Abstract
Honey bees are social insects that forage for flower nectar cooperatively. When an individual forager discovers a flower patch rich in nectar, it returns to the hive and performs a “waggle dance” in the vicinity of other bees that consists of movements communicating the direction and distance to the nectar source. The dance recruits “witnessing” bees to fly to the location of the nectar to retrieve it, thus cooperatively exploiting the environment. Replicating such complex animal behavior is a step forward on the path to artificial intelligence. This project simulates the bee foraging behavior in a cellular automaton using the Morphognosis machine learning model. The model features hierarchical spatial and temporal contexts that output motor responses from sensory inputs. Given a set of bee foraging and dancing exemplars, and exposing only the external input-output of these behaviors to the Morphognosis learning algorithm, a hive of artificial bees can be generated that forage as their biological counterparts do. A comparison of Morphognosis foraging performance with that of an artificial recurrent neural network is also presented.
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Portegys, T.E. (2023). Morphognostic Honey Bees Communicating Nectar Location Through Dance Movements. In: Abraham, A., Hanne, T., Gandhi, N., Manghirmalani Mishra, P., Bajaj, A., Siarry, P. (eds) Proceedings of the 14th International Conference on Soft Computing and Pattern Recognition (SoCPaR 2022). SoCPaR 2022. Lecture Notes in Networks and Systems, vol 648. Springer, Cham. https://doi.org/10.1007/978-3-031-27524-1_72
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DOI: https://doi.org/10.1007/978-3-031-27524-1_72
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