LEARNING AND PREDICTION BASED ON A RELATIONAL HIDDEN MARKOV MODEL

被引：0

作者：

Elfers, Carsten ^{[1
]}

Wagner, Thomas ^{[1
]}

机构：

[1] Ctr Comp & Commun Technol, Fallturm 1, D-28359 Bremen, Germany

来源：

ICAART 2010: PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON AGENTS AND ARTIFICIAL INTELLIGENCE, VOL 1: ARTIFICIAL INTELLIGENCE | 2010年

关键词：

Hidden Markov model; Relational Markov model; Machine learning; Multi agent system; RoboCup;

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：

In this paper we show a novel method on how the well-established hidden markov model and the relational markov model can be combined to the relational hidden markov model to solve currently unrecognized challenging problems of the original models. Our presented methods allows for prediction on different granularity level depending on the validity of the underlying observations. We demonstrate the use of this new method based on a spatio-temporal qualitative representation and validate the approach in the RoboCupSoccer multi-agent environment.

引用

页码：211 / 216

页数：6

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