Uncovering latent behaviors in ant colonies

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Version: Author's accepted manuscript
Serval ID
serval:BIB_AC76378DAE50
Type
Article: article from journal or magazin.
Publication sub-type
Review (review): journal as complete as possible of one specific subject, written based on exhaustive analyses from published work.
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Publications
Institution
Title
Uncovering latent behaviors in ant colonies
Journal
Proceedings of the 2016 SIAM International Conference on Data Mining
Author(s)
Kafsi M., Braunschweig R., Mersch D., Grossglauser M., Keller L., Thiran P.
ISBN
978-1-61197-434-8
Publication state
Published
Issued date
2016
Volume
51
Pages
450-458
Language
english
Abstract
Many biological systems exhibit collective behaviors that strengthen their adaptability to their environment, compared to more solitary species. Describing these behaviors is challenging yet necessary in order to understand these biological systems. We propose a probabilistic model that enables us to uncover the collective behaviors observed in a colony of ants. This model is based on the assumption that the behavior of an individual ant is a time-dependent mixture of latent behaviors that are specific to the whole colony. We apply this model to a large-scale dataset obtained by observing the mobility of nearly 1000 Camponotus fellah ants from six different colonies. Our results indicate that a colony typically exhibits three classes of behaviors, each characterized by a specific spatial distribution and a level of activity. Moreover, these spatial distributions, which are uncovered automatically by our model, match well with the ground truth as manually annotated by domain experts. We further explore the evolution of the behavior of individual ants and show that it is well captured by a second order Markov chain that encodes the fact that the future behavior of an ant depends not only on its current behavior but also on its preceding one.
Create date
15/09/2016 12:31
Last modification date
20/08/2019 15:16
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