KEEPING ADAPTIVE GAME AI INTERESTING

被引：0
|
作者：
Szita, Istvan
Ponsen, Marc
Spronck, Pieter
机构：
来源：
PROCEEDINGS OF CGAMES'2008: 13TH INTERNATIONAL CONFERENCE ON COMPUTER GAMES: AI, ANIMATION, MOBILE, EDUCATIONAL AND SERIOUS GAMES | 2008年
关键词：
Game AI; cross-entropy; reinforcement; dynamic scripting;
D O I：
暂无
中图分类号：
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号：
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要：
We propose a method for automatically learning diverse but effective macros that can be used as components of game AI scripts. Macros are learnt by a selection-based optimisation method that maximises an interestingness measure. Our demonstrations are performed in a CRPG simulation with two wizards duelling. The results show that the macros learned this way can increase adaptivity, and diversity, while retaining playing strength.
引用
收藏
页码：70 / 74
页数：5
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