Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Optimisation of a honeybee-colony's energetics via social learning based on queuing delays
Connection Science - Social Learning in Embodied Agents
Economic optimisation in honeybees: adaptive behaviour of a superorganism
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
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Honeybees forage for nectar in a fluctuating environment. Scouts search for new nectar sources and provide information about source location and food quality to the colony. Via a decentralized system, foragers are recruited to nectar sources in appropriate numbers. Without any global decision making, the bees are able to select from multiple available nectar sources the optimal one, the one offering the best ratio of gain to cost. Using our multi-agent simulation of this foraging system that includes nectar sources fluctuating in quality over time in a virtual environment, we found that the honeybee foraging system is robust over a wide variety of fluctuation patterns. We believe that this robustness of a purely decentralized system of decision-making can provide inspiration for technical applications.