Evolution of Self-Organized Task Specialization in Robot Swarms
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{oai:HAL:hal-01378166v1,
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author = "Eliseo Ferrante and Ali Turgut and
Edgar Duenez-Guzman and Marco Dorigo and Tom Wenseleers",
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title = "Evolution of Self-Organized Task Specialization in
Robot Swarms",
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journal = "PLoS Computational Biology",
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year = "2015",
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volume = "11",
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pages = "1004273--1004273",
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month = aug # " 6",
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keywords = "genetic algorithms, genetic programming, artificial
intelligence, machine learning, multiagent systems
nonlinear sciences, adaptation and self-organising
systems",
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ISSN = "1553-734X; 1553-7358",
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bibsource = "OAI-PMH server at api.archives-ouvertes.fr",
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identifier = "hal-01378166",
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DOI = "doi:10.1371/journal.pcbi.1004273.s009",
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language = "en",
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oai = "oai:HAL:hal-01378166v1",
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relation = "info:eu-repo/semantics/altIdentifier/doi/10.1371/journal.pcbi.1004273.s009",
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URL = "https://hal.archives-ouvertes.fr/hal-01378166",
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URL = "https://hal.archives-ouvertes.fr/hal-01378166/document",
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URL = "https://hal.archives-ouvertes.fr/hal-01378166/file/2015_PlosComputationalBiology.pdf",
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DOI = "doi:10.1371/journal.pcbi.1004273",
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size = "21 pages",
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abstract = "Division of labour is ubiquitous in biological
systems, as evidenced by various forms of complex task
specialization observed in both animal societies and
multicellular organisms. Although clearly adaptive, the
way in which division of labor first evolved remains
enigmatic, as it requires the simultaneous
co-occurrence of several complex traits to achieve the
required degree of coordination. Recently, evolutionary
swarm robotics has emerged as an excellent test bed to
study the evolution of coordinated group-level
behaviour. Here we use this framework for the first
time to study the evolutionary origin of behavioural
task specialization among groups of identical robots.
The scenario we study involves an advanced form of
division of labour, common in insect societies and
known as task partitioning, whereby two sets of tasks
have to be carried out in sequence by different
individuals. Our results show that task partitioning is
favoured whenever the environment has features that,
when exploited, reduce switching costs and increase the
net efficiency of the group, and that an optimal mix of
task specialists is achieved most readily when the
behavioural repertoires aimed at carrying out the
different subtasks are available as pre-adapted
building blocks. Nevertheless, we also show for the
first time that self-organized task specialization
could be evolved entirely from scratch, starting only
from basic, low-level behavioural primitives, using a
nature-inspired evolutionary method known as
Grammatical Evolution. Remarkably, division of labour
was achieved merely by selecting on overall group
performance, and without providing any prior
information on how the global object retrieval task was
best divided into smaller subtasks. We discuss the
potential of our method for engineering adaptively
behaving robot swarms and interpret our results in
relation to the likely path that nature took to evolve
complex sociality and task specialization.",
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notes = "Is this GP?",
- }
Genetic Programming entries for
Eliseo Ferrante
Ali Turgut
Edgar Duenez-Guzman
Marco Dorigo
Tom Wenseleers
Citations