earticle

영어

User churn in games often arises due to inadequate game difficulty. To address this, non-player characters (NPCs) has been utilized to modulate difficulty according to individual game skill. Nevertheless, the effectiveness of solely NPC-based adjustments is limited since game difficulty is influenced by both NPCs and environmental factors. This paper introduces a novel method for dynamically tailoring game difficulty by adjusting in-game environments based on player behavior patterns in top-down shooter game. Through analysis of diverse user game play data, we find that factors within the game environment, such as the distribution of enemy characters and the arrangement of terrain, have a substantial influence on the level of difficulty. Furthermore, it has been observed that behavioral patterns of players show variations according to changes in the game environment. Using these analytical result, we devise an artificial neural network model that configures an environment that suit player behavior patterns. With the model, we figure out the user player pattern and control the difficulty dynamically by changing the environment factors. Through the experiments, we show that our method provides an appropriate level of difficulty for users to prevent user churn.

한국어

사용자에게 적절한 게임 난이도가 제공되지 않는다면 사용자들은 쉽게 게임에서 이탈한다. 이를 방지하기 위 하여, 각 사용자의 실력에 따른 난이도 조절을 위해 NPC을 이용하여 변화시키는 방법이 사용되고 있다. 그러 나 게임의 난이도는 NPC 뿐만 아니라 게임 환경 요소에 따라 변화하기 때문에 NPC 만으로 난이도 조절을 하 기엔 한계점이 있다. 본 연구에서는 게임 환경 요소를 사용자의 게임 패턴에 기반하여 동적으로 제공하는 방 법을 제안한다. 인공 신경망을 이용하여 사용자의 게임 패턴을 분석하고 해당 결과를 이용하여 사용자의 이탈 을 막을수 있는 적절한 난이도를 각 사용자에게 제공한다.