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Q-Learning based Market-Driven Multi-Agent Collaboration in Robot Soccer

Hatice Köse, Utku Tatlıdede, Çetin Meriçli, Kemal Kaplan, and H. Levent Akın. Q-Learning based Market-Driven Multi-Agent Collaboration in Robot Soccer. In TAINN, pp. 219–228, 2004.

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Abstract

This work proposes a novel approach for introducing market-driven multi-agent collaboration strategy with Q-Learning based behavior assignment mechanism to the robot soccer domain in order to solve issues related to multi-agent coordination. Robot soccer differs from many other multi-agent problems with its highly dynamic and complex nature. Market-driven approach applies the basic properties of free market economy to a team of robots, to increase the profit of the team as much as possible. For the benefit of the team, robots should work collaboratively, whenever possible. Through Q-learning, a more successful behavior assignment policy have been achieved after a set of training games and the team with learned strategy is shown to be better than the original purely market-driven team.

BibTeX

@inproceedings{DBLP-conf-tainn-Kose2004,
  author    = {Hatice Köse and Utku Tatlıdede and Çetin Meriçli and Kemal Kaplan and H. Levent Akın},
  title     = {Q-Learning based Market-Driven Multi-Agent Collaboration in Robot Soccer},
  booktitle = {TAINN},
  year      = {2004},
  pages     = {219-228},
  abstract  = {This work proposes a novel approach for introducing market-driven multi-agent collaboration strategy with Q-Learning based behavior assignment mechanism to the robot soccer domain in order to solve issues related to multi-agent coordination. Robot soccer differs from many other multi-agent problems with its highly dynamic and complex nature. Market-driven approach applies the basic properties of free market economy to a team of robots, to increase the profit of the team as much as possible. For the benefit of the team, robots should work collaboratively, whenever possible. Through Q-learning, a more successful behavior assignment policy have been achieved after a set of training games and the team with learned strategy is shown to be better than the original purely market-driven team.}
  bib2html_dl_html = "http://www.cmpe.boun.edu.tr/%7Eakin/papers/tainn04.pdf"
  bib2html_pubtype = {Refereed Conference},
  bib2html_rescat = {Multi-robot Planning},
  bib2html_dl_pdf = {../files/koseTAINN2004Market.pdf},
}

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