Time Series Data Mining and Behavioral Recognition for Computer Games Player Modeling and Adaptation(Internationa Session 2)

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概要

• 論文の詳細を見る

• This paper proposes a method of modeling computer game player with behavioral analysis and adaptation using time series data mining concept. We capture player's behavior through collecting significant parameters while player is playing the game and cluster the data using Self Organizing Map (SOM) with dimension reduction. Classification is performed offline with overall mean as indicator to label the categories by heuristic deduction. In our experiment with a shooting game, we have demonstrated the clustering process and selected two sample player's trend and explain here in our analysis. Our motivation is to create a personalization for player and improves gaming experience based on behavioral recognition.

• 社団法人電子情報通信学会の論文
• 2007-10-18

著者

• Jung Keechul

  Human-centered Interface (hci) Laboratory Department Of Media Soongsil University

• Wong Chee-onn

  Human-centered Interface (hci) Laboratory Department Of Media Soongsil University

• Kim Jongin

  Human-Centered Interface (HCI) Laboratory, Department of Media, Soongsil University

• Yang Jongyeol

  Human-Centered Interface (HCI) Laboratory, Department of Media, Soongsil University

• Kim Jongin

  Human-centered Interface (hci) Laboratory Department Of Media Soongsil University

• Yang Jongyeol

  Human-centered Interface (hci) Laboratory Department Of Media Soongsil University

関連論文

• Time Series Data Mining and Behavioral Recognition for Computer Games Player Modeling and Adaptation(Internationa Session 2)

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