Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
A New Distributed Reinforcement Learning Algorithm for Multiple Objective Optimization Problems
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in Artificial Intelligence
L-VIBRA: Learning the VIBRA Architecture
IBERAMIA-SBIA '00 Proceedings of the International Joint Conference, 7th Ibero-American Conference on AI: Advances in 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
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
Efficient metaheuristics for pick and place robotic systems optimization
Journal of Intelligent Manufacturing
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This work compares the performance of the Ant-ViBRA system to approaches based on Distributed Q-learning and Q-learning, when they are applied to learn coordination among agent actions in a Multi Agent System. Ant-ViBRA is a modified version ofa Swarm Intelligence Algorithm called the Ant Colony System algorithm (ACS), which combines a Reinforcement Learning (RL) approach with Heuristic Search. Ant-ViBRA uses a priori domain knowledge to decompose the domain task into subtasks and to define the relationship between actions and states based on interactions among subtasks. In this way, Ant-ViBRA is able to cope with planning when several agents are involved in a combinatorial optimization problem where interleaved execution is needed. The domain in which the comparison is made is that of a manipulator performing visually-guided pick-and-place tasks in an assembly cell. The experiments carried out are encouraging, showing that Ant-ViBRA presents better results than the Distributed Q-learning and the Q-learning algorithms.