The software package is available from Planet Source Code:
http://www.planetsourcecode.com/vb/scripts/ShowCode.asp?txtCodeId=74628&lngWId=1
Or download with this link, which includes compiled exe for Windows:
Or download with this link, which includes compiled exe for Windows:
THEORY
OF OPERATION – HOW IT WORKS
The
Intelligence Design Lab-5 is a cognitive model with behavior that is
guided by a navigational network system that maps out an internal
representation of its external environment (an internal world model)
using a 2D array where signal flow (magnitude and direction) vectors
point out the shortest path to where they want to go. This is a vital
part of our visual imagination. During human development it is common
and expected to cause children to stretch out their arms and say “I
can fly!” as they run around while visualizing themselves
navigating the sky.
Physical
properties at each place in the external environment are mapped into
a network according to whether they are safely navigable, an
unnavigable boundary or border at a barrier, or place attracting it
(in this case where the food is).
An
attracting location in the network provides an always signaling
(action potential) signal that propagates outward in all directions
and around barrier locations that do not signal at all (the signal
stops there just as the critter would by bashing into a barrier). In
math these directional activity patterns are shown using a vector
map. The ID Lab provides this in the onscreen Navigation Network form
that can show the signal direction through each place in the network.
Its
confidence in motor actions (forward/reverse and left/right) depend
on the magnitude and direction it is actually traveling matching the
magnitude and direction of the signal flow at the corresponding place
it is currently at. Where there is more than one pathway the shortest
path dominates, will be the first to propagate to that point and be
favored. Where there are two or more paths of equal distance it may
become indecisive but will soon favor one path over the others.
To
test its place avoidance behavior a hidden moving shock zone slowly
rotates counterclockwise, while the critter chases food in a
clockwise direction heading straight towards the hazard. Although the
test is demanding the confidence system of this intelligence strives
for perfection, as does a human athlete. The relatively high
confidence levels shown in the included line chart indicates that the
virtual critter is having fun. In the research paper “Dynamic
Grouping of Hippocampal Neural Activity During Cognitive Control of
Two Spatial Frames” (see notes) that the arena and some of the
navigational network is based upon it was found that; some live rats
preferred to chase after the treats even though they are not hungry
enough to need to eat, while others preferred to remain in the shock
free center zone. Even a live animal has to first be willing to
accept the challenge. For the virtual critter several If-Then
statements that compare actual travel magnitude and direction to that
of the internal representation is enough to make it want nothing else
but to chase the food around its arena.
Intentionally
getting out of the way of the approaching invisible shock zone
requires the ability to (from past experience) predict future
environmental events. This was added by alternating between current
angular time (by default room angle is from 0 to 15) and the next
angular time frame ahead. The places that will soon become a shock
hazard periodically become a place to avoid. This sequential on and
off signaling causes a (over time) temporal decision to be made. The
same works for swarming bees. Scouts that find a possible new place
to build a hive are one at a time allowed to dance out the location
for other bees to inspect. This way each option is first considered,
before making a final decision. Otherwise all the bees would either
swarm to the first site found or to different ones (instead of
staying together).
The
virtual critter cannot (like a swarm of bees) divide itself then go
separate ways, therefore appropriate actions are taken simply by
repeatedly presenting (in any sequence) what must be considered.
Exactly
what it will choose to do at any given time is as hard to predict as
it is in real animals. The only way to know for sure is read their
mind, which (by adding RAM monitoring code) is possible to do to the
ID Lab critter. But it's still not at all like the easy predictable
behavior of zombie-like “programmed” actions from an algorithm
that uses math to make it go in a given direction in response to an
approaching hazard instead of simply showing the options to consider
then leaving the decision up to it to figure out, on its own.
After
avoiding being surrounded by the approaching zone it must have the
common sense to go around to behind then wait for the food to be in
the clear, while knowing where the food is located even when it's
surrounded by places to avoid that can (where signal timing is way
off) block its signal activity. Where the signals from attract and
avoid locations combine: the wanting to go both towards and away from
the food results in it becoming nervously anxious, skittish, as are
real animals with such a dilemma.
The
signal timing that was found to work best closely follows Hebbian
Theory. Neighboring cells that fire together, wire together a network
with activity patterns that recreate the physical properties of what
is in the external environment. It can also be conceptualized as a
conservation of energy strategy where at each place in the network an
incoming charge is transferred to uncharged neighbors on the opposite
side, outgoing direction. The signal energy is moved from place to
place, not destroyed then regenerated all over again.
To
establish a benchmark that assumes error free signals from parts of
the brain that use dead reckoning to convert what is seen through the
eyes into spatial coordinates in its external environment the program
simply uses the already calculated X,Y positions that are used to
place things in the virtual environment. In the real world our brain
oppositely converts visual signals to these spatial X,Y locations,
which a virtual environment has to instead start with. Where this
dead reckoning system were added to this model and working perfectly
that's what you would get for coordinates. Using the exact
coordinates that the program already has provides ideal numbers to
work from, which in turn gives this critter an excellent sense of
where visible things are located around itself even though in this
Lab its eyes cannot visually see them.
This
navigation system demonstrates how simple it is to organize a network
that provides navigational intuition like we have. It helps explain
why animals (insects are also animals) seem born with a navigational
ability that is there from the start. The origin of this behavior in
living animals does not have to be a learned instinct that slowly
developed over many millions of years of time by blundering animals
passing on slightly less blundering behavioral traits to offspring.
It's possible for these neural navigational networks to have existed
when multicellular animals first developed, which set off the
Cambrian Explosion. The origin of these inherent navigational
behaviors may best explained by the activity patterns in these
relatively simple cellular networks.
The
origin of our brain may in part be from subcellular networks that
work much the same way in unicellular protozoans (single celled
animals) such as paramecia, which have eye spots, antennae and other
features once thought to only exist in multicellular animals. Testing
such a hypothesis using this computer model requires additional
theory, which may have a controversial title but going further into
biology this way meets all of the requirements of the premise for an
already proposed theory. In a case like this regardless of being
controversial science requires developing already existing theory.
Therefore see the TheoryOfID.pdf in Notes folder, for a testable
operational definition for "intelligent cause" where each
of the three emergent levels can be individually modeled. It is
predicted to this way be possible to demonstrate a never before
programmed intelligent causation event, which is still a further
research goal and challenge for all to enjoy.
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