TY - GEN
T1 - Emergence of Specialised Collective Behaviors in Evolving Heterogeneous Swarms
AU - van Diggelen, Fuda
AU - de Carlo, Matteo
AU - Cambier, Nicolas
AU - Ferrante, Eliseo
AU - Eiben, Guszti
N1 - Publisher Copyright:
© The Author(s) 2024.
PY - 2024
Y1 - 2024
N2 - Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability; 2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.
AB - Natural groups of animals, such as swarms of social insects, exhibit astonishing degrees of task specialization, useful for solving complex tasks and for survival. This is supported by phenotypic plasticity: individuals sharing the same genotype that is expressed differently for different classes of individuals, each specializing in one task. In this work, we evolve a swarm of simulated robots with phenotypic plasticity to study the emergence of specialized collective behavior during an emergent perception task. Phenotypic plasticity is realized in the form of heterogeneity of behavior by dividing the genotype into two components, with a different neural network controller associated to each component. The whole genotype, which expresses the behavior of the whole group through the two components, is subject to evolution with a single fitness function. We analyze the obtained behaviors and use the insights provided by these results to design an online regulatory mechanism. Our experiments show four main findings: 1) Heterogeneity improves both robustness and scalability; 2) The sub-groups evolve distinct emergent behaviors. 3) The effectiveness of the whole swarm depends on the interaction between the two sub-groups, leading to a more robust performance than with singular sub-group behavior. 4) The online regulatory mechanism improves overall performance and scalability.
KW - Evolutionary robotics
KW - Heterogeneous swarm
KW - Swarm robotics
UR - http://www.scopus.com/inward/record.url?scp=85204605799&partnerID=8YFLogxK
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U2 - 10.1007/978-3-031-70068-2_4
DO - 10.1007/978-3-031-70068-2_4
M3 - Conference contribution
AN - SCOPUS:85204605799
SN - 9783031700675
VL - 2
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 53
EP - 69
BT - Parallel Problem Solving from Nature – PPSN XVIII
A2 - Affenzeller, Michael
A2 - Winkler, Stephan M.
A2 - Kononova, Anna V.
A2 - Bäck, Thomas
A2 - Trautmann, Heike
A2 - Tušar, Tea
A2 - Machado, Penousal
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Conference on Parallel Problem Solving from Nature, PPSN 2024
Y2 - 14 September 2024 through 18 September 2024
ER -