A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games

Jingchen Yang

doi:10.47297/taposatWSP2633-456917.20250609

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A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games

6卷第9期

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A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games
A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games
Xi'an Jiaotong-Liverpool University, Suzhou, Jiangsu, 215000, China
2025年6卷第9期页码：85-89
纸质出版：2025-09-30
DOI：10.47297/taposatWSP2633-456917.20250609
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Jingchen Yang. (2025). A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games. 科技理论与实践（英文版）, 6(9), 85-89.

Yang Jingchen.A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games[J].Theory and Practice of Science and Technology,2025,06(09):85-89.
Jingchen Yang. (2025). A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games. 科技理论与实践（英文版）, 6(9), 85-89. DOI： 10.47297/taposatWSP2633-456917.20250609.

Yang Jingchen.A Survey of Recent Advances in Artificial Intelligence for Detecting Balance-Breaking Behaviors in FPS Games[J].Theory and Practice of Science and Technology,2025,06(09):85-89. DOI： 10.47297/taposatWSP2633-456917.20250609.

摘要

Abstract

First-person shooter games have become a cornerstone of esports due to their competitiveness and commercial value. However

balance-breaking behaviors such as cheating and smurfing severely undermine fairness

degrade player experience

and threaten the sustainability of the industry. Traditional rule-based or manual inspection approaches have proven inadequate against the growing sophistication of such behaviors. In recent years

artificial intelligence

with its strengths in pattern recognition

temporal modeling

and anomaly detection

has emerged as a promising solution for both research and industrial applications. This paper reviews major advances in AI-driven detection of balance-breaking behaviors in FPS games over the past decade

covering methods based on behavioral features

input device dynamics

vision-based analysis

and unsupervised learning

while comparing their strengths and limitations in terms of accuracy

efficiency

and robustness. Finally

the paper identifies critical challenges—including data scarcity

adversarial dynamics

and the lack of explainability—and proposes future directions focusing on large-scale open dataset construction

adversarial and adaptive modeling

and the development of explainable AI frameworks.

关键词

Keywords

references

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