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Author(s): Hirsbrunner, B. | PerezUribe, A. |
Year: 2000
Citation: SAB2000 Proceedings Supplement Book, Meyer, Berthoz, Floreano, Roitblat and Wilson (Eds), Published by International Society for Adaptive Behavior, Honolulu, 2000, pp. 185-194.
Abstract: Honey-bees have long served as a model organism for investigating insect navigation and collective behavior: they exhibit division of labor and are an example of insect societies where direct communication between workers enable cooperation in the task of collecting nectar and pollen for the colony. However, honey-bees seem to learn about their environment progressively before becoming foragers and displaying the very complex collective behaviors that have inspired researchers interested in collective intelligence. Motivated by recent researches by biologists and neuroscientists on the individual learning in honey-bees, we have implemented a hebbian-learning model and tested it in a foraging task with an autonomous mobile robot (a robot-bee). Then, we used a second learning model that merges unsupervised learning and reinforcement learning techniques. We present some experimental results, as well as the advantages and disadvantages of both models, and describe future directions of research.
Topics:
Robotics,
Applications:
Information Fusion,
Models:
Modified ART,
