  Robot-control designers have begun to exploit the properties of the human
immune system in order to produce dynamic systems that can adapt to complex,
varying, real-world tasks. Jernes idiotypic-network theory has proved the most
popular artificial-immune-system (AIS) method for incorporation into
behaviour-based robotics, since idiotypic selection produces highly adaptive
responses. However, previous efforts have mostly focused on evolving the
network connections and have often worked with a single, pre-engineered set of
behaviours, limiting variability. This paper describes a method for encoding
behaviours as a variable set of attributes, and shows that when the encoding is
used with a genetic algorithm (GA), multiple sets of diverse behaviours can
develop naturally and rapidly, providing much greater scope for flexible
behaviour-selection. The algorithm is tested extensively with a simulated
e-puck robot that navigates around a maze by tracking colour. Results show that
highly successful behaviour sets can be generated within about 25 minutes, and
that much greater diversity can be obtained when multiple autonomous
populations are used, rather than a single one.
