Ant foraging is a paradigmatic example of self-organized behavior. We give new experimental evidence for previously unobserved short-term adaptiveness in ant foraging and show that current mathematical foraging models cannot predict this behavior. As a true extension, we develop Itô diffusion models that explain the newly discovered behavior qualitatively and quantitatively. The theoretical analysis is supported by individual-based simulations. Our work shows that randomness is a key factor in allowing self-organizing systems to be adaptive. Implications for technical applications of Swarm Intelligence are discussed. © 2008 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Meyer, B., Beekman, M., & Dussutour, A. (2008). Noise-induced adaptive decision-making in ant-foraging. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5040 LNAI, pp. 415–425). Springer Verlag. https://doi.org/10.1007/978-3-540-69134-1_41
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