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Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment

Yao Yao UGent, Veronique Storme UGent, Kathleen Marchal UGent and Yves Van de Peer UGent (2016) PEERJ. 4.
abstract
We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
IBCN, Complex adaptation, Complex adaptive systems, Self-organizing systems, Artificial life, Swarm robots, Emergent behaviour, MULTILEVEL SELECTION, ECOLOGICAL-SYSTEMS, GAME-THEORY, EVOLUTION, COMPLEXITY, COST, BIOLOGY, PERSPECTIVE, ADAPTATION, NETWORKS
journal title
PEERJ
PeerJ
volume
4
article number
e2812
pages
20 pages
Web of Science type
Article
Web of Science id
000390663600012
JCR category
MULTIDISCIPLINARY SCIENCES
JCR impact factor
2.177 (2016)
JCR rank
20/64 (2016)
JCR quartile
2 (2016)
ISSN
2167-8359
DOI
10.7717/peerj.2812
project
Bioinformatics: from nucleotids to networks (N2N)
language
English
UGent publication?
yes
classification
A1
copyright statement
Creative Commons Attribution 4.0 International Public License (CC-BY 4.0)
id
8501742
handle
http://hdl.handle.net/1854/LU-8501742
date created
2017-01-12 12:51:12
date last changed
2017-04-25 07:26:20
@article{8501742,
  abstract     = {We developed a bio-inspired robot controller combining an artificial genome with an agent-based control system. The genome encodes a gene regulatory network (GRN) that is switched on by environmental cues and, following the rules of transcriptional regulation, provides output signals to actuators. Whereas the genome represents the full encoding of the transcriptional network, the agent-based system mimics the active regulatory network and signal transduction system also present in naturally occurring biological systems. Using such a design that separates the static from the conditionally active part of the gene regulatory network contributes to a better general adaptive behaviour. Here, we have explored the potential of our platform with respect to the evolution of adaptive behaviour, such as preying when food becomes scarce, in a complex and changing environment and show through simulations of swarm robots in an A-life environment that evolution of collective behaviour likely can be attributed to bio-inspired evolutionary processes acting at different levels, from the gene and the genome to the individual robot and robot population.},
  articleno    = {e2812},
  author       = {Yao, Yao and Storme, Veronique and Marchal, Kathleen and Van de Peer, Yves},
  issn         = {2167-8359},
  journal      = {PEERJ},
  keyword      = {IBCN,Complex adaptation,Complex adaptive systems,Self-organizing systems,Artificial life,Swarm robots,Emergent behaviour,MULTILEVEL SELECTION,ECOLOGICAL-SYSTEMS,GAME-THEORY,EVOLUTION,COMPLEXITY,COST,BIOLOGY,PERSPECTIVE,ADAPTATION,NETWORKS},
  language     = {eng},
  pages        = {20},
  title        = {Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment},
  url          = {http://dx.doi.org/10.7717/peerj.2812},
  volume       = {4},
  year         = {2016},
}

Chicago
Yao, Yao, Veronique Storme, Kathleen Marchal, and Yves Van de Peer. 2016. “Emergent Adaptive Behaviour of GRN-controlled Simulated Robots in a Changing Environment.” Peerj 4.
APA
Yao, Yao, Storme, V., Marchal, K., & Van de Peer, Y. (2016). Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment. PEERJ, 4.
Vancouver
1.
Yao Y, Storme V, Marchal K, Van de Peer Y. Emergent adaptive behaviour of GRN-controlled simulated robots in a changing environment. PEERJ. 2016;4.
MLA
Yao, Yao, Veronique Storme, Kathleen Marchal, et al. “Emergent Adaptive Behaviour of GRN-controlled Simulated Robots in a Changing Environment.” PEERJ 4 (2016): n. pag. Print.