emergent adaptive behaviour of grn-controlled simulated robots in a changing environment

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ID: 192553
2016
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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.
Reference Key
yao2016peerjemergent Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Yao Yao;Veronique Storme;Kathleen Marchal;Yves Van de Peer
Journal pediatrics
Year 2016
DOI
10.7717/peerj.2812
URL
https://peerj.com/articles/2812/
Keywords
complex adaptive systems artificial life emergent behaviourmedicine

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