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.
