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Garbage
Middle
Far
Near
Robot
Battery charge
Garbage can
(a)
Paratope
Idiotope
Desirable
condition
Interacting antibodies
and degree of interaction
Action
(b)
Figure 7.9 (a) Environment for testing the consensus-making algorithm based
on immune networks, (b) defi nition of antibody.
concentration level of antibodies, and “metadynamics,” which maintained the
appropriate repertoire of antibodies. h is work was then extended by the authors to
attempt in creating a more emergent behavior within the network of robots (Kondro
et al., 1998) by the introduction of genetic operators.
KrishnaKumar et al. (1995) and KrishnaKumar and Neidhoefer (1997a, 1997b,
1999) proposed an “immunized computational systems” (ICS). h ey successfully
tested ICS on an autonomous aircraft control problem. Ootsuki and Sekiguchi
(1999) suggested a method for determining control sequences of a sequential con-
trol plant based on the immune system. h ey used Petri nets with control sequences
equal to the fi ring sequences of a Petri net.
Attempts have been made to apply the immune network idea to control large
populations of robots to have some form of a self-organizing group behavior. Toma
et al. (1999) attempted to create a group of robots, which self-organized to search
for food without any global control mechanism. h e authors used B cells to rep-
resent a robot and its stimulation is calculated according to its performance. Each
robot carried a record of its degree of success in collecting food, while neighboring
robots compared their success and strategies and stimulated and suppressed each
other accordingly. h is work was advanced by Mitsumoto et al. (1996) with the
introduction of the idea of B cell cloning to represent messages to other robots.
If a robot is achieving the work, then it receives more stimulation. After a certain
stimulation threshold, it produces clone B cells. Also, Mitsumoto et al. (1997) and
Lee and Sim (1997) used metaphors based on T cell behavior in robotics.
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