Biomedical Engineering Reference
In-Depth Information
INTRODUCTION
or interactions exerted locally upon each other,
either directly or through the environment. These
local interactions are often governed in a simple
manner, via small sets of rules or short equations,
and in some cases the sets of reactive agents used
are best characterized as
particle systems
where
each agent is viewed as a particle. This provides
swarming systems with a sets of properties that
includes scalability, fault tolerance, and perhaps
more importantly, self-organization. Through
this latter property, there is no need for a system
to be controlled in a hierarchical fashion by one
or more central components that determine the
required behavior of the system. Instead, collective
behavior emerges from the local interactions of all
the components, and the global system behaves as
a super-organism of loosely connected parts that
react “intelligently” to the environment.
In our view, the self-organizing feature of
swarm systems represents its main advantage and
also its main disadvantage: the resulting global
behavior is often hard to predict based solely on
the local rules, and in some cases it can be hard
to control the system, that is, to obtain a desired
behavior by imposing local rules on its compo-
nents. This not only can require prolonged, trial-
and-error style tweaking and fine tuning, but even
limits the kinds of problems that can be tackled
by these essentially reactive systems.
In our ongoing research in swarm intelligence
(Grushin, 2006; Lapizco, 2005; Rodriguez, 2004;
Winder, 2004), we have proposed, and shown to
be partially successful, an approach to overcome
these limitations: the introduction of layered con-
trollers into the previously purely reactive particles
or components of a system. The layered controllers
allow each particle to extend its reactive behavior
in a more goal-oriented style, switching between
alternative behaviors in different contexts, while
retaining the locality of the interactions and the
general simplicity of the system. In this way, by
providing a larger, more complex set of behaviors
for the particles and finer control over them, the
resulting system remains self-organizing, but a
The term
swarm intelligence
, initially introduced
by Beni, 1988 in the context of cellular robotics,
refers to a collection of techniques inspired in part
by the behavior of social insects, such as ants, bees,
termites, etc., and of aggregations of animals, such
as flocks, herds, schools, and even human groups
and economic models (Bonabeau, 1999; Kennedy,
2001). These swarms possess the ability to present
remarkably complex and “intelligent” behavior,
despite the apparent lack of relative complexity in
the individuals that form them. These behaviors
can include cooperative synchronized hunting,
coordinated raiding, migration, foraging, path
finding, bridge construction, allocation of labor,
and nest construction. Past discoveries (Deneu-
bourg, 1989) have led investigators to the belief
that such behaviors, although in part produced
by the genetic and physiological structure of
the individuals, are largely caused by the
self-
organization
of the systems they form (Aron,
1990; Bonabeau, 1996). In other words, out of the
direct or indirect local interactions between the
individuals, the collective behavior emerges in a
way that may have the appearance of being glob-
ally organized, although no centralized control or
global communication actually exists. It is pre-
cisely this self-organization that artificial swarm
intelligence systems try to achieve, by infusing
the components, homogeneous or heterogeneous,
of a system with simple rules. Swarm intelligence
presents a novel and promising paradigm for
the design and engineering of complex systems,
increasingly found in many fields of engineering
and science, where the number of elements and
the nature of the interactions among them make it
considerably difficult to model or understand the
system's behavior by traditional methods.
Several methodological approaches to swarm
intelligence have been explored, but they often
share a common feature: collections of simple
entities (simulated birds, ants, vehicles, etc.) move
autonomously through space, controlled by forces
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