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, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles and ECAM, Institut Supérieur Industriel, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium and DEIS-Cesena, Alma Mater Studiorum Università di Bologna, Cesena, Italy;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;DEIS-Cesena, Alma Mater Studiorum Università di Bologna, Cesena, Italy and IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium;IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

  • Venue:
  • TAROS'11 Proceedings of the 12th Annual conference on Towards autonomous robotic systems
  • Year:
  • 2011

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Abstract

In this work, we study behavioral specialization in a swarm of autonomous robots. In the studied swarm, a robot working repeatedly on the same type of task improves in task performance due to learning. Robots may exploit this positive effect of learning by selecting with higher probability the tasks on which they have improved their performance. However, even though the exploitation of such performanceimproving effects is clearly a benefit, specialization also entails certain costs. Using a task allocation strategy that allows the robots to behaviorally specialize, we study the trade-off between costs and benefits in simulation experiments. Additionally, we give a perspective on the impact of this trade-off in systems that use specialization.