.png){kind=icon}
返回
Online behavior change detection in computer games
.png){kind=icon}
PRE
AI
DOI：10.1016/j.eswa.2013.05.059.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Player Modelling has been receiving much attention from the game community in the recent years. The ability to build accurate models of player behavior can be useful in many aspects of a game. One important aspect is the tracking of a player's behavior along time, informing every time a change is perceived. This way, the game Artificial Intelligence can adapt itself to better respond to this new behavior. In order to build models of player behavior, researchers frequently resort to Machine Learning techniques. Such methods work on previously recorded game metrics representing player's interactions with the game environment. However, if the player changes styles over time, the constructed models get out of date. In order to address this drawback, this work proposes the use of and incremental learning technique to track a player's behavior during his/her interaction with the game environment. Our approach attempts to automatically detect the moments in time when the player changes behavior. We apply a change detection technique from the area of Data Stream Mining that is based on incremental clustering and novelty detection. We also propose three modifications to the original technique, in order to formalize change detection, improve detection rate and reduce detection delay. Simulations were performed considering data produced by the Unreal Tournament game, showing the applicability of the method to online tracking of a player's behavior and informing whenever behavior changes occur. (C) 2013 Elsevier Ltd. All rights reserved.
Keyword:
Online player modeling
Behavior change detection
Data Stream Mining
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
机构
引用论文
分享收藏The complete genome sequence of the Gram-positive bacterium Bacillus subtilis革兰氏阳性细菌枯草芽孢杆菌的全基因组序列.png)
Nature
OAAI
NatureIF0
OAAI分享收藏Continuous infusion of neurotrophin-3 triggers sprouting, decreases the levels of TrkA and TrkC, and inhibits epileptogenesis and activity-dependent axonal growth in adult ratsNeuroscience
NeuroscienceIF0
PREAI
分享收藏
查看更多内容