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As mobile robots become increasingly autonomous over extended periods of time, opportunities arise for their use on repetitive tasks. We define and implement behaviors for a class of such tasks that we call continuous area sweeping tasks. A continuous area sweeping task is one in which a group of robots must repeatedly visit all points in a fixed area,(More)
— The prediction of the future states in Multi-Agent Systems has been a challenging problem since the begining of MAS. Robotic soccer is a MAS environment in which the predictions of the opponents strategy, or opponent modeling, plays an important role. In this paper, a novel case-based architecture is applied in the soccer coach that learns and predicts(More)
For many distributed autonomous robotic systems, it is important to maintain communication connectivity among the robots. That is, each robot must be able to communicate with each other robot, perhaps through a series of other robots. Ideally, this property should be robust to the removal of any single robot from the system. In this work, we define a(More)
For many distributed autonomous robotic systems, it is important to maintain communication connectivity among the robots. That is, each robot must be able to communicate with each other robot, perhaps through a series of other robots. Ideally, this property should be robust to the removal of any single robot from the system. In (Ahmadi & Stone 2006a) we(More)
In this paper we will study dynamical modeling of a parallel robot Hexa using Lagrangian equation of the first type. Because of complexity and nonlinearity of parallel robotspsila relationships few works are done on their dynamical modeling. Although Newtonian approach is a straightforward method, it may not directly result in a suitable model for most of(More)
Forward kinematics problem of parallel robots is very difficult to solve in comparison to the serial manipulators because of the highly nonlinear relations between joint variables and position and orientation of the end effector. This problem is almost impossible to solve analytically. Numerical methods are one of the common solutions for this problem. But,(More)
Reinforcement learning is a popular and successful framework for many agent-related problems because only limited environmental feedback is necessary for learning. While many algorithms exist to learn effective policies in such problems, learning is often used to solve real world problems, which typically have large state spaces, and therefore suffer from(More)
— One of the most important issues in multi-agent systems is communication. The agents with more comprehensive information (which can be a human observer) advise other agents by sending messages. Similar agents may also communicate to transfer their experience. These messages are called advice. In this paper a general method, which is applicable for all(More)