Kemal Kaplan

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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 multiagent coordination. Robot soccer differs from many other multi-agent problems with its highly dynamic and complex nature. Market-driven(More)
This work proposes a novel approach for introducing market-driven strategy to robot soccer domain in order to solve vital issues related to multiagent coordination. 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. Market-driven approach(More)
This paper proposes a set of practical extensions to the vision-based Monte Carlo localization for RoboCup Sony AIBO legged robot soccer domain. The main disadvantage of AIBO robots is that they have a narrow field of view so the number of landmarks seen in one frame is usually not enough for geometric calculation. MCL methods have been shown to be accurate(More)
In this paper, two expert system implementations for the Automatic Driver Evaluation System (ADES) Project are introduced. These expert systems are used for deciding whether a driver violates the traffic rules or not, according to the facts derived from the data acquired by the sensors. Sample scenarios are executed in a highly realistic simulation(More)
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