Graphics Reference
In-Depth Information
intelligence explosion (a term coined in 1965 by I.
J. Good) and partly because “computer engineers
would possibly build an entity with intelligence at
a level able to compete with human intelligence”
(Joshua Fox, a blog at the Singularity Institute web-
site http://singularity.org/blog/2012/08/). In 2009,
Ray Kurzweil and Peter Diamandis established
a Singularity University in Silicon Valley, CA.
Since then, the theme is being vividly discussed
by the computer scientists, artificial intelligence
researchers, and robotic engineers, for example at
the Association for the Advancement of Artificial
Intelligence conferences and at the Singularity
Institute for Artificial Intelligence and the Future
of Humanity Institute forum.
With these three simple rules, the modeled
flock moves in an extremely realistic way, creat-
ing complex motion and interaction that would be
hard to create otherwise. Collective intelligence
appears in computer networks, business, political
science, sociobiology, mass communication, and
many other domains.
Several games such as the Sims series, and also
Second Life depend on collective intelligence. Data
mining models designed for particular games and
the behavior learning models based on evolutionary
optimization of mixed-games can serve to predict
outcomes in the real-world time-series data analy-
sis such as the stock market (Yu Du, Dong, Quin,
&Wan, 2011). One may imagine future sculptures
placed in public spaces to allow the passer-by people
interact with these sculptures by changing their
appearance and behavior, maybe even competing
for attaining the final result according to their will.
Collective Intelligence
Some animals who form organized groups, such
as schools of fish, flocks of birds, ant colonies,
or swarms of insects, display swarm intelligence
where interactions between individual members
lead to emergence of behavior that is often optimal
for the whole group. Members of a school, flock,
colony, or swarm exhibit collective behavior that
is decentralized and self-organized. This shared
or group intelligence involves cooperation, co-
ordination and collective actions coming from
the consensus decision making. Basic model of
flocking behavior simulated by Craig Reynolds
(2011), who in 1986 developed boids - an artificial
life program that simulates the flocking behavior
of the birds, describes individual creatures as
controlled by three simple rules:
CONCLUSION
Basic information about human cognition and
cognitive science, which relates to visual reasoning,
aesthetic emotions, and art, is considered essential
to developing cognitive computing theories, inves-
tigations on information-processing mechanisms
in computing, and working on constructing cogni-
tive computers that perceive, conclude, and learn.
Studying neural structures and functions of the brain
supports theories about cognitive thinking, memory,
and philosophical approaches to perceptual think-
ing. Cognitive informatics encompasses cognitive
science, neurophysiology, psychology, and informa-
tion sciences such as computer science, information
sciences, and intelligence science. It enables design-
ing engineering applications. Cognitive informatics
specialists search for the potential applications of
information processing and natural intelligence to
cognitive computing. This chapter links cognitive
processes with processes involved in visual thought
and visual problem solving, which will be applied
in interactive projects offered in this topic.
•
Separation:
Steer to avoid crowding
neighbors (short range repulsion).
•
Alignment:
Steer towards the average
heading of local flock mates.
•
Cohesion:
Steer to move toward the av-
erage position of neighbors (long range
attraction).
