Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
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TY - GEN
T1 - Sequentially updated probability collectives
AU - Smyrnakis, Michalis
AU - Leslie, David S.
PY - 2009
Y1 - 2009
N2 - Multi-agent coordination problems can be cast as distributed optimization tasks. Probability collectives (PCs) are techniques that deal with such problems in discrete and continuous spaces. In this paper we are going to propose a new variation of PCs, sequentially updated probability collectives. Our objective is to show how standard techniques from the statistics literature, sequential Monte Carlo methods and kernel regression, can be used as building blocks within PCs instead of the ad hoc approaches taken previously to produce samples and estimate values in continuous action spaces. We test our algorithm in three different simulation scenarios with continuous action spaces. Two classical distributed optimization functions, the three and six dimensional Hartman functions and a vehicle target assignment type game. The results for the Hartman functions were close to the global optimum, and the agents managed to coordinate to the optimal solution of the target assignment game.
AB - Multi-agent coordination problems can be cast as distributed optimization tasks. Probability collectives (PCs) are techniques that deal with such problems in discrete and continuous spaces. In this paper we are going to propose a new variation of PCs, sequentially updated probability collectives. Our objective is to show how standard techniques from the statistics literature, sequential Monte Carlo methods and kernel regression, can be used as building blocks within PCs instead of the ad hoc approaches taken previously to produce samples and estimate values in continuous action spaces. We test our algorithm in three different simulation scenarios with continuous action spaces. Two classical distributed optimization functions, the three and six dimensional Hartman functions and a vehicle target assignment type game. The results for the Hartman functions were close to the global optimum, and the agents managed to coordinate to the optimal solution of the target assignment game.
U2 - 10.1109/CDC.2009.5400064
DO - 10.1109/CDC.2009.5400064
M3 - Conference contribution/Paper
SN - 9781424438716
SP - 5774
EP - 5779
BT - Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Conference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on
PB - IEEE
ER -