Collective robotics: from social insects to robots
Adaptive Behavior
Ant-based load balancing in telecommunications networks
Adaptive Behavior
Swarm intelligence: from natural to artificial systems
Swarm intelligence: from natural to artificial systems
Task Modelling in Collective Robotics
Autonomous Robots
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 colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
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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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.