All Bids for One and One Does for All: Market-Driven Multi-agent Collaboration in Robot Soccer Domain.
ABSTRACT In this paper, a novel market-driven collaborative task allocation algorithm called “Collaboration by competition / cooperation”
for the robot soccer domain is proposed and implemented. In robot soccer, two teams of robots compete with each other to win
the match. For the benefit of the team, the robots should work collaboratively, whenever possible. The market-driven approach
applies the basic properties of free market economy to a team of robots for increasing the profit of the team as much as possible.
The experimental results show that the approach is robust and flexible and the developed team is more succcessful than its
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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 difiers 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 proflt of the team as much as possible. For the beneflt 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.
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ABSTRACT: This survey paper starts with a basic explanation about robot soccer and its systems, then will focus on the strategies that have been used by previous researchers. There is a time-line of described robot soccer strategies, which will show the trend of strategies and technologies. The basic algorithm for each robot, that is described here, morphs from just simple mechanical maneuvering strategies to biologically inspired strategies. These strategies are adapted from many realms. The realm of educational psychology, produced reinforcement learning and Q-learning, commerce produced concepts of market-driven economy, engineering with its potential field, AI with its petri-nets, neural network and fuzzy logic. Even insect and fish were simulated in PSO and have been adapted into robot soccer. All these strategies are surveyed in this paper. Another aspect surveyed here is the vision system trend that is shifting from global vision, to local omni-directional vision, to front-facing local vision, which shows the evolution is towards biologically inspired robot soccer agent, the human soccer player.Artificial Intelligence Review 10/2013; DOI:10.1007/s10462-011-9284-0 · 0.90 Impact Factor
12/2006, Degree: PhD Robotics, Supervisor: Anthony Stentz