  Jerne's idiotypic network theory postulates that the immune response involves
inter-antibody stimulation and suppression as well as matching to antigens. The
theory has proved the most popular Artificial Immune System (ais) model for
incorporation into behavior-based robotics but guidelines for implementing
idiotypic selection are scarce. Furthermore, the direct effects of employing
the technique have not been demonstrated in the form of a comparison with
non-idiotypic systems. This paper aims to address these issues. A method for
integrating an idiotypic ais network with a Reinforcement Learning based
control system (rl) is described and the mechanisms underlying antibody
stimulation and suppression are explained in detail. Some hypotheses that
account for the network advantage are put forward and tested using three
systems with increasing idiotypic complexity. The basic rl, a simplified hybrid
ais-rl that implements idiotypic selection independently of derived
concentration levels and a full hybrid ais-rl scheme are examined. The test bed
takes the form of a simulated Pioneer robot that is required to navigate
through maze worlds detecting and tracking door markers.
