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Hatice Köse, Kemal Kaplan, Utku Tatlıdede, Cetin Mericli, and H. Levent Akın. Market-Driven Multi-Agent Collaboration in Robot Soccer Domain. In Vedran Kordic, Aleksandar Lazinica, and Munir Merdan, editors, Cutting Edge Robotics, Pro Literatur Verlag, 2005.
This work proposes several methods extending from an approach for introducing market-driven multi-agent collaboration strategy 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. In this work several methods, varying from the simple static task allocation mechanism, to market-driven method with fixed number of roles and extended market-driven method with Q-learning are studied and compared. Through Q-learning, a more successful behavior assignment policy have been achieved after a set of training games and the team with learning capability is shown to be better than the original purely market-driven team.
@InCollection{cuttingedgerobotics,
author = {Hatice Köse and Kemal Kaplan and Utku Tatlıdede and Cetin Mericli and H. Levent Akın},
title = {Market-Driven Multi-Agent Collaboration in Robot Soccer Domain},
editor = {Vedran Kordic and Aleksandar Lazinica and Munir Merdan},
booktitle = {Cutting Edge Robotics},
publisher = {Pro Literatur Verlag},
year = {2005},
abstract = {This work proposes several methods extending from an approach for introducing market-driven multi-agent collaboration strategy 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. In this work several methods, varying from the simple static task allocation mechanism, to market-driven method with fixed number of roles and extended market-driven method with Q-learning are studied and compared. Through Q-learning, a more successful behavior assignment policy have been achieved after a set of training games and the team with learning capability is shown to be better than the original purely
market-driven team.}
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bib2html_rescat = {Multi-robot Planning},
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