In this paper we describe our approach for an efficient design and implementation of multi-agent systems using agent oriented methodologies and tools. We demonstrate the strength of this approach taking the example of the TAC domain. The trading agent competition (TAC) is a challenging e-marketplace domain for autonomous auction agents. The development process turned out to be very effective with respect to time and success and with the living agents team finishing as the highest scoring team.
In this paper we show that a model-free approach to learn behaviors in joint space can be successfully used to utilize toes of a humanoid robot. Keeping the approach model-free makes it applicable to any kind of humanoid robot, or robot in general. Here we focus on the benefit on robots with toes which is otherwise more difficult to exploit. The task has been to learn different kick behaviors on simulated Nao robots with toes in the RoboCup 3D soccer simulator. As a result, the robot learned to step on its toe for a kick that performs 30% better than learning the same kick without toes.
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