Name
Abstract | ||
|---|---|---|
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. |
Year | Venue | Keywords |
|---|---|---|
2010 | ICEIS 2010: PROCEEDINGS OF THE 12TH INTERNATIONAL CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS, VOL 2: ARTIFICIAL INTELLIGENCE AND DECISION SUPPORT SYSTEMS | Supervised learning, Self-organizing map, Multi-agent |
Field | DocType | Citations |
Computer science,Self-organizing map,Supervised learning,Unsupervised learning,Artificial intelligence,Machine learning | Conference | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kazuma Moriyasu | 1 | 0 | 0.34 |
| Takeshi Yoshikawa | 2 | 4 | 1.11 |
| Hidetoshi Nonaka | 3 | 85 | 12.18 |