Abstract
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.
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Bianchi, R.A.C., Costa, A.H.R. (2002). Ant-ViBRA: A Swarm Intelligence Approach to Learn Task Coordination. In: Bittencourt, G., Ramalho, G.L. (eds) Advances in Artificial Intelligence. SBIA 2002. Lecture Notes in Computer Science(), vol 2507. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36127-8_19
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DOI: https://doi.org/10.1007/3-540-36127-8_19
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