Using Stereotypes to Improve Early-Match Poker Play

被引：0
|
作者：
Layton, Robert [1 ]
Vamplew, Peter [1 ]
Turville, Chris [1 ]
机构：
[1] Univ Ballarat, Sch ITMS, Ballarat, Vic 3353, Australia
来源：
AI 2008: ADVANCES IN ARTIFICIAL INTELLIGENCE, PROCEEDINGS | 2008年 / 5360卷
关键词：
opponent modelling; agent modelling; stereotypes; poker; games;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Agent modelling is a critical aspect of many artificial intelligence systems. Many different techniques are used to learn the tendencies of another agent, though most suffer from a slow learning time. The research proposed in this paper examines stereotyping as a method to improve the learning time of poker playing agents. Poker is a difficult domain for opponent modelling due to its hidden information, stochastic elements and complex strategies. However, the literature suggests there are clusters of similar poker strategies, making it an ideal environment to test the effectiveness of stereotyping. This paper presents a method for using stereotyping in a poker bot, and shows that stereotyping improves performance in early-match play in many scenarios.
引用
收藏
页码：584 / 593
页数：10
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