| dc.contributor |
Fulbright Commission |
|
| dc.contributor |
Plan Nacional de Investigación Científica y Desarrollo (España) |
|
| dc.contributor |
Generalidad de Cataluña |
|
| dc.creator |
Busquets, Dídac |
|
| dc.creator |
Lopez de Mantaras, Ramon |
|
| dc.creator |
Sierra, Carles |
|
| dc.creator |
Dietterich, Thomas G. |
|
| dc.date |
2008-02-19T11:44:28Z |
|
| dc.date |
2008-02-19T11:44:28Z |
|
| dc.date |
2002 |
|
| dc.date.accessioned |
2017-01-31T01:00:12Z |
|
| dc.date.available |
2017-01-31T01:00:12Z |
|
| dc.identifier |
Topics in Artificial Intelligence, 5th Catalonian Conference on AI, CCIA 2002 Castellón, Spain, October 2002. Proceedings. Lecture Notes in Artificial Intelligence Vol. 2504, p.p.: 269-281, Springer-Verlag, 2002. |
|
| dc.identifier |
0302-9743 |
|
| dc.identifier |
http://hdl.handle.net/10261/2984 |
|
| dc.identifier.uri |
http://dspace.mediu.edu.my:8181/xmlui/handle/10261/2984 |
|
| dc.description |
La publicación original está disponible en www.springerlink.com. |
|
| dc.description |
This paper extends a navigation system implemented as a multi-agent system (MAS). The arbitration mechanism controlling the interactions between the agents was based on manually-tuned bidding functions. A difficulty with hand-tuning is that it is hard to handle situations involving complex tradeoffs. In this paper we explore the suitability of reinforcement learning for automatically tuning agents within a MAS to optimize a complex tradeoff, namely the camera use. |
|
| dc.description |
Fullbright Joint Research Project and Plan Nacional Project DPI 2000-1352-C02-02.
Dídac Busquets holds the CIRIT doctoral scholarship 2000FI-00191. |
|
| dc.description |
Peer reviewed |
|
| dc.format |
240583 bytes |
|
| dc.format |
application/pdf |
|
| dc.language |
eng |
|
| dc.publisher |
Springer |
|
| dc.rights |
openAccess |
|
| dc.subject |
Artificial Intelligence |
|
| dc.subject |
Machine Learning |
|
| dc.subject |
Robotics |
|
| dc.subject |
Multiagent Systems |
|
| dc.subject |
Fuzzy Logic |
|
| dc.title |
A Multi-Agent Architecture Integrating Learning and Fuzzy Techniques for Landmark-Based Robot Navigation |
|
| dc.type |
Comunicación de congreso |
|