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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Nunes, Luis & Oliveira, E. (2003). Cooperative learning using advice exchange. Lecture Notes in Computer Science. 2636, 33-48
Exportar Referência (IEEE)
L. M. Nunes and E. D. Oliveira,  "Cooperative learning using advice exchange", in Lecture Notes in Computer Science, vol. 2636, pp. 33-48, 2003
Exportar BibTeX
@article{nunes2003_1772873580023,
	author = "Nunes, Luis and Oliveira, E.",
	title = "Cooperative learning using advice exchange",
	journal = "Lecture Notes in Computer Science",
	year = "2003",
	volume = "2636",
	number = "",
	doi = "10.1007/3-540-44826-8_3",
	pages = "33-48",
	url = "http://link.springer.com/chapter/10.1007/3-540-44826-8_3"
}
Exportar RIS
TY  - JOUR
TI  - Cooperative learning using advice exchange
T2  - Lecture Notes in Computer Science
VL  - 2636
AU  - Nunes, Luis
AU  - Oliveira, E.
PY  - 2003
SP  - 33-48
SN  - 0302-9743
DO  - 10.1007/3-540-44826-8_3
UR  - http://link.springer.com/chapter/10.1007/3-540-44826-8_3
AB  - One of the main questions concerning learning in a Multi-Agent System’s environment is: “(How) can agents benefit from mutual interaction during the learning process?” This paper describes a technique that enables a heterogeneous group of Learning Agents (LAs) to improve its learning performance by exchanging advice. This technique uses supervised learning (backpropagation), where the desired response is not given by the environment but is based on advice given by peers with better performance score. The LAs are facing problems with similar structure, in environments where only reinforcement information is available. Each LA applies a different, well known, learning technique. The problem used for the evaluation of LAs performance is a simplified traffic-control simulation. In this paper the reader can find a summarized description of the traffic simulation and Learning Agents (focused on the advice-exchange mechanism), a discussion of the first results obtained and suggested techniques to overcome the problems that have been observed.
ER  -