10.5061/DRYAD.7PN80
Ferrante, Eliseo
Zoological Institute
Turgut, Ali Emre
Middle East Technical University
Duéñez Guzmán, Edgar
Zoological Institute
Dorigo, Marco
Université Libre de Bruxelles
Wenseleers, Tom
Zoological Institute
Duéñez-Guzmán, Edgar
Zoological Institute
Data from: Evolution of self-organized task specialization in robot swarms
Dryad
dataset
2015
Evolutionary Swarm Robotics
division of labor
Task Specialization
Embodied Multi-Agent Simulations
Evolutionary biology
2015-07-15T18:32:12Z
2015-07-15T18:32:12Z
en
https://doi.org/10.1371/journal.pcbi.1004273
491692 bytes
1
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Division of labor 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 behavior. Here we use this framework for the first time to
study the evolutionary origin of behavioral task specialization among
groups of identical robots. The scenario we study involves an advanced
form of division of labor, 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 favored 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
behavioral 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 behavioral
primitives, using a nature-inspired evolutionary method known as
Grammatical Evolution. Remarkably, division of labor 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.
Data Figure 3Data to reproduce Figure 3, ternary plots showing group
performance of handcoded controller in flat and sloped environment. The
first field indicates the length of the sloped part of the environment (6
meters for the sloped environment, 0 for the flat), the following three
indicate the number of robots engaging in the three strategies, the fifth
field indicates the fitness and the last field the number of items dropped
in the cache.dataFigure3.zipData Figure 4Data to reproduce Figure 4,
showing group performance and amount of task partitioning over subsequent
generations for each of the 22 evolutionary runs. The first three fields
indicate the ID of the evolutionary run, generation and repetition
(respectively), the fourth and fifth fields indicate the number items
retrieved in a partitioned and non-partitioned way (respectively, the sum
of which row-wise corresponds to fitness), and the sixth indicates the
average absolute robot speed projected along the main axis of the
environment.dataFigure4.zipData Figure 5Data to reproduce Figure 5, robot
densities and trajectories for a typical run. The first fields indicates
the timestep, the second, third, fourth and fifth fields indicate the
x-axis (main environment axis) coordinate of the four robots, and the last
field contains the number of items present in the
cache.dataFigure5.zipData Figure 6Data to reproduce Figure 6, showing the
effect of the degree of task specialization and average linear speed on
the fitness performance of the 22 controllers evolved from first
principles. The first and second fields indicate the ID of the
evolutionary run and of the repetition, the third and fourth fields
indicate the number of items retrieved in a partitioned and
non-partitioned way (respectively), the fifth field indicates the
proportion of items retrieved in a task partitioned way, the sixth and
seventh fields indicate the average linear speed and the same quantity as
a proportion of the maximum theoretical speed, and the last field
indicates the fitness.dataFigure6.zip