Peer Reviewed Journal via three different mandatory reviewing processes, since 2006, and, from September 2020, a fourth mandatory peer-editing has been added.
Previous work has shown that robot navigation systems that
employ an architecture based upon the idiotypic network theory
of the immune system have an advantage over control
techniques that rely on reinforcement learning only. This is
thought to be a result of intelligent behaviour selection on the
part of the idiotypic robot. In this paper an attempt is made to
imitate idiotypic dynamics by creating controllers that use
reinforcement with a number of different probabilistic schemes
to select robot behaviour. The aims are to show that the
idiotypic system is not merely performing some kind of periodic
random behaviour selection, and to try to gain further insight
into the processes that govern the idiotypic mechanism. Trials
are carried out using simulated Pioneer robots that undertake
navigation exercises. Results show that a scheme that boosts the
probability of selecting highly-ranked alternative behaviours to
50% during stall conditions comes closest to achieving the
properties of the idiotypic system, but remains unable to match
it in terms of all round performance.
