  This paper explores the use of a novel form of Hierarchical Graph Neurons
(HGN) for in-operation behaviour selection in a swarm of robotic agents. This
new HGN is called Robotic-HGN (R-HGN), as it matches robot environment
observations to environment labels via fusion of match probabilities from both
temporal and intra-swarm collections. This approach is novel for HGN as it
addresses robotic observations being pseudo-continuous numbers, rather than
categorical values. Additionally, the proposed approach is memory and
computation-power conservative and thus is acceptable for use in mobile devices
such as single-board computers, which are often used in mobile robotic agents.
This R-HGN approach is validated against individual behaviour implementation
and random behaviour selection. This contrast is made in two sets of simulated
environments: environments designed to challenge the held behaviours of the
R-HGN, and randomly generated environments which are more challenging for the
robotic swarm than R-HGN training conditions. R-HGN has been found to enable
appropriate behaviour selection in both these sets, allowing significant swarm
performance in pre-trained and unexpected environment conditions.
