Real-Time Log-Based Novice Recognition and Dynamic Support System

This study proposes Continue AI, an AI support system for novice gamers. To address the problem of novice player attrition in the Vampire Survivors-like genre, we developed a real-time personalized support system that integrates skill classification based on RandomForestClassifier, danger detection with the Danger_score algorithm, and quest generation using Ollama LLM. By implementing efficient real-time communication through a Unity-Python-Redis three-tier architecture and Protocol Buffers, we aim to improve game accessibility for beginners.

Authors

Publication Details

Journal
International Journal of Computer Science and Mobile Computing
Published
2025-09-30
DOI
https://doi.org/10.47760/ijcsmc.2025.v14i09.007
Primary Topic
Embedded Systems and FPGA Design
Type
article
Field-Weighted Citation Impact
0.00
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article

Real-Time Log-Based Novice Recognition and Dynamic Support System

Jinyoung Maeng, Jung‐Won Hwang, Junhui Kim, Seungjae Lee et al.
International Journal of Computer Science and Mobile Computing
Embedded Systems and FPGA Design
article

Real-Time Log-Based Novice Recognition and Dynamic Support System

Jinyoung Maeng, Jung‐Won Hwang, Junhui Kim, Seungjae Lee, Dowon Seo
article en

Abstract

This study proposes Continue AI, an AI support system for novice gamers. To address the problem of novice player attrition in the Vampire Survivors-like genre, we developed a real-time personalized support system that integrates skill classification based on RandomForestClassifier, danger detection with the Danger_score algorithm, and quest generation using Ollama LLM. By implementing efficient real-time communication through a Unity-Python-Redis three-tier architecture and Protocol Buffers, we aim to improve game accessibility for beginners.

International Journal of Computer Science and Mobile ComputingVol. 14(9)
Openalex Percentile: Top 63%
Embedded Systems and FPGA Design
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