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

Yao Yao,Veronique Storme,Yves Van De Peer,Kathleen Marchal

doi:10.7717/peerj.2812

Yao Yao, Veronique Storme + Show 2 more

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https://doi.org/10.7717/peerj.2812

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Journal: PeerJ	Publication Date: Dec 21, 2016
Citations: 3	License type: CC BY 4.0

Affiliation: Ghent University, University of Pretoria

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.

Highlights

In biology, evolutionary systems of all kinds, such as gene regulatory networks, organisms, populations, and even entire ecological communities can be regarded as complex systems of many interacting components
To set up a computational framework to study multilevel evolutionary processes and adaptation in complex systems, and to gain further insights into how adaptation in a changing environment might evolve, we have developed a robot controller that combines an artificial genome with an agent-based system that represents the active Gene Regulatory Network(s) or gene regulatory network (GRN)(s)
Prey behaviour represents the competitive relationship between robots (Nolfi & Floreano, 1998), while the aggregation behaviour represents the symbiotic relationships between individual robots (Yao, Marchal & Van de Peer, 2016)

Summary

Introduction

Evolutionary systems of all kinds, such as gene regulatory networks, organisms, populations, and even entire ecological communities can be regarded as complex systems of many interacting components. These interacting components have not evolved independently and in isolation, but in concert throughout different levels of organization. Complex adaptation requires more than one novel mutation to yield a functional advantage (Lynch & Abegg, 2010) and even if one single ‘mutation’ could lead to a novel trait (for instance in the case of genetically modified organisms where one gene can be introduced to confer a novel phenotype), the novel trait still needs to exist and persist in a biological context (Williamson, 1992; Prakash, 2001)

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