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A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform all previously investigated RL-based, shortest path routing algorithms.
@inproceedings{tumer-wolpert_nips99,
author = {D. H. Wolpert and K. Tumer and J. Frank},
title = {Using Collective Intelligence to Route Internet Traffic},
booktitle={Advances in Neural Information Processing Systems - 11},
publisher={MIT Press},
pages = {952-958},
editor ={Kearns, M. and Solla, S. A. and Cohn, D.},
abstract={A COllective INtelligence (COIN) is a set of interacting reinforcement learning (RL) algorithms designed so that their collective behavior optimizes a global utility function. We summarize the theory of COINs, then present experiments using that theory to design COINs to control internet traffic routing. These experiments indicate that COINs outperform all previously investigated RL-based, shortest path routing algorithms.},
bib2html_pubtype = {Refereed Conference Papers},
bib2html_rescat = {Multiagent Systems, Collectives, Optimization},
year = {1999}
}
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