SUPERVISED LEARNING FOR AGENT POSITIONING BY USING SELF-ORGANIZING MAP

被引：0

作者：

Moriyasu, Kazuma ^{[1
]}

Yoshikawa, Takeshi ^{[1
]}

Nonaka, Hidetoshi ^{[1
]}

机构：

[1] Hokkaido Univ, Grad Sch Informat & Technol, Sapporo, Hokkaido 0600814, Japan

来源：

ICEIS 2010: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL 2: ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS | 2010年

关键词：

Supervised learning; Self-organizing map; Multi-agent;

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：

We propose a multi-agent cooperative method that helps each agent to cope with partial observation and reduces the number of teaching data. It learns cooperative actions between agents by using the Self-Organizing Map as supervised learning. Input Vectors of the Self-Organizing Map are the data that reflects the operator's intention. We show that our proposed method can acquire cooperative actions between agents and reduce the number of teaching data by two evaluation experiments using the pursuit problem that is one of multi-agent system.

引用

页码：368 / 372

页数：5