Costs and benefits of behavioral specialization

Authors:
Arne Brutschy;Nam-Luc Tran;Nadir Baiboun;Marco Frison;Giovanni Pini;Andrea Roli;Marco Dorigo;Mauro Birattari
Affiliations:
IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium and ECAM, Institut Supérieur Industriel, Rue du Tir 14, 1060 Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium and DEIS-Cesena, Alma Mater Studiorum Universití di Bologna, Via Montalti 69, 47521 Cese ...;IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium;DEIS-Cesena, Alma Mater Studiorum Universití di Bologna, Via Montalti 69, 47521 Cesena, Italy and IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels ...;IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Avenue Franklin Roosevelt 50, 1050 Brussels, Belgium
Venue:
Robotics and Autonomous Systems
Year:
2012

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Editorial: TAROS2011

Robotics and Autonomous Systems
Costs and benefits of behavioral specialization

Robotics and Autonomous Systems

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Abstract

In this work, we study behavioral specialization in a swarm of autonomous robots. In the studied swarm, robots have to carry out tasks of different types that appear stochastically in time and space in a given environment. We consider a setting in which a robot working repeatedly on tasks of the same type improves its performance on them due to learning. Robots can exploit learning by adapting their task selection behavior, that is, by selecting with higher probability tasks of the type on which they have improved their performance. This adaptation of behavior is called behavioral specialization. We employ a simple task allocation strategy that allows a swarm of robots to behaviorally specialize. We study the influence of different environmental parameters on the performance of the swarm and show that the swarm can exploit learning successfully. However, there is a trade-off between the benefits and the costs of specialization. We study this trade-off in multiple experiments using different swarm sizes. Our experimental results indicate that spatiality has a major influence on the costs and benefits of specialization.