Brooks's ideas gelled in a cockroachlike contraption the size of a football
called "Genghis." Brooks had pushed his downsizing to an extreme.
Genghis had six legs but
no "brain" at all.
All of its 12 motors and 21 sensors were distributed in a decomposable
network without a centralized controller.
The interaction of these 12 muscles and 21 sensors yielded an amazingly
complex and lifelike behavior.
Each of Genghis's six tiny legs
worked on its own, independent
of the others.
Each leg had its own ganglion of
neural cells-a tiny microprocessor-
that controlled the leg's actions.
Walking for Genghis then became a group project with at least six small
minds at work. Other small semiminds within its body coordinated
communication between the legs.
Entomologists say this is how ants and real cockroaches cope-they have
neurons in their legs that do the leg's thinking.
In the mobot Genghis, walking emerges out of the collective behavior of
the 12 motors.
Two motors at each leg lift, or
not, depending on what the other
legs around them are doing.
If they activate in the right
sequence-Okay, hup! One, three,
six, two, five, four!---
---walking "happens."
In one of his papers, Rod Brooks first laid out his instructions on how
to make a creature walk without knowing how:
There is no central controller which directs the body where to put each
foot or how high to lift a leg should there be an obstacle ahead.
Each leg is granted a fewFor instance, two basic behaviors can be thought of as
simple behaviors and each
independently knows what to
do under various circumstances.
"If I'm a leg and I'm up,These processes exist independently, run at all times, and fire whenever the sensory preconditions are true.
put myself down, " or
"If I'm a leg and I'm forward,
put the other five legs back
a little."
To create walking then, there just needs to be a sequencing of lifting legs (this is the only instance where any central control is evident).
As soon as a leg is raised it
automatically swings itself
forward, and also down.
But the act of swinging forward
triggers all the other legs
to move back a little.
Since those legs (happen) to be
touching the ground, the
body moves forward.
Once the beast can walk on a flat smooth floor without tripping, other behaviors can be added to improve the walk. For Genghis to get up and over a mound of phone books on the floor, it needs a pair of sensing whiskers to send information from the floor to the first set of legs. A signal from a whisker can suppress a motor's action. The rule might be, "If you feel something, I'll stop; if you don't, I'll keep going."
While Genghis learns to climb over an obstacle, the foundational walking routine is never fiddled with. This is a universal biological principle that Brooks helped illuminate-a law of god: When something works, don't mess with it; build on top of it. In natural systems, improvements are "pasted" over an existing debugged system. The original layer continues to operate without even being (or needing to be) aware that it has another layer above it.
When friends give you directions on how to get to their house, they don't tell you to "avoid hitting other cars" even though you must absolutely follow this instruction. They don't need to communicate the goals of lower operating levels because that work is done smoothly by a well-practiced steering skill. Instead, the directions to their house all pertain to high-level activities like navigating through a town.
Animals learn (in evolutionary time) in a similar manner. As do Brooks's mobots. His machines learn to move through a complicated world by building up a hierarchy of behaviors, somewhat in this order:
- Avoid contact with objects
- Wander aimlessly
- Explore the world
- Build an internal map
- Notice changes in the environment
- Formulate travel plans
- Anticipate and modify plans accordingly
The Wander-Aimlessly Department doesn't give a hoot about obstacles, since the Avoidance Department takes such good care of that.
http://www.kk.org/outofcontrol/ch3-b.html
Bees Do It - Distributed Governance
"The hive chooses," is the disarming answer of William Morton Wheeler, a natural philosopher and entomologist of the old school, who founded the field of social insects. Writing in a bombshell of an essay in 1911 ("The Ant Colony as an Organism" in the Journal of Morphology), Wheeler claimed that an insect colony was not merely the analog of an organism, it is indeed an organism, in every important and scientific sense of the word. He wrote: "Like a cell or the person, it behaves as a unitary whole, maintaining its identity in space, resisting dissolution...neither a thing nor a concept, but a continual flux or process."
It was a mob of 20,000 united into oneness.
http://www.kk.org/outofcontrol/ch2-a.html
- Collective Intelligence of Mob Plays Pong & Flys Plane by Simple Rules & Motion Waves
In a darkened Las Vegas conference room, a cheering audience waves cardboard wands in the air. Each wand is red on one side, green on the other. Far in back of the huge auditorium, a camera scans the frantic attendees. The video camera links the color spots of the wands to a nest of computers set up by graphics wizard Loren Carpenter. Carpenter's custom software locates each red and each green wand in the auditorium. Tonight there are just shy of 5,000 wandwavers. The computer displays the precise location of each wand (and its color) onto an immense, detailed video map of the auditorium hung on the front stage, which all can see. More importantly, the computer counts the total red or green wands and uses that value to control software. As the audience wave the wands, the display screen shows a sea of lights dancing crazily in the dark, like a candlelight parade gone punk. The viewers see themselves on the map; they are either a red or green pixel. By flipping their own wands, they can change the color of their projected pixels instantly.
Loren Carpenter boots up the ancient video game of Pong onto the immense screen. Pong was the first commercial video game to reach pop consciousness. It's a minimalist arrangement: a white dot bounces inside a square; two movable rectangles on each side act as virtual paddles. In short, electronic ping-pong. In this version, displaying the red side of your wand moves the paddle up. Green moves it down. More precisely, the Pong paddle moves as the average number of red wands in the auditorium increases or decreases. Your wand is just one vote.
