In this work we propose the Ant-ViBRA system, which uses a Swarm Intelligence Algorithm that combines a Reinforcement Learning (RL) approach with Heuristic Search in order to coordinate agent actions in a Multi Agent System. The goal of Ant-ViBRA is to create plans that minimize the execution time of assembly tasks. To achieve this goal, a swarm algorithm called the Ant Colony System algorithm (ACS) was modified to be able to cope with planning when several agents are involved in a combinatorial optimization problem where interleaved execution is needed. Aiming at the reduction of the learning time, Ant-ViBRA uses a priori domain knowledge to decompose the assembly problem into subtasks and to define the relationship between actions and states based on the interactions among subtasks. Ant-ViBRA was applied to the domain of visually guided assembly tasks performed by a manipulator working in an assembly cell. Results acquired using Ant-ViBRA are encouraging and show that the combination of RL, Heuristic Search and the use of explicit domain knowledge presents better results than any of the techniques alone.
CITATION STYLE
Bianchi, R. A. C., & Costa, A. H. R. (2002). Ant-ViBRA: A swarm intelligence approach to learn task coordination. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2507, pp. 195–205). Springer Verlag. https://doi.org/10.1007/3-540-36127-8_19
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