arXiv:2607.07498v1 Announce Type: new Abstract: Testing is a major effort for the gaming industry, requiring a significant part of development budget and people power. We present a case study on a development version of the ice hockey game EA SPORTS NHL 26, for which human playtesters test the goalie AI for behavioral exploits. To reduce the effort of re-testing the goalie AI after every game or behavior modification in the development phase, we propose Reward-Adaptive Iterative Discovery (RAID), a novel approach to automatically find exploits using an iterative Reinforcement Learning (RL) app
The increasing complexity of game AI and the rising costs of human testing are driving the development of automated solutions for quality assurance.
This development indicates a growing capability for AI agents to autonomously perform and improve complex cognitive tasks, reducing significant operational overhead in industries dependent on continuous testing and development.
The reliance on human playtesters for exhaustive bug discovery in game development is shifting towards AI-driven adaptive systems, allowing for faster iteration and potentially higher quality products.
- · Gaming industry
- · AI development platforms
- · Software testing companies
- · Manual game testers
- · Traditional QA services
Reduced development cycles and costs for complex software that requires extensive testing.
Expansion of similar AI-driven autonomous testing approaches into other industries such as automotive, aerospace, and general software development.
Enhanced product quality and innovation across multiple sectors as the bottleneck of testing is alleviated by autonomous AI systems.
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