Emergent coordination through the use of cooperative state-changing rules
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning
Designing a Family of Coordination Algorithms
Designing a Family of Coordination Algorithms
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Planning with concurrent interacting actions
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Ant-ViBRA: A Swarm Intelligence Approach to Learn Task Coordination
SBIA '02 Proceedings of the 16th Brazilian Symposium on Artificial Intelligence: Advances in Artificial Intelligence
Comparing Distributed Reinforcement Learning Approaches to Learn Agent Coordination
IBERAMIA 2002 Proceedings of the 8th Ibero-American Conference on AI: Advances in Artificial Intelligence
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Research in Multi-Agents Systems (MAS) has been, from its outset, concerned with coordinating intelligent behavior among a collection of autonomous intelligent agents. In the last years the use of on-line learning approaches to achieve coordination has attracted an increasing attention. The purpose of this work is to use a Reinforcement Learning approach in the job of learning how to coordinate agent actions in a MAS, aiming to minimize the task execution time. To achieve this goal, a control agent with learning capabilities is introduced in an agent society. The domain on which the system is applied consists of visually guided assembly tasks such as picking up pieces, performed by a manipulator working in an assembly cell. Since RL requires a large amount of learning trials, the approach was tested in a simulated domain. From the experiments carried out we conclude that RL is a feasible approach leading to encouraging results.