Bee-inspired foraging in an embodied swarm
By S. Alers, Daan Bloembergen, Daniel Hennes, Steven de Jong, Michael Kaisers, N. Lemmens, Karl Tuyls, Gerhard Weiss. 2011
17 citations recorded by the source corpus.
We show the emergence of Swarm Intelligence in physical robots. We transfer an optimization algorithm which is based on beeforaging behavior to a robotic swarm. In simulation this algorithm has already been shown to be more effective, scalable and adaptive than algorithms inspired by ant foraging. In addition to this advantage, bee-inspired foraging does not require (de-)centralized simulation of environmental parameters (e.g. pheromones).
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
- Karl Tuyls
- Gerhard Weiss
- Craig Boutilier
- Daan Bloembergen
- Gerhard Weiß
- Sven Gronauer
- Pablo Hernandez-Leal
- Sandip Sen
Citations in the corpus
- Multiagent Learning: Basics, Challenges, and Prospects (2012, methodology): “Examples of MAL approaches from which this classification was derived are Haynes, Lau, and Sen (1996); Vidal and Durfee (1996) (multiplied learning), Sen, Sekaran, and Hale (1994) and Weiss (1993) (divided learning), and Bui, Kieronska, and Venkatesh (1996) and Nagendra Prasad, Lesser, and Lander (1”