- author = "Pawel Lichocki",
- title = "Evolution of division of labor in artificial societies",
- school = "Ecole Polytechnique Federale de Lausanne",
- year = "2013",
- address = "Switzerland",
- month = "22 " # mar,
- keywords = "genetic algorithms, genetic programming, artificial evolution, multi-agent systems, cooperation, division of labour, specialisation, team composition, task allocation, response thresholds, artificial neural networks, simulations, evolutionary computation, selection method, crossover. Evolution artificielle, systemes multi-agents, cooporation, division du travail, specialisation, composition des equipes, allocation des taches, seuil de raponse, reseaux neuronaux artificiels, simulations, algorithmes evolutionnaires, methode de selection, enjambement",
-
URL = "
http://infoscience.epfl.ch/record/184959/files/EPFL_TH5687.pdf",
- size = "162 pages",
-
abstract = "Natural and artificial societies often divide the
workload between specialised members. For example, an
ant worker may preferentially perform one of many tasks
such as brood rearing, foraging and nest maintenance. A
robot from a rescue team may specialise in search,
obstacle removal, or transportation. Such division of
labour is considered crucial for efficient operation of
multi-agent systems and has been studied from two
perspectives. First, scientists address the how
question seeking for mechanical explanations of
division of labour. The focus has been put on
behavioural and environmental factors and on task
allocation algorithms leading to specialisation.
Second, scientists address the why question uncovering
the origins of division of labour. The focus has been
put on evolutionary pressures and optimisation
procedures giving rise to specialisation. Studies have
usually addressed one of these two questions in
isolation, but for a full understanding of division of
labour the explanation of the origins of specific
mechanisms is necessary. Here, we rise to this
challenge and study three major transitions related to
division of labour. By means of theoretical analyses
and evolutionary simulations, we construct a pathway
from the occurrence of cooperation, through fixed
castes, up to dynamic task allocation.
First, we study conditions favouring the evolution of cooperation, as it opens the doors for the potentially following specialisation. We demonstrate that these conditions are sensitive to the mechanisms of intra-specific selection (or selection methods). Next, we take an engineering perspective and we study division of labour at the genetic level in teams of artificial agents. We devise efficient algorithms to evolve fixed assignments of agents to castes (or team compositions). To this end, we propose a novel technique that exchanges agents between teams, which greatly eases the search for the optimal composition. Finally, we take a biological perspective and we study division of labour at the behavioural level in simulated ant colonies. We quantify the efficiency of task allocation algorithms, which have been used to explain specialisation in social insects. We show that these algorithms fail to induce precise reallocation of the workforce in response to changes in the environment. We overcome this issue by modelling task allocation with artificial neural networks, which lead to near optimal colony performance.
Overall, this work contributes both to biology and to engineering. We shed light on the evolution of cooperation and division of labour in social insects, and we show how to efficiently optimise teams of artificial agents. We resolve the encountered methodological issues and demonstrate the power of evolutionary simulations to address biological questions and to tackle engineering problems.",
-
notes = "Is this GP?
EPFL Presentee le 22 mars 2013 Suisse
THESE NO 5687 (2013)
acceptee sur proposition du jury: Prof. A. Billard, presidente du jury Prof. D. Floreano, Prof. L. Keller, directeurs de these Prof. M.-O. Hongler, rapporteur Prof. L. Lehmann, rapporteur",
