop system that improves efficiency while preserving developer oversight.
2Measurement precision
If manual adjustment of AI parameters is used, then the developer can analyze travel effects, but the process requires repeated simulation and adjustment consuming significant time
Solution Approach 1:
The system performs preliminary actions by automatically conducting adaptation degree tests and generating new parameter sets before full-scale deployment. The preliminary testing phase identifies promising parameter combinations, reducing the need for extensive repeated simulations and accelerating the overall configuration process while maintaining analysis accuracy.
Solution Approach 2:
The system replaces the mechanical manual adjustment process with an automated computational system. Instead of manual parameter tweaking and repeated simulations, the system uses automatic adaptation degree testing and genetic algorithm-based parameter generation, significantly reducing configuration time while preserving the ability to analyze travel effects through automated metrics.
3Adaptability or versatility
If a large quantity of AI parameters are configured, then the parameter coverage is improved, but the parameters are coupled and require extensive manual adjustment
Solution Approach 1:
The system applies segmentation by breaking down the complex coupled parameter space into manageable adaptation degree tests. Each parameter set is evaluated independently through standardized tests, and the genetic algorithm processes parameters in discrete generations, making the complex configuration process more tractable while maintaining comprehensive parameter coverage.
Solution Approach 2:
The system utilizes parameter changes by automatically transforming parameter sets through the genetic algorithm process. Parameters are systematically varied and optimized across multiple generations, allowing comprehensive exploration of the parameter space and its couplings without requiring manual adjustment of each individual parameter, thus handling complexity while maintaining adaptability.
Data Source
AI summary
An artificial intelligence (AI) parameter configuration method for a racing AI model performed by an AI parameter configuration device is provided. A first parameter set including m sets of AI parameters is obtained. Each of the m sets of AI parameters is used by the racing AI model to travel on a track. The racing AI model is controlled to undergo an adaptation degree test according to each of the m sets of AI parameters to obtain m adaptation degrees. The m adaptation degrees are positively correlated with travel distances of the racing AI model on the track according to the m sets of AI parameters. A second parameter set is generated according to the first parameter set in a case that all the m adaptation degrees are less than an adaptation degree threshold. In a case that a target adaptation degree in the m adaptation degrees is greater than the adaptation degree threshold, an AI parameter corresponding to the target adaptation degree is configured as a target AI parameter.