Carpenter doesn't need to explain very much. Every attendee at this 1991 conference of computer graphic experts was probably once hooked on Pong. His amplified voice booms in the hall, "Okay guys. Folks on the left side of the auditorium control the left paddle. Folks on the right side control the right paddle. If you think you are on the left, then you really are. Okay? Go!"
The audience roars in delight. Without a moment's hesitation, 5,000 people are playing a reasonably good game of Pong. Each move of the paddle is the average of several thousand players' intentions. The sensation is unnerving. The paddle usually does what you intend, but not always. When it doesn't, you find yourself spending as much attention trying to anticipate the paddle as the incoming ball. One is definitely aware of another intelligence online: it's this hollering mob.
The group mind plays Pong so well that Carpenter decides to up the ante. Without warning the ball bounces faster. The participants squeal in unison. In a second or two, the mob has adjusted to the quicker pace and is playing better than before. Carpenter speeds up the game further; the mob learns instantly.
"Let's try something else," Carpenter suggests. A map of seats in the auditorium appears on the screen. He draws a wide circle in white around the center. "Can you make a green '5' in the circle?" he asks the audience. The audience stares at the rows of red pixels. The game is similar to that of holding a placard up in a stadium to make a picture, but now there are no preset orders, just a virtual mirror. Almost immediately wiggles of green pixels appear and grow haphazardly, as those who think their seat is in the path of the "5" flip their wands to green. A vague figure is materializing. The audience collectively begins to discern a "5" in the noise. Once discerned, the "5" quickly precipitates out into stark clarity. The wand-wavers on the fuzzy edge of the figure decide what side they "should" be on, and the emerging "5" sharpens up. The number assembles itself.
"Now make a four!" the voice booms. Within moments a "4" emerges. "Three." And in a blink a "3" appears. Then in rapid succession, "Two... One...Zero." The emergent thing is on a roll.
Loren Carpenter launches an airplane flight simulator on the screen. His instructions are terse: "You guys on the left are controlling roll; you on the right, pitch. If you point the plane at anything interesting, I'll fire a rocket at it." The plane is airborne. The pilot is...5,000 novices. For once the auditorium is completely silent. Everyone studies the navigation instruments as the scene outside the windshield sinks in. The plane is headed for a landing in a pink valley among pink hills. The runway looks very tiny.
There is something both delicious and ludicrous about the notion of having the passengers of a plane collectively fly it. The brute democratic sense of it all is very appealing. As a passenger you get to vote for everything; not only where the group is headed, but when to trim the flaps.
But group mind seems to be a liability in the decisive moments of touchdown, where there is no room for averages. As the 5,000 conference participants begin to take down their plane for landing, the hush in the hall is ended by abrupt shouts and urgent commands. The auditorium becomes a gigantic cockpit in crisis. "Green, green, green!" one faction shouts. "More red!" a moment later from the crowd. "Red, red! REEEEED!" The plane is pitching to the left in a sickening way. It is obvious that it will miss the landing strip and arrive wing first. Unlike Pong, the flight simulator entails long delays in feedback from lever to effect, from the moment you tap the aileron to the moment it banks. The latent signals confuse the group mind. It is caught in oscillations of overcompensation. The plane is lurching wildly. Yet the mob somehow aborts the landing and pulls the plane up sensibly. They turn the plane around to try again.
How did they turn around? Nobody decided whether to turn left or right, or even to turn at all. Nobody was in charge. But as if of one mind, the plane banks and turns wide. It tries landing again. Again it approaches cockeyed. The mob decides in unison, without lateral communication, like a flock of birds taking off, to pull up once more. On the way up the plane rolls a bit. And then rolls a bit more. At some magical moment, the same strong thought simultaneously infects five thousand minds: "I wonder if we can do a 360?"
Without speaking a word, the collective keeps tilting the plane. There's no undoing it. As the horizon spins dizzily, 5,000 amateur pilots roll a jet on their first solo flight. It was actually quite graceful. They give themselves a standing ovation.
The conferees did what birds do: they flocked. But they flocked self- consciously. They responded to an overview of themselves as they co-formed a "5" or steered the jet. A bird on the fly, however, has no overarching concept of the shape of its flock. "Flockness" emerges from creatures completely oblivious of their collective shape, size, or alignment. A flocking bird is blind to the grace and cohesiveness of a flock in flight.
https://kk.org/mt-files/outofcontrol/ch2-b.html
https://www.amazon.com/Out-Control-Biology-Machines-Economic/dp/0201483408
Determining interaction rules in animal swarms
The collective motion of living organisms, as manifested by flocking birds, schooling fish, or swarming insects, presents a captivating phenomenon believed to emerge mainly from local interactions between individual group members. In part, the study of swarming and flocking aims to understand how animals use visual, audial, and other cues to orient themselves with respect to the swarm of which they are part and how the properties of the swarm as a whole depend on the behaviors of the individual animals...
... Because flocks cannot be understood by studying individuals in isolation and are difficult to conduct controlled experiments on, understanding the behavioral patterns underlying flocking and swarming is especially challenging. Consequently, collective behavior has been extensively modeled particularly using the agent-based modeling framework, where simple mechanistic behavioral rules are used to generate qualitatively realistic swarming behavior.
The rules usually comprise three kinds of influences: (1) - A short-range zone where the individual tries to avoid collisions with obstacles or other animals, (2) - a tendency to adjust the velocity to fit nearby individuals' velocities, and (3) - a desire for avoiding being alone, for example, by moving toward the average position of the nearby individuals...
The Wisdom of Crowds & Collectives
https://immortalista.blogspot.com/p/the-wisdom-of-crowds.html
