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Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence

Franziska Boenisch, Benjamin Rosemann, Benjamin Wild, David Dormagen, Fernando Wario and Tim Landgraf* Dahlem Center for Machine Learning and Robotics, FB Mathematik und Informatik, Freie Universität Berlin, Berlin, Germany Edited by: Vito Trianni, Istituto di Scienze e Tecnologie della Cognizione (ISTC)-CNR, Italy Reviewed by: Athanasios Voulodimos, National Technical University of Athens, Greece Bertrand Collignon, Eidgenössische Technische Hochschule Lausanne, Switzerland *Correspondence: Tim Landgraf tim.landgraf@fu-berlin.de Specialty section: This article was submitted to Computational Intelligence, a section of the journal Frontiers in Robotics and AI Received: 30 November 2017 Accepted: 16 March 2018 Published: 04 April 2018 Citation: Boenisch F, Rosemann B, Wild B, Dormagen D, Wario F and Landgraf T (2018) Tracking All Members of a Honey Bee Colony Over Their Lifetime Using Learned Models of Correspondence. AI 5:35. doi: 10.3389/frobt.2018.00035 Computational approaches to the analysis of collective behavior in social insects increasingly rely on motion paths as an intermediate data layer from which one can infer individual behaviors or social interactions. Honey bees are a popular model for learning and memory. Previous experience has been shown to affect and modulate future social interactions.