Adaptive system
Explanation
Biological adaptation
The term
adaptation is used in biology in relation to how living beings
adapt to their environments, but with two different meanings.
First, the continuous adaptation of an organism to its
environment, so as to maintain itself in a viable state, through
sensory feedback mechanisms. Second, the development (through
evolutionary steps) of an adaptation (an anatomic structure,
physiological process or behavior characteristic) that increases
the probability of an organism reproducing itself (although
sometimes not directly).
General definition
Generally speaking, an adaptive system is a set of interacting or
interdependent entities, real or abstract, forming an integrated
whole that together are able to respond to environmental changes
or changes in the interacting parts. Feedback loops
represent a key feature of adaptive systems, allowing the
response to changes; examples of adaptive systems include:
natural ecosystems, individual organisms, human communities,
human organizations, and human families. Some artificial systems
can be adaptive as well; for instance, robots employ control
systems that utilize feedback loops to sense new conditions in
their environment and adapt accordingly.
The Law of Adaptation
Every adaptive system converges to a state in which all kind of
stimulation ceases.
Benefit of Self-Adjusting Systems
In an adaptive system, a parameter changes slowly and has no
preferred value. In a self-adjusting system though, the parameter
value “depends on the history of the system dynamics”. One of the
most important qualities of self-adjusting systems is its
“adaption to the edge of chaos” or ability to avoid chaos.
Practically speaking, by heading to the edge of chaos without
going further, a leader may act spontaneously yet without
disaster.
Adaptation across levels of organization
A theory of how systems adapt across different levels of
organisation is called practopoiesis. According to that theory,
the purpose of an adaptation processes at each lower level of
organisation is creation of the adaptation mechanism at the next
higher level of organisation. For a living system such as an
animal or a person, a total of three such hierarchical steps of
adaptation are needed — and such systems are denoted as
T3:
-
At the lowest level of a T3-system lay gene expression
mechanisms, which, when activated, produce machinery that can
adapt the system at higher levels of organization.
-
The next higher level corresponds to various physiological
structures other than gene expression mechanisms. In the
nervous system, these higher mechanisms adjust the properties
of the neural circuitry such that they operate with the pace
much faster than the gene expression mechanisms. These faster
adaptive mechanisms are responsible for e.g., neural
adaptation.
-
Finally, at the top of that adaptive hierarchy lays the
electrochemical activity of neuronal networks together with the
contractions of the muscles. At this level the behaviour of the
organism is generated.
When an entire species is considered as an adaptive system, one
more level of organization must be included: the evolution by
natural selection—making a total of four adaptive levels, or a
T4-system.
Artificial Systems
In contrast, artificial systems such as machine learning
algorithms or neural networks are adaptive only at two levels or
organizations (T2). According to practopoiesis, this lack of a
deeper adaptive hierarchy of machines is the main limitation
factor for their capability to achieve intelligence.
Linguistic derivation
The term Adaptive is derived from the Latin verb
adaptāre, which is a combination of the prefix
ad- meaning "to; at" + verb aptāre meaning "to
fit".
The term System is derived from Latin systēma,
which may originate from the Greek word
sustēma (σύστημα), which is a
combination of the prefix syn- meaning "with;
together" + verb histanai meaning "to cause; to
stand".
External Sources
http://en.wikipedia.org/wiki/Adaptive_system
Book:
José Antonio Martín H., Javier de Lope and Darío Maravall:
"Adaptation, Anticipation and Rationality in Natural and
Artificial Systems: Computational Paradigms Mimicking
Nature" Natural Computing, December, 2009. Vol. 8(4), pp.
757-775.