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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.
Author
Organization
Project
Bioinformatics: from nucleotids to networks (N2N)
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.
Keywords
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

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Citation

Please use this url to cite or link to this publication:

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.
@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},
}

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