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Smyrnakis, M
& Leslie, DS 2010,
Convergence of probability collectives with adaptive choice of temperature parameters. in C Blum & R Battiti (eds),
Learning and intelligent optimization: 4th International Conference, LION 4, Venice, Italy, January 18-22, 2010. Selected Papers. Lecture Notes in Computer Science, vol. 6073, Springer, Berlin, pp. 200-203.
https://doi.org/10.1007/978-3-642-13800-3_18APA
Smyrnakis, M.
, & Leslie, D. S. (2010).
Convergence of probability collectives with adaptive choice of temperature parameters. In C. Blum, & R. Battiti (Eds.),
Learning and intelligent optimization: 4th International Conference, LION 4, Venice, Italy, January 18-22, 2010. Selected Papers (pp. 200-203). (Lecture Notes in Computer Science; Vol. 6073). Springer.
https://doi.org/10.1007/978-3-642-13800-3_18Vancouver
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Bibtex
@inproceedings{f9da739cd4d24cd5a2129adc09cdc5c3,
title = "Convergence of probability collectives with adaptive choice of temperature parameters",
abstract = "There are numerous applications of multi-agent systems like disaster management [1], sensor networks [2], traffic control [3] and scheduling problems [4] where agents should coordinate to achieve a common goal. In most of these cases a centralized solution is inefficient because of the scale and the complexity of the problems and thus distributed solutions are required.",
author = "Michalis Smyrnakis and Leslie, {David S.}",
year = "2010",
doi = "10.1007/978-3-642-13800-3_18",
language = "English",
isbn = "9783642137990",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "200--203",
editor = "Christian Blum and Roberto Battiti",
booktitle = "Learning and intelligent optimization",
}
RIS
TY - GEN
T1 - Convergence of probability collectives with adaptive choice of temperature parameters
AU - Smyrnakis, Michalis
AU - Leslie, David S.
PY - 2010
Y1 - 2010
N2 - There are numerous applications of multi-agent systems like disaster management [1], sensor networks [2], traffic control [3] and scheduling problems [4] where agents should coordinate to achieve a common goal. In most of these cases a centralized solution is inefficient because of the scale and the complexity of the problems and thus distributed solutions are required.
AB - There are numerous applications of multi-agent systems like disaster management [1], sensor networks [2], traffic control [3] and scheduling problems [4] where agents should coordinate to achieve a common goal. In most of these cases a centralized solution is inefficient because of the scale and the complexity of the problems and thus distributed solutions are required.
U2 - 10.1007/978-3-642-13800-3_18
DO - 10.1007/978-3-642-13800-3_18
M3 - Conference contribution/Paper
SN - 9783642137990
T3 - Lecture Notes in Computer Science
SP - 200
EP - 203
BT - Learning and intelligent optimization
A2 - Blum, Christian
A2 - Battiti, Roberto
PB - Springer
CY - Berlin
ER -
