  Evolutionary techniques driven by behavioural diversity, such as novelty
search, have shown significant potential in evolutionary robotics. These
techniques rely on priorly specified behaviour characterisations to estimate
the similarity between individuals. Characterisations are typically defined in
an ad hoc manner based on the experimenter's intuition and knowledge about the
task. Alternatively, generic characterisations based on the sensor-effector
values of the agents are used. In this paper, we propose a novel approach that
allows for systematic derivation of behaviour characterisations for
evolutionary robotics, based on a formal description of the agents and their
environment. Systematically derived behaviour characterisations (SDBCs) go
beyond generic characterisations in that they can contain task-specific
features related to the internal state of the agents, environmental features,
and relations between them. We evaluate SDBCs with novelty search in three
simulated collective robotics tasks. Our results show that SDBCs yield a
performance comparable to the task-specific characterisations, in terms of both
solution quality and behaviour space exploration.
